Aerocar motion planning method and mode switching control system

Abstract

The invention provides an aerocar motion planning method. The aerocar motion planning method comprises the following steps: S1, constructing a three-dimensional grid urban environment simulation model and an aerocar simulation model; constructing a three-dimensional coordinate system according to the three-dimensional grids, and defining coordinates of a starting point and a terminal point; s2, taking the starting point as a current node, and starting to search an expansion node; s3, judging whether the searched expansion node exists in an open list (openList) or not; s4, calculating the total estimation cost of the expansion nodes in the openList, and determining the expansion node with the minimum total estimation cost as a child node; s5, taking the determined child node as the current node, searching the expansion node of the current node, repeating the steps S3-S4, and stopping searching until the searched new expansion node is the terminal point; and a path formed from the starting point to the end point through the node stored in the closed list is a planned path. By means of the method, safety guarantee can be provided when ground and low-altitude mode switching is involved, and it is guaranteed that the flying car smoothly enters the flying state and ends the flying state.

Description

Technical Field

[0001] The present invention relates to the field of flight vehicle path planning and control, and particularly to a motion planning method and a mode switching control system for a flight vehicle. Background Art

[0002] Due to the increasingly serious urban traffic congestion problem, as a new type of transportation vehicle for urban air traffic and future travel, the flight vehicle has greatly promoted the cross-border leakage and integrated development between the automotive and aviation fields. A flight vehicle is an intelligent transportation vehicle with two motion modes, capable of realizing a land driving mode and a low-altitude flight mode. Compared with the traditional ground traffic system, the low-altitude intelligent traffic system faces a three-dimensional space, with higher complexity and uncertainty. Therefore, in-depth research is needed in motion planning and control to improve the safety and comfort of the automatic driving of the flight vehicle.

[0003] Currently, the mainstream motion planning and control algorithms include control algorithms such as PID, LQR, and MPC, and path planning algorithms such as A*, Hybrid A*, EMPlanner, RRT*, and PRM. The research on the path planning of mobile vehicles with a single motion mode is relatively mature, such as UGVs and UAVs. Their goal is to find the collision-free optimal path from the starting point to the ending point with reference to certain criteria. These criteria include path length, path smoothness, energy consumption, and calculation time, etc. However, the above research is limited to a certain specific motion mode, such as ground, air, and water surface. Therefore, the research on these mobile vehicles is also single. At the same time, it is also unable to solve the three-dimensional path planning with multiple road types and multiple target conditions in the urban environment.

[0004] Therefore, to solve the above technical problems, it is urgent to propose a new technical means. Summary of the Invention

[0005] In view of this, a motion planning method and a mode switching control system for a flight vehicle provided by the present invention involve ground motion control and low-altitude flight control, especially ground and low-altitude mode switching control technology, which is a technology to ensure that the flight vehicle smoothly enters and ends the flight state, and is even more a guarantee of safety.

[0006] A motion planning method and a mode switching control system for a flight vehicle provided by the present invention include the following steps:

[0007] S1. Construct a three-dimensional grid urban environment simulation model and a flight vehicle simulation model; construct a three-dimensional coordinate system according to the three-dimensional grid, and define the coordinates of the starting point and the ending point;

[0008] S2. Take the starting point as the current node and start searching for and expanding nodes;

[0009] S3. Determine whether the searched expansion node exists in the open list openList. If so, directly proceed to step S4. If not, store the searched expansion node in the open list openList, and then proceed to step S4;

[0010] S4. Calculate the total estimated cost of the expansion nodes of the current node in the open list openList, determine the expansion node with the minimum total estimated cost as the child node, store the determined child node in the closed list closeList, and update the actual cost from the starting point to the child node, the estimated cost from the child node to the end point, and the parent node pointer;

[0011] S5. Take the determined child node as the current node, search for the expansion nodes of the current node, and repeat steps S3 - S4 until the newly searched expansion node is the end point, then stop the search; The path formed from the starting point through the nodes stored in the closed list closeList to the end point is the planned path.

[0012] Furthermore, in step S1, the three - dimensional grid urban environment simulation model includes an obstacle model and a road model. The obstacle model is a vehicle during traffic congestion, and the road model includes a hard pavement, a soil pavement, grassland, and sand;

[0013] The unit length of the grid is the maximum length in the flight mode of the flying car.

[0014] Furthermore, in step S2, when the current node is a ground node, search for 9 neighborhood nodes, and the 9 neighborhood nodes include 8 ground nodes and 1 air node;

[0015] When the current node is an air node, search for 10 neighborhood nodes, and the 10 neighborhood nodes include 8 air nodes, 1 ground node, and 1 high - altitude node.

[0016] Furthermore, in step S4, calculate the total estimated cost F(n) through the following steps:

[0017] S41. Calculate the motion energy consumption cost and motion time of the flying car in different states. The calculation formulas are as follows:

[0018] When the flying car is moving on the ground, the motion energy consumption cost E1 of the flying car relative to the ground is calculated by the following formula:

[0019]

[0020] where E 1,n represents the motion energy consumption cost of the flying car moving from the n - th node to the (n + 1) - th node on the ground, μ represents the rolling friction coefficient of the wheel, m represents the mass of the flying car, g represents the acceleration due to gravity, D nrepresents the distance between the nth node and the (n + 1)th node, r slip represents the wheel slip ratio, ρ represents the air density, A f represents the frontal windward area of the flying car, C d represents the air drag coefficient represents the average speed of the flying car during ground movement;

[0021] When the flying car takes off, the motion energy consumption cost E2 during the take-off process of the flying car is calculated by the following formula:

[0022]

[0023] where, E 2,n represents the motion energy consumption cost of the flying car moving from the nth node on the ground to the (n + 1)th node in the air, π represents the pi, ρ represents the air density, m represents the mass of the flying car, g represents the acceleration due to gravity, b represents the number of rotors of the flying car, r represents the radius of the rotors of the flying car, D n represents the distance between the nth node and the (n + 1)th node, η represents the efficiency of the rotor motor represents the average speed during the take-off process of the flying car, A u represents the area of the top region of the flying car, C d represents the air drag coefficient;

[0024] When the flying car is flying at low altitude, the motion energy consumption cost E3 of the flying car during flight is calculated by the following formula:

[0025]

[0026] where, E 3,n represents the flight motion energy consumption cost of the flying car moving from the nth node to the (n + 1)th node in the air, π represents the pi, ρ represents the air density, m represents the mass of the flying car, g represents the acceleration due to gravity, b represents the number of rotors of the flying car, r represents the radius of the rotors of the flying car, D n represents the distance between the nth node and the (n + 1)th node, η represents the efficiency of the rotor motor represents the average speed during the flight of the flying car, A f represents the frontal windward area of the flying car, C d represents the air drag coefficient;

[0027] When the flying car lands, the motion energy consumption cost E4 during the landing process of the flying car is calculated by the following formula:

[0028]

