Multi-source energy-saving obstacle avoidance control method and system for ocean energy driven robot

Abstract

The invention discloses a multi-source energy-saving obstacle avoidance control method and system for an ocean energy driven robot, and relates to the field of ocean energy driven robots. The method aims at solving the problem that energy capture and consumption of an ocean energy driven robot in the danger avoidance process are not considered in an existing danger avoidance method. The method comprises the following steps: acquiring marine environment information of a navigation area of the ocean energy driven robot in a current decision period and position, attitude and speed information of the ocean energy driven robot, and obtaining a speed limit set, a collision avoidance action set and a speed set which can be reached in a next decision period; obtaining an action space window of the current decision period; sampling in the action space window to obtain a current candidate water linear velocity and an angular velocity, and obtaining a current candidate trajectory cluster through a state transition equation; and selecting the optimal speed and angular speed in the candidate trajectory cluster by using the evaluation function, and realizing multi-source energy-saving obstacle avoidance control of the ocean energy driven robot in the current decision period.

Description

Technical Field

[0001] The invention belongs to the field of ocean energy driven robots. Background Art

[0002] As a new concept of marine navigation vehicle, the ocean energy-driven robot can obtain various energies from the surrounding marine environment and atmospheric environment through various devices carried by it. It has the characteristics of high endurance and adaptability, and saves energy. The ocean energy-driven robot relies on various devices to convert ocean energy into its own power or electrical energy. It is also equipped with propeller thrusters to ensure its normal navigation in harsh environments. Due to its high endurance and strong adaptability, the ocean energy-driven robot can play an irreplaceable role in tasks such as large-scale marine environment detection, cruising, and exploration.

[0003] Path planning is one of the core technologies in the research of ocean-powered robots. A good navigation route helps ocean-powered robots complete their tasks efficiently and with high quality. Ocean-powered robots will encounter unknown obstacles and other ships that are not marked on the map during actual navigation, so it is necessary to explore the danger avoidance methods of ocean robots.

[0004] The paper "Exploiting ocean energy for improved AUV persistent presence: path planning based on spatiotemporal current forecasts" proposed an energy-saving path planning method based on particle swarm optimization algorithm in a time-varying and uncertain ocean current environment, combining the ocean current forecast model to obtain a more optimized track. However, the paper did not consider the conversion and utilization of other ocean energy by the vehicle.

[0005] The paper "Research on Speed ​​Prediction and Path Planning of Wave Glider" incorporates the predicted speed of wave glider into the algorithm when planning the path of wave glider, that is, the influence of ocean environmental factors such as effective wave height, surface current and wind speed on wave glider is considered in the path planning process. However, the model mentioned in the paper calculates the offline speed of wave glider through the offline speed prediction model of wave glider, which lacks the real-time performance of the algorithm in actual engineering applications.

[0006] Most of the above literature only captures and utilizes one type of ocean energy, and most of them lack real-time performance. In actual engineering applications, there are more complex ocean environments with multiple energy sources available for ocean energy-driven robots to capture and utilize. Summary of the invention

[0007] The present invention aims to solve the problem that the existing danger avoidance methods do not take into account the energy capture and consumption of ocean energy-driven robots during the danger avoidance process, and now provides a multi-source energy-saving obstacle avoidance control method and system for ocean energy-driven robots.

[0008] A multi-source energy-saving obstacle avoidance control method for an ocean energy-driven robot, including:

[0009] Collect the ocean environment information of the navigation area of ​​the ocean energy driven robot in the current decision cycle and the position, posture and speed information of the ocean energy driven robot itself;

[0010] According to the collected information, the speed limit set, collision avoidance action set and speed set that can be achieved in the next decision cycle of the ocean energy-driven robot are obtained, and the intersection of all sets is taken to obtain the action space window of the current decision cycle;

[0011] Sampling is performed within the action space window of the current decision cycle to obtain candidate waterline velocities and angular velocities of the ocean energy-driven robot in the current decision cycle, and a candidate trajectory cluster of the ocean energy-driven robot in the current decision cycle is obtained through a state transition equation;

[0012] The evaluation function is used to select the optimal speed and angular velocity in the candidate trajectory cluster to realize the multi-source energy-saving obstacle avoidance control of the ocean energy-driven robot in the current decision cycle.

[0013] Furthermore, the marine environment information of the above-mentioned ocean energy-driven robot's navigation area includes: speed and direction information of sea breeze and ocean current, light radiation information of sunlight, wave direction, wave period and wave height information of waves, and static obstacle information around the ocean energy-driven robot that has not been marked in advance.

[0014] Furthermore, the speed limit set, collision avoidance action set and speed set expressions that can be achieved in the next decision cycle of the above-mentioned ocean energy driven robot are:

[0015] V S ={(v w ,ω)|v w ∈[v wmin ,v wmax ],ω∈[ω min ,ω max ]},

[0016]

[0017] Among them, V S 、V a and V d are the speed limit set, collision avoidance action set and speed set that can be achieved in the next decision cycle of the ocean energy driven robot, respectively.w and ω represent the candidate waterline velocity and angular velocity sampled by the ocean energy driven robot in the set, respectively, v wmax and v wmin are the maximum and minimum permissible linear speeds of the ocean energy driven robot, ω max and ω min are the maximum and minimum permissible angular velocities of the ocean energy driven robot, dist(v w ,ω) represents the obstacle distance evaluation function, and are the waterline acceleration and angular acceleration of the ocean energy driven robot, v wa and ω a are the waterline speed and angular velocity of the ocean energy driven robot in the decision cycle, and are the maximum waterline acceleration and maximum angular acceleration of the ocean energy driven robot, T DWA for the next decision cycle.

[0018] Furthermore, the above intersection of all sets is taken to obtain the action space window of the current decision cycle, which is expressed as:

[0019] V r =V s ∩V a ∩V d ,

[0020] Among them, V r is the action space window of the current decision cycle.

[0021] Furthermore, the expression of the above state transition equation is:

[0022]

[0023] Where t is the current time, Δt is the time interval of the action space window, [x(t), y(t)] is the Cartesian coordinate of the ocean energy driven robot at time t, θ is the heading of the ocean energy driven robot relative to the ocean current, and v w and ω represent the candidate waterline velocity and angular velocity sampled by the ocean energy driven robot in the set, respectively. and are the waterline acceleration and angular acceleration of the ocean energy driven robot, E thrust The estimated energy consumption of the thruster for sampling the predicted trajectory, E wind is the estimated energy capture of the wind energy capture system, E solar is the estimated energy captured by the solar energy capture system, E N is the estimated energy capture of the NSV energy system, P Tpr is the predicted input power of the thruster, Pwb The charging power for charging the battery through the wind energy capture system, P θ is the solar input power of the ocean energy driven robot, P Npr Predicting power for energy systems of ocean powered robots.

[0024] Furthermore, the predicted input power P of the above thruster Tpr It is obtained through the propeller energy consumption conversion function, and the expression of the propeller energy consumption conversion function is:

[0025] P Tpr =f[R tol (H 1 / 3 ,T w ,θ we ),v cx ],

[0026] Where f[] represents the function symbol, R tol () represents the longitudinal total resistance function of the ocean energy driven robot, H 1 / 3 is the significant wave height, T w is the average wave period, θ we is the wave encounter angle, v cx The induced velocity of the ocean current in the X-axis direction of the geodetic coordinate system measured by the electromagnetic flow meter carried by the ocean energy driven robot;

[0027] The charging power P of the battery charged by the wind energy capture system wb The wind energy conversion function considering the influence of the pitch angle of the ocean energy driven robot is obtained, and the expression of the wind energy conversion function is:

[0028] P wb =ρ air ·C p1 (v app ·cosθ wsn )·v app ·cosθ wsn 3 ,

[0029] Among them, ρ air is the air density, C p1 is the composite wind energy utilization coefficient, v app is the apparent wind speed, θ wsn is the angle between the apparent wind flow direction and the wind turbine rotation axis, and:

[0030] cos 2 θ wsn =1-[sinθ p ·(sinθ ws sinθ ns +cosθws cosθ ns )] 2 ,

[0031] θ p is the longitudinal inclination angle of the ocean energy driven robot, θ ws is the angle between the apparent wind flow direction and the south, θ ns is the angle between the bow of the ocean energy driven robot and the south;

[0032] Solar input power P of the ocean energy driven robot θ It is obtained through the solar energy conversion function, and the expression of the solar energy conversion function is:

[0033] P θ =21.5 G θ ·η STC ·A sp ,

[0034] Among them, η STC A is the light energy conversion factor that takes into account the battery charging loss. sp is the area of ​​the photovoltaic panel, G θ is the solar radiation intensity on the photovoltaic panel plane of the ocean energy driven robot, and:

[0035] G θ =G DH ·(cosθ i / sinθ α ),

[0036] cosθ i = sinθ α cosθ p +cosθ α sinθ As sinθ p sinθ ns +cosθ α cosθ As sinθ p cosθ ns ,