[0029] where, E4,n represents the motion energy consumption cost of the flying car moving from the nth node in the air to the (n + 1)th node on the ground. π represents the pi, ρ represents the air density, m represents the mass of the flying car, g represents the acceleration due to gravity, b represents the number of rotors of the flying car, r represents the radius of the rotors of the flying car, D n represents the distance between the nth node and the (n + 1)th node, η represents the efficiency of the rotor motor, represents the average speed during the landing process of the flying car, A u represents the area of the top region of the flying car, C d represents the air resistance coefficient;

[0030] S42. Calculate the surface energy consumption cost E5 of the flying car when moving on the ground. The calculation formula is as follows:

[0031]

[0032] where E 5,n represents the surface energy consumption cost between the nth node and the (n + 1)th node on the ground, x n , y n and z n represent the coordinates of the current node n on the x-axis, y-axis, and z-axis, x n+1 , y n+1 and z n+1 represent the coordinates of the extended point n + 1 on the x-axis, y-axis, and z-axis, and k represents the surface passage coefficient;

[0033] S43. Calculate the loss cost E6 during the mode switching of the flying car. The calculation formula is as follows:

[0034]

[0035] where P represents the output power of the rotor motor under rated working conditions, η represents the efficiency of the rotor motor, and T5 represents the time of mode switching;

[0036] S44. Unify the dimensions of the motion energy consumption cost, surface energy consumption cost, and loss cost during mode switching of the flying car. The calculation formula is as follows:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] Among them, e 1,n represents E after unifying the dimension 1,n , E 1,n represents the motion energy consumption cost of the flying car moving from the nth node to the (n + 1)th node on the ground, and e 2,n represents E after unifying the dimension 2,n , E 2,n represents the motion energy consumption cost of the flying car moving from the nth node on the ground to the (n + 1)th node in the air, and e 3,n represents E after unifying the dimension 3,n , E 3,n represents the flight motion energy consumption cost of the flying car moving from the nth node to the (n + 1)th node in the air, and e 4,n represents E after unifying the dimension 4,n , E 4,n represents the motion energy consumption cost of the flying car moving from the nth node in the air to the (n + 1)th node on the ground, and e 5,n represents E after unifying the dimension 5,n , E 5,n represents the surface energy consumption cost between the nth node and the (n + 1)th node. e6 represents E6 after unifying the dimension, and E6 represents the loss cost during the mode switching of the flying car;

[0044] S45. Calculate the total estimated cost of the current node n according to the motion energy consumption cost, surface energy consumption cost and loss cost during mode switching of the flying car after unifying the dimension. The calculation formula is as follows:

[0045] F(n) = G(n) + H(n)

[0046]

[0047]

[0048] Among them, G(n) represents the actual cost from the current node n to the expanded node n + 1, G(n - 1) represents the actual cost from the parent node n - 1 to the current node n, H(n) represents the estimated cost from the expanded node n + 1 to the end point N, F(n) represents the total estimated cost of the expanded node n + 1, ω1 represents the weight of the energy consumption cost, T(n, n + 1) represents the time for the flying car to move from the current node n to the expanded node n + 1, ω2 represents the weight of the time interval, T(n + 1, N) represents the time for the flying car to move from the expanded node n + 1 to the end point N, e1 represents the motion energy consumption cost when the flying car moves on the ground, e2 represents the take-off energy consumption cost during the take-off process of the flying car after unifying the dimension, e3 represents the flight energy consumption cost when the flying car flies in the air after unifying the dimension, e4 represents the landing energy consumption cost during the landing process of the flying car after unifying the dimension, e5 represents the surface energy consumption cost when the flying car moves on the ground after unifying the dimension, e6 represents the loss cost during the mode switching of the flying car, Z n represents the height of the current node n, Z n+1 represents the height of the expanded node n + 1, Z N represents the height of the end point N.

[0049] Correspondingly, the present invention further provides a flying car mode switching control system, which includes an upper computer module, a communication module, a lower computer module, a drive system and a remote control system;

[0050] The upper computer module includes an environment modeling module and a path planning module, and the path planning module is used to plan a path according to the above method;

[0051] The communication module is used to transmit the path planned by the upper computer to the lower computer and store it, and to transmit the information collected by the lower computer to the upper computer and store it;

[0052] The lower computer module includes a mode switching state machine, a bottom layer controller, sensors and an emergency planning module. The mode switching state machine is used to preset the mode switching state according to the path planned by the upper computer and independently switch between relative ground movement and relative space movement; the bottom layer controller is used to control the state of the flying car and transmit the collected information to the path planning module through the communication module; the sensors are used to detect the state of the flying car and the external environment; the emergency planning module is used to plan a path or find a landing point in case of emergency;

[0053] The drive system includes a ground control system and an air control system; the ground control system includes a vehicle chassis controller, hub motors, and a steering motor; the vehicle chassis controller is used to control the hub motors and the steering motor according to the control signals output by the underlying controller to achieve the driving, braking, and steering of the flying car, and transmit the state information of the flying car on the ground to the underlying controller;

[0054] The air control system includes a flight rotor controller, an electronic speed controller module, and rotor motors; the flight rotor controller is used to control the rotor motors according to the control signals output by the underlying controller to achieve hovering, orbiting, low-altitude acceleration, and low-altitude deceleration, and transmit the state information of the flying car in the air to the underlying controller;

[0055] The remote control system is connected to the path planning module through a wireless communication module, and is used to store the information transmitted by the path planning module and monitor the state of the flying car. The remote control system is also used to manually control the flying car by sending control signals to the upper computer.

[0056] Furthermore, emergency situations include: bad weather, the flying car flying above the maximum height, no feasible path points, and insufficient battery power of the flying car;

[0057] When the flying car encounters bad weather during driving, the flying car enters an emergency state. At this time, the remote control system can be used for manual operation to control the automatic driving system of the flying car, or the remote control system can send a command to land nearby to the emergency planning module through the upper computer. The emergency planning module sends the nearest landing point found to the underlying control module, and the underlying control module issues a control command to control the flying car to land nearby; it is also possible to exit the automatic driving system and the driver operates to land at the landing point searched by the emergency planning module;

[0058] When the flying car is flying in the air, the mode switching state machine will perform position detection. If the flying height of the flying car exceeds the maximum height set by the system, the flying car enters an emergency state. At this time, the remote control system adjusts the height of the flying car to within the safe driving height range through manual operation, or exits the automatic driving system and the driver operates to adjust the height of the flying car to within the safe driving height range;

[0059] When the upper computer encounters an obstacle in path planning, the emergency planning module plans the path according to the above method. When the emergency planning module does not search for valid path nodes, the flying car enters an emergency state. At this time, the remote control system sends a command to re-plan to the emergency planning module, and the emergency planning module roughly plans the route based on the information detected by the sensor, and the flying car is controlled manually through the remote control system;

[0060] When the system prompts that the battery power of the flying car is insufficient during flight, the flying car enters an emergency state. At this time, the remote control system adds the nearest landing point to the path planned manually on the host computer, enabling the flying car to vertically land at the nearest landing point for charging, and replanning the path on the host computer based on the current landing point and the destination.

[0061] Furthermore, the mode switching state machine sets 15 states, 6 state transition conditions, and 8 output commands according to the 4 modes of the flying car;

[0062] The 4 modes include: ground driving, aerial flight, vertical takeoff, and vertical landing;

[0063] The 15 states include: Start Begin = 0, Ground movement preparation Ground_pre = 1, During ground movement Ground_pre = 2, Ground movement completed Ground_finsh = 3, Vertical takeoff preparation Takeoff_pre = 4, During vertical takeoff Takeoff_ing = 5, Vertical takeoff completed Takeoff_finsh = 6, Low-altitude flight preparation Fly_pre = 7, During low-altitude flight Fly_ing = 8, Low-altitude flight completed Fly_finsh = 9, Vertical landing preparation Land_pre = 10, During vertical landing Land_ing = 11, Vertical landing completed Land_finsh = 12, Emergency state State of emergency = 13, and End Finsh = 14;

[0064] The 6 state transition conditions include: the height of the path node is equal to 0, the height of the next path node is not equal to 0, no feasible path node, reaching the destination, the flying height of the flying car exceeding the maximum height, and insufficient battery power;

[0065] The 8 output commands include: locking the rotor motor, unlocking the rotor motor, locking the hub motor, unlocking the hub motor, unlocking the steering motor, locking the steering motor, position detection, and confirming landing.

[0066] Furthermore, the mode switching state machine performs autonomous switching according to the path planned by the host computer through the following method;

[0067] When the starting point of the flying car is on the ground, the flying car enters the start state Begin = 0. It judges whether the next path node is on the ground according to the planned path. If so, the height of the next path node is 0, and the flying car enters the ground movement preparation state Ground_pre = 1. At this time, the hub motors and steering motors are unlocked. After the hub motors and steering motors are unlocked, it enters the ground movement process Ground_pre = 2. When the flying car moves to the specified path node, the flying car enters the ground movement completion state Ground_finsh = 3; if not, the height of the next path node is not equal to 0. At this time, the hub motors and steering motors are locked. After the hub motors and steering motors are locked, it enters the vertical takeoff preparation state Takeoff_pre = 4. At the same time, the rotor motors are unlocked. After the rotor motors are unlocked, it enters the vertical takeoff process Takeoff_ing = 5. At this time, the mode switching state machine issues a position detection instruction to judge whether the flying height of the flying car exceeds the maximum height. If so, the flying car enters the emergency state State of emergency = 13. If not, it continues vertical takeoff. When the flying car moves to the specified path node, the flying car enters the vertical takeoff completion state Takeoff_finsh = 6; wait for the next path node until the next path node is the end point, and the movement of the flying car ends Finsh = 14;

[0068] When the starting point of the flying car is in the air, the flying car enters the start state Begin = 0. It judges whether the next path node is in the air according to the planned path. If so, the height of the next path node is not equal to 0, and the flying car enters the low-altitude flight preparation state Fly_pre = 7. At the same time, the rotor motors are unlocked. After the rotor motors are unlocked, it enters the low-altitude flight process Fly_ing = 8. The mode switching state machine issues a position detection instruction to judge whether the flying height of the flying car exceeds the maximum height. If it exceeds the maximum height, the flying car enters the emergency state State of emergency = 13. If it does not exceed the maximum height, it continues low-altitude flight. When the flying car moves to the specified path node, it enters the low-altitude flight completion state Fly_finsh = 9; if not, the height of the next path node is equal to 0, and the flying car enters the vertical landing preparation state Land_pre = 10. When the remote interaction module confirms safety and then confirms the landing, the flying car enters the vertical landing process Land_ing = 11. When the flying car moves to the specified path node, the flying car enters the vertical landing completion state Land_finsh = 12. At this time, the rotor motors are locked. Wait for the next path node until the next path node is the end point, and the movement of the flying car ends Finsh = 14;

[0069] When the flight vehicle shows insufficient power in the air flight system, the flight vehicle enters the emergency state State of emergency = 13. The emergency planning module searches for and determines the nearest landing point, and the flight vehicle enters the vertical landing preparation state Land_pre = 10. After the remote interaction module confirms safety, it confirms the landing, and the flight vehicle enters the vertical landing process Land_ing = 11. When the flight vehicle moves to the landing point, the flight vehicle enters the vertical landing completion state Land_finsh = 12. At this time, the rotor motor is locked and the flight vehicle is charged.

[0070] Advantages of the present invention: The present invention performs path planning for the flight vehicle through an improved A* algorithm, and adds the motion energy consumption, motion time, energy consumption through different terrains, and mode switching loss of the flight vehicle in different modes to the cost function, fully considering the situation of multiple road types and multi-objective conditions in the urban environment, so as to plan a smooth path that takes into account both energy consumption and mobility, and gives play to the unique advantages of the two motion modes; the mode switching control system can realize the autonomous switching between the land and air modes; at the same time, the ground control and air control are integrated together, and the continuous motion control of the flying vehicle mode switching is reliably realized. Brief Description of the Drawings

[0071] The present invention will be further described below in conjunction with the drawings and embodiments:

[0072] Figure 1 It is a flowchart of the present invention;

[0073] Figure 2 It is a block diagram of the mode switching control system of the present invention. Detailed Embodiment

[0074] The present invention will be further described below in conjunction with the accompanying drawings of the specification:

[0075] A flight vehicle motion planning method provided by the present invention includes the following steps:

[0076] S1. Construct a three-dimensional grid urban environment simulation model and a flight vehicle simulation model; construct a three-dimensional coordinate system according to the three-dimensional grid, and define the coordinates of the starting point and the ending point;

[0077] S2. Take the starting point as the current node and start searching for and expanding nodes;

[0078] S3. Determine whether the expanded node found exists in the open list openList. If so, directly enter step S4. If not, store the expanded node found in the open list openList, and then enter step S4;

[0079] S4. Calculate the total estimated cost of the expanded nodes of the current node in the open list openList, determine the expanded node with the minimum total estimated cost as the child node, store the determined child node in the closed list closeList, and update the actual cost from the starting point to the child node, the estimated cost from the child node to the end point, and the parent node pointer;

[0080] S5. Take the determined child node as the current node, search for the expanded nodes of the current node, and repeat steps S3 - S4 until the newly searched expanded node is the end point, then stop the search; The path formed from the starting point through the nodes stored in the closed list closeList to the end point is the planned path. The flight vehicle motion planning method adopted in the present invention is an improved A* algorithm. The improved A* algorithm fully considers the motion energy consumption, motion time in different modes, the energy consumption passing through different terrains, and the mode switching loss, as well as the situation of multiple road types and multiple target conditions in the urban environment, so as to be able to plan a smooth path that takes into account both energy consumption and mobility, and give full play to the unique advantages of the two motion modes.