[0037] G DH is the solar radiation intensity at the earth’s horizontal surface, θ i is the incident angle of sunlight to the photovoltaic panel surface, θ α is the solar altitude angle, θ As is the solar azimuth;

[0038] Predicted power P of the energy system of the ocean energy driven robot Npr It is obtained through the energy system energy consumption conversion function, and the expression of the energy system energy consumption conversion function is:

[0039]

[0040] P ei =P wb +P θ ,

[0041] P eo =P ru +P eps +P snc +P ce ,

[0042] Among them, U bat is the current voltage of the battery, U max and U min are the maximum and minimum voltages of the battery, P ei is the energy input power of the ocean energy driven robot, P eo is the output power of the energy system of the ocean energy driven robot, P ru is the predicted output power of the servo, P eps is the predicted output power of the environment perception system, P snc is the predicted output power of the satellite navigation and communication system, P ce It is the output power of the electronic components of the control system.

[0043] Furthermore, the expression of the above evaluation function G(v,ω) is:

[0044] G(v,ω)=σ[α·heading(v w ,ω)+β·dist(v w ,ω)+γ·velocity(v w ,ω)+λ·energy(v w ,ω)],

[0045] Among them, σ is the normalization coefficient;

[0046] α is the azimuth evaluation coefficient, heading(v w ,ω) is the azimuth evaluation function;

[0047] β is the obstacle distance evaluation coefficient, dist(v w ,ω) is the obstacle distance evaluation function;

[0048] γ is the speed evaluation coefficient, velocity(v w ,ω) is the speed evaluation function;

[0049] λ is the energy evaluation coefficient, energy(v w ,ω) is the energy evaluation function.

[0050] Multi-source energy-saving obstacle avoidance control system for ocean energy-driven robots, including:

[0051] Information collection unit: collects the marine environment information of the navigation area of ​​the ocean energy driven robot in the current decision cycle and the position, posture and speed information of the ocean energy driven robot itself;

[0052] Window calculation unit: obtains the speed limit set, collision avoidance action set and speed set that can be achieved in the next decision cycle of the ocean energy-driven robot based on the collected information, and takes the intersection of all sets to obtain the action space window of the current decision cycle;

[0053] Trajectory cluster calculation unit: sampling in the action space window of the current decision cycle, obtaining the candidate waterline velocity and angular velocity of the ocean energy-driven robot in the current decision cycle, and obtaining the candidate trajectory cluster of the ocean energy-driven robot in the current decision cycle through the state transition equation;

[0054] Optimization unit: Use the evaluation function to select the optimal speed and angular velocity in the candidate trajectory cluster to achieve multi-source energy-saving obstacle avoidance control of the ocean energy-driven robot in the current decision cycle.

[0055] A multi-source energy-saving obstacle avoidance control device for an ocean-powered robot, wherein the preset performance control device comprises a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the multi-source energy-saving obstacle avoidance control method for an ocean-powered robot as described above.

[0056] A computer storage medium stores at least one instruction, which is loaded and executed by a processor to implement the multi-source energy-saving obstacle avoidance control method for an ocean energy-driven robot as described above.

[0057] The beneficial effects of the present invention are:

[0058] The multi-source energy-saving obstacle avoidance control method and system for the ocean energy-driven robot described in the present invention establishes an energy system energy consumption conversion function, fully balances the state of energy capture and consumption, optimizes the traditional DWA collision avoidance algorithm, and screens out dangerous avoidance paths with lower overall system energy consumption while ensuring navigation safety, thereby improving the endurance of the ocean energy-driven robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 The principle flow chart of the multi-source energy-saving obstacle avoidance control method for the ocean energy-driven robot;

[0060] Figure 2 It is a schematic diagram of a dynamic window;

[0061] Figure 3 This is a schematic diagram of wind power generation pitch apparent wind compensation;

[0062] Figure 4 This is a schematic diagram of the compensation of the pitch radiation intensity of photovoltaic power generation;

[0063] Figure 5 It is the relationship diagram between the velocity of the ocean current over the ground and the data measured by the current meter;

[0064] Figure 6 This is the motion model diagram of the ocean energy driven robot;

[0065] Figure 7 A cluster diagram of candidate trajectories for the ocean energy driven robot;

[0066] Figure 8 This is a schematic diagram of the azimuth evaluation of the ocean energy driven robot;

[0067] Fig. 9 Block diagram of a multi-source energy-saving obstacle avoidance control system for an ocean energy-driven robot. DETAILED DESCRIPTION

[0068] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0069] Specific implementation method 1: refer to Figures 1 to 8 Specifically describing this embodiment, the multi-source energy-saving obstacle avoidance control method for an ocean energy-driven robot described in this embodiment, the ocean energy-driven robot in this embodiment refers to an unmanned ocean vehicle in a broad sense that is equipped with a device that can convert ocean energy into its own electrical energy or kinetic energy through various energy conversion devices.