[0081] In this embodiment, in step S1, a three - dimensional grid urban environment simulation model and a flight vehicle simulation model are constructed through Matlab; A three - dimensional coordinate system is constructed according to the three - dimensional grid, and the coordinates of the starting point and the end point are defined, and a three - dimensional space coordinate system is established according to the three - dimensional grid, and the three - dimensional coordinates of the starting point and the end point are defined; Matlab is a prior art and will not be elaborated here;

[0082] Among them, the three - dimensional grid urban environment simulation model includes an obstacle model and a road model. The obstacle model is a vehicle during traffic congestion, and the road model includes a hard pavement, a soil pavement, a grassland, and a sandy land;

[0083] The unit length of the grid is the maximum length in the flight mode of the flight vehicle.

[0084] In this embodiment, in step S2, the starting point is taken as the current node, and the search for expanded nodes starts;

[0085] When the current node is a ground node, search for 9 neighborhood nodes. Among the 9 neighborhood nodes, there are 8 ground nodes and 1 air node;

[0086] When the current node is an air node, search for 10 neighborhood nodes. The 10 neighborhood nodes include 8 air nodes, 1 ground node, and 1 high - altitude node. Among them, an air node represents a node lower than or at the same height as the current node, and the Z - axis coordinate of the air node is greater than 0; A high - altitude node represents a node with a larger Z - axis coordinate value than the current node; Searching for high - altitude nodes is used to restore the flight altitude after crossing obstacles.

[0087] In this embodiment, in step S3, when the current node is a ground node, there are a total of 9 expanded nodes searched. When the current node is an air node, there are a total of 10 expanded nodes searched. Determine whether the searched expanded nodes exist in the open list openList. If so, directly proceed to step S4. If not, store the searched expanded nodes in the open list openList, and then proceed to step S4.

[0088] In this embodiment, in step S4, calculate the total estimated cost of the expanded nodes of the current node in the open list openList. If the current node is a ground node, calculate the total estimated cost of 9 expanded nodes. If the current node is an air node, calculate the total estimated cost of 10 expanded nodes. Determine the expanded node with the minimum total estimated cost as the child node, store the determined child node in the closed list closeList, and update the actual cost from the start point to the child node, the estimated cost from the child node to the end point, and the parent node pointer; update the actual cost from the start point to the child node and the estimated cost from the child node to the end point for calculating the total estimated cost of the next expanded node, and update the parent node pointer to obtain the latest planned path; updating the actual cost from the start point to the child node, the estimated cost from the child node to the end point, and the parent node pointer is the prior art of the A* algorithm and is directly completed in the simulation software, which will not be elaborated here;

[0089] Calculate the total estimated cost F(n) through the following steps:

[0090] S41. Calculate the motion energy consumption cost and motion time of the flying car in different states. The calculation formulas are as follows:

[0091] When the flying car is moving on the ground, the motion energy consumption cost E1 of the flying car relative to the ground is calculated by the following formula:

[0092]

[0093] Among them, E 1,n represents the motion energy consumption cost of the flying car moving from the nth node to the (n + 1)th node on the ground. μ represents the rolling friction coefficient of the wheel, m represents the mass of the flying car, g represents the acceleration due to gravity, D n represents the distance between the nth node and the (n + 1)th node, r slip represents the wheel slip rate, ρ represents the air density, A f represents the frontal windward area of the flying car, C d represents the air resistance coefficient, represents the average speed of the flying car moving on the ground;

[0094] When the flying car takes off, the motion energy consumption cost E2 during the takeoff process of the flying car is calculated by the following formula:

[0095]

[0096] Among them, E 2,n represents the motion energy consumption cost for the flying car to move from the nth node on the ground to the (n + 1)th node in the air. π represents the pi, ρ represents the air density, m represents the mass of the flying car, g represents the acceleration due to gravity, b represents the number of rotors of the flying car, r represents the radius of the rotors of the flying car, D n represents the distance between the nth node and the (n + 1)th node, and η represents the efficiency of the rotor motor. represents the average speed during the takeoff process of the flying car, A u represents the area of the top region of the flying car, C d represents the air resistance coefficient;

[0097] When the flying car is flying at low altitude, low altitude refers to the space within 200 m from the ground to the air. Specifically in this application, it is scaled down accordingly according to the simulation ratio to obtain the low altitude height. The motion energy consumption cost E3 of the flying car flying in the air is calculated by the following formula:

[0098]

[0099] Among them, E 3,n represents the flight motion energy consumption cost for the flying car to move from the nth node to the (n + 1)th node in the air. π represents the pi, ρ represents the air density, m represents the mass of the flying car, g represents the acceleration due to gravity, b represents the number of rotors of the flying car, r represents the radius of the rotors of the flying car, D n represents the distance between the nth node and the (n + 1)th node, and η represents the efficiency of the rotor motor. represents the average speed during the flight process of the flying car, A f represents the frontal windward area of the flying car, C d represents the air resistance coefficient;

[0100] When the flying car lands, the motion energy consumption cost E4 during the landing process of the flying car is calculated by the following formula:

[0101]

[0102] Among them, E 4,n represents the motion energy consumption cost for the flying car to move from the nth node in the air to the (n + 1)th node on the ground. π represents the pi, ρ represents the air density, m represents the mass of the flying car, g represents the acceleration due to gravity, b represents the number of rotors of the flying car, r represents the radius of the rotors of the flying car, D nrepresents the distance between the nth node and the (n + 1)th node, and η represents the efficiency of the rotor motor. represents the average speed during the landing process of the flying car, A u represents the area of the top region of the flying car, C d represents the air resistance coefficient;

[0103] S42. Calculate the ground surface energy consumption cost E5 when the flying car is moving on the ground. The calculation formula is as follows:

[0104]

[0105] where, E 5,n represents the ground surface energy consumption cost between the nth node and the (n + 1)th node on the ground, x n , y n and z n represent the coordinates of the current node n on the x-axis, y-axis and z-axis, x n+1 , y n+1 and z n+1 represent the coordinates of the extended point n + 1 on the x-axis, y-axis and z-axis. k represents the ground surface passing coefficient. The passing coefficient for hard pavement is 1, for soil pavement is 0.8, for grassland is 0.4, and for sandy land is 0.2;

[0106] S43. Calculate the loss cost E6 during the mode switching of the flying car. The calculation formula is as follows:

[0107]

[0108] where, P represents the output power of the rotor motor under rated working conditions, η represents the efficiency of the rotor motor, and T5 represents the time of mode switching;

[0109] S44. Unify the dimensions of the motion energy consumption cost, ground surface energy consumption cost and loss cost during mode switching of the flying car. The calculation formula is as follows:

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] where, e 1,n represents E after unifying the dimensions1,n , E 1,n represents the motion energy consumption cost of the flying car moving from the nth node to the (n + 1)th node on the ground, and e 2,n represents E after unified dimension 2,n , E 2,n represents the motion energy consumption cost of the flying car moving from the nth node on the ground to the (n + 1)th node in the air, and e 3,n represents E after unified dimension 3,n , E 3,n represents the flight motion energy consumption cost of the flying car moving from the nth node to the (n + 1)th node in the air, and e 4,n represents E after unified dimension 4,n , E 4,n represents the motion energy consumption cost of the flying car moving from the nth node in the air to the (n + 1)th node on the ground, and e 5,n represents E after unified dimension 5,n , E 5,n represents the ground surface energy consumption cost between the nth node and the (n + 1)th node. e6 represents E6 after unified dimension, and E6 represents the loss cost during the mode switching of the flying car;

[0117] S45. Calculate the total estimated cost of the current node n according to the motion energy consumption cost, ground surface energy consumption cost of the flying car after unified dimension, and the loss cost during mode switching. The calculation formula is as follows:

[0118] F(n) = G(n) + H(n)

[0119]

[0120]

[0121] Among them, G(n) represents the actual cost from the current node n to the expanded node n+1, G(n-1) represents the actual cost from the parent node n-1 to the current node n, H(n) represents the estimated cost from the expanded node n+1 to the end point N, F(n) represents the total estimated cost of the expanded node n+1, ω1 represents the weight of the energy consumption cost, T(n,n+1) represents the time for the flying car to move from the current node n to the expanded node n+1, ω2 represents the weight of the time interval, T(n+1,N) represents the time for the flying car to move from the expanded node n+1 to the end point N, e1 represents the movement energy consumption cost when the flying car moves on the ground, e2 represents the take-off energy consumption cost during the take-off process of the flying car after unifying the dimension, e3 represents the flight energy consumption cost when the flying car flies in the air after unifying the dimension, e4 represents the landing energy consumption cost during the landing process of the flying car after unifying the dimension, e5 represents the surface energy consumption cost when the flying car moves on the ground after unifying the dimension, e6 represents the loss cost during the mode switching of the flying car after unifying the dimension, Z n represents the height of the current node n, Z n+1 represents the height of the expanded node n+1, Z N represents the height of the end point N;

[0122] The total estimated cost function F(n)=G(n)+H(n). When the height Z n =0 of the current node and the height Z n+1 =0 of the expanded node, it means that moving from the current node n to the expanded node n+1 is moving on the ground. If the end point is also on the ground, then the total estimated cost:

[0123] If the end point is in the air, then the total estimated cost And so on for other cases. Among them, the time T is obtained by dividing the movement distance by the average speed of the whole journey. The above method is not only time-saving but also energy-saving. Compared with the general H(n) function that only considers distance, the flying car will not switch modes to save time, and will only switch to the flight mode when necessary. Most of the time, it still moves mainly on the ground, ensuring a certain cruising range and safety.

[0124] In this embodiment, in step S5, the determined child node is used as the current node, the expanded node of the current node is searched, and steps S3-S4 are repeated until the newly searched expanded node is the end point, and the search is stopped; the path formed from the starting point through the nodes stored in the closed list closeList to the end point is the planned path.

[0125] Accordingly, the present invention further provides a flight vehicle mode switching control system. The flight vehicle mode switching control system is built using the state - flow module of Simulink and consists of a host computer module, a communication module, a slave computer module, a drive system, and a remote control system, as Figure 2 shown;

[0126] The host computer module includes an environment modeling module and a path planning module. The output end of the environment modeling module is connected to the input end of the path planning module. The output end of the path planning module is connected to the input end of the slave computer module through the communication module. The path planning module is used to plan a path according to the above - mentioned method;

[0127] The input end of the communication module is connected to the output end of the slave computer, and the output end of the communication module is connected to the input end of the path planning module. The communication module is used to transmit the path planned by the host computer to the slave computer and store it, and to transmit the information collected by the slave computer to the host computer and store it; The communication module uses existing technologies and will not be elaborated here;

[0128] The slave computer module includes a mode switching state machine, a low - level controller, sensors, and an emergency planning module. The control output end of the mode switching state machine is connected to the control input end of the low - level controller. The mode switching state machine is used to preset the mode switching state according to the path planned by the host computer and autonomously switch between relative ground movement and relative space movement; The control output end of the low - level controller is connected to the control input end of the drive system. The output end of the low - level controller is connected to the input end of the path planning module through the communication module. The low - level controller is used to control the state of the flight vehicle and transmit the collected information to the path planning module through the communication module. The output end of the low - level controller is also connected to the input end of the emergency planning module. The emergency planning module is used to plan a path or find a landing point in case of an emergency; The input end of the emergency planning module is connected to the output end of the sensors; The sensors are used to detect the state of the flight vehicle and the external environment;

[0129] The drive system includes a ground control system and an air control system; The ground control system includes a vehicle chassis controller, hub motors, and steering motors; The output end of the vehicle chassis controller is connected to the input ends of the hub motors and the steering motors. The output end of the vehicle chassis controller is also connected to the input end of the low - level controller; The vehicle chassis controller is used to control the hub motors and the steering motors according to the control signals output by the low - level controller to achieve the driving, braking, and steering of the flight vehicle, and transmit the state information of the flight vehicle on the ground to the low - level controller;

[0130] The air control system includes a flight rotor controller, an electronic speed controller module, and rotor motors. The control output end of the flight rotor controller is connected to the control input end of the electronic speed controller module. The control output end of the electronic speed controller module is connected to the control input end of the rotor motors. The output end of the flight rotor controller is also connected to the input end of the underlying controller. The flight rotor controller controls the rotor motors according to the control signals output by the underlying controller to achieve hovering, orbiting, low-altitude acceleration, and low-altitude deceleration, and transmits the state information of the flying car in the air to the underlying controller.

[0131] The remote control system is connected to the path planning module through a wireless communication module, and is used to store the information transmitted by the path planning module and monitor the state of the flying car. The remote control system also controls the underlying controller through a host computer, and then controls the hub motors, steering motors, and rotor motors through the underlying controller. The remote control system real-time monitors the position, speed, attitude, altitude, etc. of the flying car in the urban road network, and can achieve remote control in case of emergency, greatly improving the safety of the flying car's autonomous driving. The remote control system adopts existing technologies and will not be elaborated here.

[0132] The wireless communication module includes mobile networks, Wifi, wireless data transmission, etc., which are existing wireless communication technologies.

[0133] In this embodiment, the output end of the underlying controller is also connected to the emergency planning module, which is used to transmit the control information of the remote control system to the mode switching state machine through the host computer and the communication module in case of emergency, and then the mode switching state machine transmits it to the underlying controller, and the underlying controller controls the emergency planning module, so as to realize the control of the emergency planning module by the remote control system.

[0134] In this embodiment, the emergency situations include: bad weather, the flying height of the flying car exceeds the maximum height, there are no feasible path points, and the battery of the flying car is insufficient.

[0135] When the flying car encounters bad weather during driving, the flying car enters an emergency state. At this time, manual operation can be performed through the remote control system to control the autonomous driving system of the flying car, or the remote control system can transmit a signal to the host computer, and then the host computer transmits the signal to the mode switching state machine. The mode switching state machine transmits the signal to the underlying controller, and the underlying controller transmits the signal to the emergency planning module, so as to realize that the remote control system issues a command to land nearby to the emergency planning module through the host computer. The emergency planning module sends the nearest landing point found to the underlying control module, and the underlying control module issues a control command to control the flying car to land nearby; it can also exit the autonomous driving system and the driver operates to land at the landing point searched by the emergency planning module.