[0070] The multi-source energy-saving obstacle avoidance control method comprises:

[0071] Step 1: Obtain the ocean environment information of the navigation area of ​​the ocean energy-driven robot and the ocean energy-driven robot's own position, posture and speed information. The ocean energy-driven robot is equipped with a binocular vision system, a Beidou integrated machine, a combined navigation system, a weather station, an electromagnetic current meter, a wave sensor and a silicon photonic total radiation sensor, which can obtain the ocean environment information around the unmanned boat and its own position, posture and speed information in real time.

[0072] The above-mentioned ocean environment information includes: speed and direction information of sea breeze and ocean current, light radiation information of sunlight, wave direction, wave period and wave height information, and static obstacle information around the ocean energy-driven robot that has not been marked in advance.

[0073] Step 2: Obtain the speed limit set of the ocean energy driven robot respectively, so that the ocean energy driven robot can avoid collision with obstacles by emergency parking. Under the maximum acceleration constraint, the ocean energy driven robot will DWA The speed set that can be achieved.

[0074] With v w and ω represent the candidate waterline velocity and angular velocity of the ocean energy driven robot sampled in the ensemble, respectively.

[0075] V S It represents the speed limit set of the ocean energy driven robot determined by the maximum and minimum allowable linear speed and angular speed of the ocean energy driven robot. The specific mathematical formula is:

[0076] V S ={(v w ,ω)|v w ∈[v wmin ,v wmax ],ω∈[ω min ,ω max ]},

[0077] In the formula, v wmax and v wmin are the maximum and minimum permissible linear speeds of the ocean energy driven robot respectively; ω max and ω min are the maximum and minimum allowed angular velocities of the ocean energy driven robot, respectively.

[0078] V a represents any set of collision avoidance actions that enable the ocean energy-driven robot to avoid collision with obstacles by emergency stopping. The specific mathematical formula is:

[0079]

[0080] In the formula, dist(v w ,ω) represents the obstacle distance evaluation function, which is used to evaluate the distance between the ocean energy driven robot and the nearest obstacle on the current trajectory; The waterline acceleration of the ocean energy driven robot; is the angular acceleration of the ocean energy driven robot.

[0081] V d Indicates the current waterline speed v wa With angular velocity ωa And the ocean energy-driven robot at maximum linear acceleration With maximum angular acceleration Under the constraint, the next decision cycle T DWA The speed set that can be achieved by internal energy, the specific mathematical formula is:

[0082]

[0083] Step 3: Speed ​​limit set, collision action set and next decision cycle T DWA The intersection of the speed set that can be achieved is used to obtain the action space window V r :

[0084] V r =V s ∩V a ∩V d .

[0085] Step 4: Establish wind energy conversion function, solar energy conversion function, propeller energy consumption conversion function, energy input conversion function, energy output conversion function and energy system energy consumption conversion function respectively.

[0086] The wind energy conversion function expression is as follows:

[0087]

[0088] Among them, P wb Charging power for charging the battery through the wind energy capture system; η wb is the battery charging loss coefficient; R is the radius of the wind turbine blade; C p is the wind energy utilization coefficient; U0 is the air flow speed; C p1 is the composite wind energy utilization coefficient; ρ air is the air density, ρ air According to the formula ρ air (T,p a )=1.293·(p a / 101.325)·(273.15 / T) is calculated, T is the thermodynamic temperature of the environment where the wind energy capture system is located, p a The atmospheric pressure of the environment in which the wind energy capture system is located.

[0089] The wind energy conversion function considering the influence of the pitch angle of the ocean energy driven robot is expressed as follows:

[0090] P wb =ρ air (T,p a )·C p1 (v app ·cosθ wsn )·vapp ·cosθ wsn 3 ,

[0091] cos 2 θ wsn =1-[sinθ p ·(sinθ ws sinθ ns +cosθ ws cosθ ns )] 2 ,

[0092] In the formula, v app is the apparent wind speed; θ wsn is the angle between the apparent wind flow direction and the wind turbine rotation axis; θ p is the longitudinal inclination angle of the ocean energy driven robot (bow inclination is positive, stern inclination is negative); θ ws is the angle between the apparent wind flow direction and the south (positive to the east, negative to the west); θ ns It is the angle between the bow of the ocean energy driven robot and the south (positive to the east and negative to the west).

[0093] The solar energy conversion function expression is as follows:

[0094] P θ =21.5 G θ ·η STC ·A sp ,

[0095] G θ =G DH ·(cosθ i / sinθ α ),

[0096] cosθ i = sinθ α cosθ p +cosθ α sinθ As sinθ p sinθ ns +cosθ α cosθ As sinθ p cosθ ns ,

[0097] Where P θ is the solar input power of the ocean energy driven robot, G θ is the solar radiation intensity on the photovoltaic panel plane of the ocean energy driving robot; G DH is the solar radiation intensity at the earth’s horizontal surface; θ iis the incident angle of sunlight to the photovoltaic panel surface; θ α is the solar altitude angle; θ As is the solar azimuth (positive to the east, negative to the west); η STC A is the light energy conversion coefficient that takes into account the battery charging loss; sp is the area of ​​the photovoltaic panel.

[0098] The thruster energy conversion function expression is as follows:

[0099] v cux =v cx -v x ,

[0100] v cuy =v cy -v y ,

[0101] P Tpr =f[R tol (H 1 / 3 ,T w ,θ we ),v cx ],

[0102] Where XY is the geodetic coordinate system, then v cux and v cuy They represent the velocity of the ocean current over the ground in the X-axis and Y-axis directions, v x and v y They represent the speed of the ocean energy driven robot over the ground in the X-axis and Y-axis directions, v cx and v cy They respectively represent the induced flow velocities of the ocean current in the X-axis and Y-axis directions measured by the electromagnetic flowmeter carried by the ocean energy driven robot.

[0103] H 1 / 3 is the significant wave height; T w is the average wave period; θ we is the wave encounter angle; R tol is the total longitudinal resistance of the ocean energy driven robot; P Tpr is the predicted input power of the thruster; f[] represents the function sign.

[0104] The energy input conversion function expression is as follows:

[0105] P ei =P wb +P θ ,

[0106] P θ =21.5 G θ ·η STC ·A sp ,

[0107] Among them, P ei is the energy input power of the ocean energy driven robot; P wb is the wind energy input power of the ocean energy driven robot; P θ is the solar input power of the ocean energy driven robot.

[0108] The energy output conversion function expression is as follows:

[0109] P eo =P ru +P eps +P snc +P ce ,

[0110] Among them, P eo is the output power of the energy system of the ocean energy driven robot; P ru is the predicted output power of the servo; P eps is the predicted output power of the environmental perception system; P snc is the predicted output power of the satellite navigation and communication system; P ce It is the output power of the electronic components of the control system.

[0111] The energy system energy consumption conversion function expression is as follows:

[0112]

[0113] Among them, P Npr is the predicted power of the ocean energy driven robot energy system; U bat is the current voltage of the battery; U max and U min They are the maximum and minimum voltages of the battery respectively.

[0114] When the battery is fully charged, if the system energy input is greater than the system energy output, all input energy will be converted into electrical loss and cannot be stored in the battery; when the battery is lower than the operating voltage, all output systems of the ocean energy-driven robot except the control system electronic components will stop working, in order to store energy as quickly as possible to achieve normal working state.

[0115] Step 5: Sampling is performed within the action space window of each decision cycle to obtain different candidate speeds and angular velocities, and the candidate trajectory cluster under the current state is obtained through the state transition equation; an evaluation function is designed, and the evaluation function is used to select the optimal speed and angular velocity from the candidate trajectory cluster to achieve multi-source energy-saving obstacle avoidance of the ocean energy-driven robot in the current decision cycle.

[0116] Establish the motion model and state transition equation of the ocean energy driven robot:

[0117] In a very short period of time, the trajectory of the ocean energy driven robot can be approximately regarded as uniform linear motion. Figure 6 As shown, XY is the geodetic coordinate system, x robot -y robot is the attached coordinate system, θ represents the heading angle of the ocean energy driven robot. At this time, the kinematic model of the ocean energy driven robot can be expressed by the following formula:

[0118]

[0119] Where [x(t), y(t)] is the Cartesian coordinate of the ocean energy driven robot, which can be expressed as u is the longitudinal linear velocity in the same direction as the forward direction of the ocean energy driven robot, v is the transverse linear velocity perpendicular to the forward direction of the ocean energy driven robot; ψ(t) is the heading angle of the ocean energy driven robot, is the first-order derivative of ψ(t), that is, the angular velocity r(t); the velocity vector of the ocean energy driven robot in the Cartesian coordinate system can be expressed as Indicates the speed of the ocean-powered robot in the X-axis direction, Indicates the speed of the ocean-powered robot in the Y-axis direction. To express the dynamic constraints, the longitudinal speed of the ocean-powered robot satisfies 0≤u(t)≤u max , the angular velocity satisfies -r max ≤r(t)≤r max ,u max is the maximum longitudinal velocity, r max is the maximum angular velocity.