[0136] When the flying car is in flight, the mode switching state machine performs position detection. If the flying height of the flying car exceeds the maximum height set by the system, the flying car enters an emergency state. At this time, the remote control system adjusts the height of the flying car to within the safe driving height range through manual operation, or exits the autopilot system and the driver operates to adjust the height of the flying car to within the safe driving height range. Among them, the maximum heights set for different flying cars are different;

[0137] When the upper computer encounters an obstacle in path planning, such as road construction, landslide, debris flow, etc., which causes the planned path to be blocked, the emergency planning module plans the path according to the above method. When the emergency planning module fails to search for valid path nodes, the flying car enters an emergency state. At this time, the remote control system sends a replanning command to the emergency planning module, and the emergency planning module roughly plans the route based on the information detected by the sensors. At this time, planning the path according to the above method cannot obtain a valid path. Therefore, the route is roughly planned based on the information detected by the sensors to quickly get out of the emergency situation, and the flying car is controlled manually through the remote control system to drive according to the roughly planned route by the sensors;

[0138] The sensors include millimeter-wave radar, vision sensor, infrared sensor, IMU inertial measurement unit, GPS, ultrasonic ranging module, barometer, and navigation and positioning module, etc.;

[0139] When the system prompts that the battery power is insufficient during the flight of the flying car, the flying car enters an emergency state. At this time, the remote control system adds the nearest landing point to the path planned by the upper computer manually, so that the flying car vertically lands at the nearest landing point to charge, and replans the path in the upper computer according to the current landing point and the destination.

[0140] In this embodiment, the mode switching state machine sets 15 states, 6 state transition conditions, and 8 output commands according to the 4 modes of the flying car;

[0141] The 4 modes include: ground driving, air flight, vertical takeoff, and vertical landing;

[0142] The 15 states include: Start Begin = 0, Ground movement preparation Ground_pre = 1, During ground movement Ground_pre = 2, Ground movement completed Ground_finsh = 3, Vertical takeoff preparation Takeoff_pre = 4, During vertical takeoff Takeoff_ing = 5, Vertical takeoff completed Takeoff_finsh = 6, Low-altitude flight preparation Fly_pre = 7, During low-altitude flight Fly_ing = 8, Low-altitude flight completed Fly_finsh = 9, Vertical landing preparation Land_pre = 10, During vertical landing Land_ing = 11, Vertical landing completed Land_finsh = 12, Emergency state State of emergency = 13, and End Finsh = 14;

[0143] The 6 state transition conditions include: the height of the path node is equal to 0, the height of the next path node is not equal to 0, there is no feasible path node, reaching the end point, the flight altitude of the flying car exceeds the maximum altitude, and the power is insufficient;

[0144] The 8 output commands include: locking the rotor motor, unlocking the rotor motor, locking the hub motor, unlocking the hub motor, unlocking the steering motor, locking the steering motor, position detection, and confirming landing.

[0145] In this embodiment, the mode switching state machine performs autonomous switching according to the path planned by the host computer through the following method;

[0146] When the starting point of the flying car is on the ground, the flying car enters the start state Begin = 0. It judges whether the next path node is on the ground according to the planned path. If so, the height of the next path node is 0, and the flying car enters the ground movement preparation state Ground_pre = 1. At this time, the hub motors and steering motors are unlocked. After the hub motors and steering motors are unlocked, it enters the ground movement process Ground_pre = 2. When the flying car moves to the specified path node, the flying car enters the ground movement completion state Ground_finsh = 3; if not, the height of the next path node is not equal to 0. At this time, the hub motors and steering motors are locked. After the hub motors and steering motors are locked, it enters the vertical takeoff preparation state Takeoff_pre = 4. At the same time, the rotor motors are unlocked. After the rotor motors are unlocked, it enters the vertical takeoff process Takeoff_ing = 5. At this time, the mode switching state machine issues a position detection instruction to judge whether the flying height of the flying car exceeds the maximum height. If so, the flying car enters the emergency state State of emergency = 13. If not, it continues vertical takeoff. When the flying car moves to the specified path node, the flying car enters the vertical takeoff completion state Takeoff_finsh = 6; wait for the next path node until the next path node is the end point, and the movement of the flying car ends Finsh = 14;

[0147] When the starting point of the flying car is in the air, the flying car enters the start state Begin = 0. It judges whether the next path node is in the air according to the planned path. If so, the height of the next path node is not equal to 0, and the flying car enters the low-altitude flight preparation state Fly_pre = 7. At the same time, the rotor motors are unlocked. After the rotor motors are unlocked, it enters the low-altitude flight process Fly_ing = 8. The mode switching state machine issues a position detection instruction to judge whether the flying height of the flying car exceeds the maximum height. If it exceeds the maximum height, the flying car enters the emergency state State of emergency = 13. If it does not exceed the maximum height, it continues low-altitude flight. When the flying car moves to the specified path node, it enters the low-altitude flight completion state Fly_finsh = 9; if not, the height of the next path node is equal to 0, and the flying car enters the vertical landing preparation state Land_pre = 10. When the remote interaction module confirms safety and then confirms the landing, the flying car enters the vertical landing process Land_ing = 11. When the flying car moves to the specified path node, the flying car enters the vertical landing completion state Land_finsh = 12. At this time, the rotor motors are locked. Wait for the next path node until the next path node is the end point, and the movement of the flying car ends Finsh = 14;

[0148] When the flight vehicle shows insufficient power in the air flight system, the flight vehicle enters the emergency state State of emergency = 13. The emergency planning module searches for and determines the nearest landing point, and the flight vehicle enters the vertical landing preparation state Land_pre = 10. When the remote interaction module confirms safety and then confirms the landing, the flight vehicle enters the vertical landing process Land_ing = 11. When the flight vehicle moves to the landing point, the flight vehicle enters the vertical landing completion state Land_finsh = 12. At this time, the rotor motor is locked and the flight vehicle is charged.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the purpose and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A motion planning method for a flying car, characterized in that: Including the following steps: S1. Construct a three-dimensional grid urban environment simulation model and a flying car simulation model; construct a three-dimensional coordinate system based on the three-dimensional grid, and define the coordinates of the starting point and the ending point; S2. Take the starting point as the current node and start searching for expanding nodes; S3. Determine whether the searched expanding node exists in the open list openList. If so, directly go to step S4. If not, store the searched expanding node in the open list openList, and then go to step S4; S4. Calculate the total estimated cost of the expanding nodes of the current node in the open list openList, determine the expanding node with the minimum total estimated cost as the child node, store the determined child node in the closed list closeList, and update the actual cost from the starting point to the child node, the estimated cost from the child node to the ending point, and the parent node pointer; S5. Take the determined child node as the current node, search for the expanding nodes of the current node, and repeat steps S3 - S4 until the newly searched expanding node is the ending point and stop the search; the path formed from the starting point through the nodes stored in the closed list closeList to the ending point is the planned path.