[0120] The state of the ocean energy driven robot at a certain moment is represented by p(t) and v(t) at that moment. Then the two variables of the sampling action (v, ω) in the DWA algorithm are added to obtain the state. Its mathematical expression is: The state transition equation of the ocean energy driven robot can be obtained:

[0121]

[0122] Among them, v w (t) = v e (t)+v cu , t is the current time, Δt is the time interval of the action space window, θ is the heading of the ocean energy driven robot relative to the ocean current, v e is the speed of the ocean energy driven robot over the ground, v cu is the speed of the ocean current over the ground.

[0123] Estimated thruster energy consumption E for sampled predicted trajectory thrust, Estimated energy capture of wind energy capture system wind and the estimated energy capture E of the solar energy capture system solar The predicted input power P of the thruster is Tpr , the charging power P of the battery charged by the wind energy capture system wb 、The solar input power P of the ocean energy driven robot θ Obtain; The predicted power P of the robot energy system driven by ocean energy Npr Get the estimated energy capture E of the entire ocean energy-driven robot energy system N .

[0124] The evaluation function formula of the DWA (dynamic window method) algorithm considering energy capture and consumption is:

[0125] G(v,ω)=σ[α·heading(v w ,ω)+β·dist(v w ,ω)+γ·velocity(v w ,ω)+λ·energy(v w ,ω)]

[0126] In the formula, σ represents the normalization coefficient;

[0127] heading(v w ,ω) is the azimuth evaluation function, which is used to evaluate the angle difference between the direction and the target when the ocean energy driven robot reaches the end of the simulation trajectory at the currently set sampling speed. The smaller the angle difference, the smaller the deviation between the heading at the end of the ocean energy driven robot trajectory and the target heading. However, the evaluation function is to find the maximum value, so the angle difference needs to be processed as a supplementary angle. α is the azimuth evaluation coefficient. The larger α is, the closer the selected trajectory is to the target heading.

[0128] dist(v w ,ω) is the obstacle distance evaluation function, which is used to evaluate the distance between the current trajectory and the nearest obstacle. β is the obstacle distance evaluation coefficient. The larger β is, the farther the target from the obstacle is selected.

[0129] velocity(v w ,ω) is the speed evaluation function, which is used to evaluate the speed of the current trajectory. γ is the speed evaluation coefficient. The larger the γ, the faster the selected trajectory speed is, and the faster it can approach the target point.

[0130] energy(v w,ω) is the energy evaluation function, which is used to evaluate the estimated energy consumption value on the current trajectory. The smaller the value of the energy evaluation function, the less energy consumption of the ocean energy-driven robot. λ is the energy evaluation coefficient. The larger the λ, the less energy consumption of the selected trajectory, and the more energy the trajectory captures.

[0131] Since the energy capture and energy consumption of ocean energy-driven robots in different marine environments are complex and changeable, the value of the energy evaluation function may be either positive or negative. Therefore, when normalizing the energy evaluation function, the sum normalization method needs to be adjusted. When calculating the cumulative value of the energy set, the absolute value of each element should be processed and then accumulated. The specific steps for normalizing the evaluation function are as follows:

[0132]

[0133]

[0134] Where n is the number of all sampled trajectories, and i is the number of the current trajectory to be evaluated.

[0135] According to the above method, after repeated iterations, the optimal trajectory is continuously searched to determine the collision avoidance actions that the ocean energy-driven robot should take, and finally reach the target point.

[0136] To sum up, this implementation method respectively establishes wind energy conversion function, solar energy conversion function, energy input conversion function, propeller energy consumption conversion function, energy output conversion function and energy system energy consumption conversion function, optimizes the traditional DWA collision avoidance algorithm, and screens out dangerous avoidance paths with lower overall energy consumption of the system while ensuring navigation safety, thereby improving the endurance of the ocean energy-driven robot, saving energy, and reducing the use cost of the ocean energy-driven robot.

[0137] Specific implementation method 2: refer to Fig. 9 Specifically describing this embodiment, the multi-source energy-saving obstacle avoidance control system for the ocean energy driven robot described in this embodiment includes:

[0138] Information collection unit: collects the marine environment information of the navigation area of ​​the ocean energy driven robot in the current decision cycle and the position, posture and speed information of the ocean energy driven robot itself;

[0139] The information collection unit can transmit the marine environment information of the sea area where the ocean energy driven robot is sailing, receive mission information, and process and optimize various data, regularly update the marine environment information, and provide the danger avoidance module with time-varying marine environment information and current posture information. The above marine environment information refers to the speed and direction information of sea breeze and current, the light radiation information of sunlight, the direction, period and height of waves, and the static obstacle information around the ocean energy driven robot that has not been marked in advance.

[0140] Window calculation unit: Based on the collected information, the speed limit set, collision avoidance action set and speed set that can be achieved in the next decision cycle of the ocean energy-driven robot are obtained, and the intersection of all sets is taken to obtain the action space window of the current decision cycle.

[0141] Trajectory cluster calculation unit: Sampling is performed in the action space window of the current decision cycle to obtain the candidate waterline velocity and angular velocity of the ocean energy-driven robot in the current decision cycle, and the candidate trajectory cluster of the ocean energy-driven robot in the current decision cycle is obtained through the state transition equation.

[0142] Optimization unit: Use the evaluation function to select the optimal speed and angular velocity in the candidate trajectory cluster to achieve multi-source energy-saving obstacle avoidance control of the ocean energy-driven robot in the current decision cycle. Determine the energy-saving avoidance action that the ocean energy-driven robot should take through optimization, and regularly update the candidate trajectory cluster and the current position of the ocean energy-driven robot according to the information transmitted by the information data processing module, and search for energy-saving danger avoidance paths according to the method described. The danger avoidance module continuously provides the latest optimal energy-saving danger avoidance action plan based on the updated marine environment information and the real-time position of the ocean energy-driven robot.

[0143] This embodiment also includes:

[0144] Environmental perception module: Obtain obstacle information around the current location through laser radar and binocular vision system; binocular vision system can realize day and night conversion function, using visible light camera during the day and infrared imaging camera at night. Obtain speed and direction information of sea breeze and current, light radiation information of sunlight, wave direction, wave period and wave height information through weather station, electromagnetic current meter, wave sensor and silicon photonic total radiation sensor.

[0145] Positioning module: obtains the position information of the ocean energy-driven robot through Beidou integrated machine and integrated navigation, transmits it to the information data processing module, and provides time-varying position information to the danger avoidance module;

[0146] Ocean energy driven robot: receives evasive action instructions, executes control mechanisms, and navigates according to prescribed instructions.

[0147] Specific implementation method three: The multi-source energy-saving obstacle avoidance control device for the ocean energy-driven robot described in this implementation method, the preset performance control device includes a processor and a memory, the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the multi-source energy-saving obstacle avoidance control method for the ocean energy-driven robot as described in specific implementation method one.

[0148] Specific implementation method 4: A computer storage medium, wherein at least one instruction is stored in the computer storage medium, and the at least one instruction is loaded and executed by a processor to implement the multi-source energy-saving obstacle avoidance control method for an ocean energy-driven robot as described in specific implementation method 1.

[0149] The present invention utilizes sensors carried by an ocean energy-driven robot to obtain information on the position, posture, and speed of the ocean energy-driven robot, information on surrounding obstacles, information on the speed and direction of sea breezes and currents, information on light radiation from sunlight, and information on the direction, period, and height of waves. The Dynamic Window Approaches (DWA) method cannot directly solve the problem of danger avoidance of ocean energy-driven robots under high endurance requirements. The present invention fully considers the energy capture and consumption issues of the ocean energy-driven robot in the process of danger avoidance. On the premise of ensuring navigation safety, a danger avoidance path with a smaller total system energy consumption is screened out, thereby improving the endurance of the ocean energy-driven robot.

[0150] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that the features described in conjunction with a single embodiment may be used in other described embodiments.

Claims

1. A multi-source energy-saving obstacle avoidance control method for an ocean energy-driven robot, characterized in that: include: Collect the ocean environment information of the navigation area of ​​the ocean energy driven robot in the current decision cycle and the position, posture and speed information of the ocean energy driven robot itself; According to the collected information, the speed limit set, collision avoidance action set and speed set that can be achieved in the next decision cycle of the ocean energy-driven robot are obtained, and the intersection of all sets is taken to obtain the action space window of the current decision cycle; Sampling is performed within the action space window of the current decision cycle to obtain candidate waterline velocities and angular velocities of the ocean energy-driven robot in the current decision cycle, and a candidate trajectory cluster of the ocean energy-driven robot in the current decision cycle is obtained through a state transition equation; The evaluation function is used to select the optimal speed and angular velocity in the candidate trajectory cluster to realize the multi-source energy-saving obstacle avoidance control of the ocean energy-driven robot in the current decision cycle.