2. The flight vehicle motion planning method according to claim 1, wherein: In step S1, the three-dimensional grid urban environment simulation model includes an obstacle model and a road model. The obstacle model is a vehicle during traffic congestion, and the road model includes a hard pavement, a soil pavement, grassland, and sand; The unit length of the grid is the maximum length in the flying mode of the flying car.

3. The flight vehicle motion planning method according to claim 1, wherein: In step S2, when the current node is a ground node, search for 9 neighborhood nodes, and the 9 neighborhood nodes include 8 ground nodes and 1 air node; When the current node is an air node, search for 10 neighborhood nodes, and the 10 neighborhood nodes include 8 air nodes, 1 ground node, and 1 high-altitude node.

4. The flight vehicle motion planning method according to claim 1, characterized in that: In step S4, calculate the total estimated cost F(n) through the following steps: S41. Calculate the motion energy consumption cost and motion time of the flying car in different states. The calculation formulas are as follows: When the flying car is moving on the ground, the energy consumption cost E1 of the flying car relative to the ground movement is calculated by the following formula: Among them, E 1,n represents the motion energy consumption cost of the flying car moving from the nth node to the (n + 1)th node on the ground. μ represents the rolling friction coefficient of the wheel, m represents the mass of the flying car, g represents the acceleration due to gravity, D n represents the distance between the nth node and the (n + 1)th node, r slip represents the wheel slip ratio, ρ represents the air density, A f represents the frontal windward area of the flying car, C d represents the air resistance coefficient, represents the average speed of the flying car during ground motion; When the flying car takes off, the motion energy consumption cost E2 during the takeoff process of the flying car is calculated by the following formula: Among them, E 2,n represents the motion energy consumption cost for the flying car to move from the nth node on the ground to the (n + 1)th node in the air, π represents the pi, ρ represents the air density, m represents the mass of the flying car, g represents the acceleration due to gravity, b represents the number of rotors of the flying car, r represents the radius of the rotors of the flying car, D n represents the distance between the nth node and the (n + 1)th node, η represents the efficiency of the rotor motor, represents the average speed during the takeoff process of the flying car, A u represents the area of the top region of the flying car, C d represents the air resistance coefficient; When the flying car is flying at low altitude, the motion energy consumption cost E3 of the flying car flying in the air is calculated by the following formula: Among them, E 3,n represents the energy consumption cost of the flying car during the flight from the nth node to the (n + 1)th node in the air. π represents the pi, ρ represents the air density, m represents the mass of the flying car, g represents the acceleration due to gravity, b represents the number of rotors of the flying car, r represents the radius of the rotors of the flying car, D n represents the distance between the nth node and the (n + 1)th node, η represents the efficiency of the rotor motor, represents the average speed during the flight of the flying car, A f represents the frontal windward area of the flying car, C d represents the air resistance coefficient; When the flying car lands, the motion energy consumption cost E4 during the landing process of the flying car is calculated by the following formula: Among them, E 4,n represents the energy consumption cost of the flying car moving from the nth node in the air to the (n + 1)th node on the ground. π represents the pi, ρ represents the air density, m represents the mass of the flying car, g represents the acceleration due to gravity, b represents the number of rotors of the flying car, r represents the radius of the rotors of the flying car, D n represents the distance between the nth node and the (n + 1)th node, η represents the efficiency of the rotor motor, represents the average speed during the landing process of the flying car, A u represents the area of the top region of the flying car, C d represents the air resistance coefficient; S42. Calculate the surface energy consumption cost E5 of the flying car when moving on the ground. The calculation formula is as follows: Among them, E 5,n represents the surface energy consumption cost between the nth node and the (n + 1)th node on the ground, x n , y n and z n represent the coordinates of the current node n on the x-axis, y-axis, and z-axis, x n+1 , y n+1 and z n+1 represent the coordinates of the extended point n + 1 on the x-axis, y-axis, and z-axis, and k represents the surface passing coefficient; S43. Calculate the loss cost E6 during the mode switching of the flying car. The calculation formula is as follows: Where, P represents the output power of the rotor motor under rated working conditions, η represents the efficiency of the rotor motor, and T5 represents the time of mode switching; S44. Unify the dimensions of the motion energy consumption cost, surface energy consumption cost, and loss cost during mode switching of the flying car. The calculation formula is as follows: Among them, e 1,n represents E after unifying the dimension 1,n , E 1,n represents the energy consumption cost of the flying car moving from the nth node to the (n + 1)th node on the ground, e 2,n represents E after unifying the dimension 2,n , E 2,n represents the energy consumption cost of the flying car moving from the nth node on the ground to the (n + 1)th node in the air, e 3,n represents E after unifying the dimension 3,n , E 3,n represents the flight energy consumption cost of the flying car moving from the nth node to the (n + 1)th node in the air, e 4,n represents E after unifying the dimension 4,n , E 4,n represents the energy consumption cost of the flying car moving from the nth node in the air to the (n + 1)th node on the ground, e 5,n represents E after unifying the dimension 5,n , E 5,n represents the ground energy consumption cost between the nth node and the (n + 1)th node, e6 represents E6 after unifying the dimension, and E6 represents the loss cost during the mode switching of the flying car; S45. Calculate the total estimated cost of the current node n according to the motion energy consumption cost, ground energy consumption cost, and loss cost during mode switching of the flying car after unifying the dimensions. The calculation formula is as follows: F(n) = G(n) + H(n) Among them, G(n) represents the actual cost from the current node n to the expanded node n+1, G(n-1) represents the actual cost from the parent node n-1 to the current node n, H(n) represents the estimated cost from the expanded node n+1 to the end point N, F(n) represents the total estimated cost of the expanded node n+1, ω1 represents the weight of the energy consumption cost, T(n,n+1) represents the time for the flying car to move from the current node n to the expanded node n+1, ω2 represents the weight of the time interval, T(n+1,N) represents the time for the flying car to move from the expanded node n+1 to the end point N, e1 represents the motion energy consumption cost when the flying car moves on the ground, e2 represents the take-off energy consumption cost during the take-off process of the flying car after unifying the dimensions, e3 represents the flight energy consumption cost when the flying car is flying in the air after unifying the dimensions, e4 represents the landing energy consumption cost during the landing process of the flying car after unifying the dimensions, e5 represents the surface energy consumption cost when the flying car moves on the ground after unifying the dimensions, e6 represents the loss cost during the mode switching of the flying car, Z n represents the height of the current node n, Z n+1 represents the height of the expanded node n+1, Z N represents the height of the end point N.

5. A flight vehicle mode switching control system, characterized in that: It includes a host computer module, a communication module, a lower computer module, a drive system, and a remote control system; The host computer module includes an environment modeling module and a path planning module. The path planning module is used to plan a path according to the method described in any one of claims 1-4; The communication module is used to transmit the path planned by the host computer to the lower computer and store it, and to transmit the information collected by the lower computer to the host computer and store it; The lower computer module includes a mode switching state machine, a bottom layer controller, sensors, and an emergency planning module. The mode switching state machine is used to preset the mode switching state according to the path planned by the host computer and autonomously switch between relative ground movement and relative space movement; the bottom layer controller is used to control the state of the flying car and transmit the collected information to the path planning module through the communication module; the sensors are used to detect the state of the flying car and the external environment; the emergency planning module is used to plan a path or find a landing point in case of an emergency; The drive system includes a ground control system and an air control system; the ground control system includes a vehicle chassis controller, hub motors, and steering motors; the vehicle chassis controller is used to control the hub motors and steering motors according to the control signals output by the bottom layer controller to achieve the driving, braking, and steering of the flying car, and transmit the state information of the flying car on the ground to the bottom layer controller; The air control system includes a flight rotor controller, an electronic speed controller module, and rotor motors. The flight rotor controller is used to control the rotor motors according to the control signals output by the bottom layer controller to achieve hovering, orbiting, low-altitude acceleration, and low-altitude deceleration, and transmit the state information of the flying car in the air to the bottom layer controller; The remote control system is connected to the path planning module through a wireless communication module, used to store the information transmitted by the path planning module, and monitor the state of the flying car. The remote control system is also used to manually control the flying car by sending control signals to the host computer; 6. The flight vehicle mode switching control system according to claim 5, wherein: Emergency situations include: Bad weather, the flying car flying height exceeds the maximum height, there are no feasible path points, and the flying car has insufficient power; When the flying car encounters bad weather during driving, the flying car enters an emergency state. At this time, it can be manually operated through the remote control system to control the autopilot system of the flying car, or the remote control system can send a command to land nearby to the emergency planning module through the host computer. The emergency planning module will send the nearest landing point found to the bottom layer control module, and the bottom layer control module will issue a control command to control the flying car to land nearby; it can also exit the autopilot system and be landed by the driver at the landing point searched by the emergency planning module; When the flying car is in the air, the mode switching state machine will perform position detection. If the flying height of the flying car exceeds the maximum height set by the system, the flying car will enter an emergency state. At this time, the remote control system will adjust the height of the flying car to within the safe driving height range through manual operation, or exit the automatic driving system and the driver will operate to adjust the height of the flying car to within the safe driving height range; When the upper computer encounters an obstacle in path planning, the emergency planning module plans the path according to the flying car motion planning method described in claims 1-4. When the emergency planning module fails to search for valid path nodes, the flying car enters an emergency state. At this time, the remote control system sends a replanning command to the emergency planning module. The emergency planning module roughly plans the route based on the information detected by the sensors, and the flying car is controlled manually through the remote control system; When the system prompts that the battery is low during the flight of the flying car, the flying car enters an emergency state. At this time, the remote control system manually adds the nearest landing point to the path planned by the upper computer, so that the flying car vertically lands at the nearest landing point for charging, and replans the path in the upper computer according to the current landing point and the destination.

7. The flight vehicle mode switching control system according to claim 5, wherein: The mode switching state machine sets 15 states, 6 state transition conditions and 8 output commands according to the 4 modes of the flying car; The 4 modes include: ground driving, air flight, vertical takeoff and vertical landing; The 15 states include: Start Begin = 0, Ground movement preparation Ground_pre = 1, During ground movement Ground_pre = 2, Ground movement completed Ground_finsh = 3, Vertical takeoff preparation Takeoff_pre = 4, During vertical takeoff Takeoff_ing = 5, Vertical takeoff completed Takeoff_finsh = 6, Low-altitude flight preparation Fly_pre = 7, During low-altitude flight Fly_ing = 8, Low-altitude flight completed Fly_finsh = 9, Vertical landing preparation Land_pre = 10, During vertical landing Land_ing = 11, Vertical landing completed Land_finsh = 12, Emergency state State of emergency = 13 and End Finsh = 14; The 6 state transition conditions include: the height of the path node is equal to 0, the height of the next path node is not equal to 0, no feasible path node, reaching the end point, the flying height of the flying car exceeding the maximum height and insufficient battery power; The 8 output commands include: locking the rotor motor, unlocking the rotor motor, locking the hub motor, unlocking the hub motor, unlocking the steering motor, locking the steering motor, position detection and confirming landing.

8. The flight vehicle mode switching control system according to claim 7, wherein: The mode switching state machine performs autonomous switching according to the path planned by the upper computer through the following method; When the starting point of the flying car is on the ground, the flying car enters the starting state Begin = 0. It judges whether the next path node is on the ground according to the planned path. If so, the height of the next path node is 0, and the flying car enters the ground movement preparation state Ground_pre = 1. At this time, the hub motors and the steering motor are unlocked. After the hub motors and the steering motor are unlocked, it enters the ground movement process Ground_pre = 2. When the flying car moves to the specified path node, the flying car enters the ground movement completion state Ground_finsh = 3; if not, the height of the next path node is not equal to 0. At this time, the hub motors and the steering motor are locked. After the hub motors and the steering motor are locked, it enters the vertical takeoff preparation state Takeoff_pre = 4. At the same time, the rotor motors are unlocked. After the rotor motors are unlocked, it enters the vertical takeoff process Takeoff_ing = 5. At this time, the mode switching state machine issues a position detection instruction to judge whether the flying height of the flying car exceeds the maximum height. If so, the flying car enters the emergency state State of emergency = 13. If not, it continues vertical takeoff. When the flying car moves to the specified path node, the flying car enters the vertical takeoff completion state Takeoff_finsh = 6; wait for the next path node until the next path node is the end point, and the movement of the flying car ends Finsh = 14; When the starting point of the flying car is in the air, the flying car enters the starting state Begin = 0. It judges whether the next path node is in the air according to the planned path. If so, the height of the next path node is not equal to 0, and the flying car enters the low-altitude flight preparation state Fly_pre = 7. At the same time, the rotor motors are unlocked. After the rotor motors are unlocked, it enters the low-altitude flight process Fly_ing = 8. The mode switching state machine issues a position detection instruction to judge whether the flying height of the flying car exceeds the maximum height. If it exceeds the maximum height, the flying car enters the emergency state State of emergency = 13. If it does not exceed the maximum height, it continues low-altitude flight. When the flying car moves to the specified path node, it enters the low-altitude flight completion state Fly_finsh = 9; if not, the height of the next path node is equal to 0, and the flying car enters the vertical landing preparation state Land_pre = 10. When the remote interaction module confirms safety and then confirms the landing, the flying car enters the vertical landing process Land_ing = 11. When the flying car moves to the specified path node, the flying car enters the vertical landing completion state Land_finsh = 12. At this time, the rotor motors are locked. Wait for the next path node until the next path node is the end point, and the movement of the flying car ends Finsh = 14; When the flight vehicle shows insufficient power in the air flight system, the flight vehicle enters the emergency state State of emergency = 13. The emergency planning module searches for and determines the nearest landing point, and the flight vehicle enters the vertical landing preparation state Land_pre = 10. When the remote interaction module confirms safety and then confirms the landing, the flight vehicle enters the vertical landing process Land_ing = 11. When the flight vehicle moves to the landing point, the flight vehicle enters the vertical landing completion state Land_finsh = 12. At this time, the rotor motor is locked and the flight vehicle is charged.

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