2. The multi-source energy-saving obstacle avoidance control method for an ocean energy-driven robot according to claim 1, characterized in that: The ocean environment information of the navigation area of ​​the ocean energy driven robot includes: speed and direction information of sea wind and ocean current, light radiation information of sunlight, wave direction, wave period and wave height information of waves, and static obstacle information around the ocean energy driven robot that has not been marked in advance.

3. The multi-source energy-saving obstacle avoidance control method for an ocean energy-driven robot according to claim 1, characterized in that: The speed limit set, collision avoidance action set and speed set expressions that can be achieved in the next decision cycle of the ocean energy driven robot are: V S ={(v w ,ω)∣v w ∈[v wmin ,v wmax ],ω∈[ω min ,ω max ]}, Among them, V S 、V a and V d are the speed limit set, collision avoidance action set and speed set that can be achieved in the next decision cycle of the ocean energy driven robot, respectively. w and ω represent the candidate waterline velocity and angular velocity sampled by the ocean energy driven robot in the set, respectively, v wmax and v wmin are the maximum and minimum permissible linear speeds of the ocean energy driven robot, ω max and ω min are the maximum and minimum permissible angular velocities of the ocean energy driven robot, dist(v w ,ω) represents the obstacle distance evaluation function, and are the waterline acceleration and angular acceleration of the ocean energy driven robot, v wa and ω a are the waterline speed and angular velocity of the ocean energy driven robot in the decision cycle, and are the maximum waterline acceleration and maximum angular acceleration of the ocean energy driven robot, T DWA for the next decision cycle.

4. The multi-source energy-saving obstacle avoidance control method for an ocean energy-driven robot according to claim 3, characterized in that: The intersection of all sets is taken to obtain the action space window of the current decision cycle, which is expressed as: V r =V s ∩V a ∩V d , Among them, V r is the action space window of the current decision cycle.

5. The multi-source energy-saving obstacle avoidance control method for an ocean energy-driven robot according to claim 1, characterized in that: The expression of the state transition equation is: Where t is the current time, Δt is the time interval of the action space window, [x(t), y(t)] is the Cartesian coordinate of the ocean energy driven robot at time t, θ is the heading of the ocean energy driven robot relative to the ocean current, and v w and ω represent the candidate waterline velocity and angular velocity sampled by the ocean energy driven robot in the set, respectively. and are the waterline acceleration and angular acceleration of the ocean energy driven robot, E thrust The estimated energy consumption of the thruster for sampling the predicted trajectory, E wind is the estimated energy capture of the wind energy capture system, E solar is the estimated energy captured by the solar energy capture system, E N is the estimated energy capture of the NSV energy system, P Tpr is the predicted input power of the thruster, P wb The charging power for charging the battery through the wind energy capture system, P θ is the solar input power of the ocean energy driven robot, P Npr Predicting power for energy systems of ocean powered robots.

6. The multi-source energy-saving obstacle avoidance control method for an ocean energy-driven robot according to claim 5, characterized in that: The predicted input power P of the thruster Tpr It is obtained through the propeller energy consumption conversion function, and the expression of the propeller energy consumption conversion function is: P Tpr =f[R tol (H 1 / 3 ,T w ,θ we ),v cx ], Where f[] represents the function symbol, R tol () represents the longitudinal total resistance function of the ocean energy driven robot, H 1 / 3 is the significant wave height, T w is the average wave period, θ we is the wave encounter angle, v cx The induced velocity of the ocean current in the X-axis direction of the geodetic coordinate system measured by the electromagnetic flow meter carried by the ocean energy driven robot; The charging power P of the battery charged by the wind energy capture system wb The wind energy conversion function considering the influence of the pitch angle of the ocean energy driven robot is obtained, and the expression of the wind energy conversion function is: P wb =ρ air ·C p1 (v app ·cosθ wsn )·v app ·cosθ wsn 3 , Among them, ρ air is the air density, C p1 is the composite wind energy utilization coefficient, v app is the apparent wind speed, θ wsn is the angle between the apparent wind flow direction and the wind turbine rotation axis, and: cos 2 i wsn =1-[sinθ p ·(sinθ ws sinth ns +cosθ ws cosθ ns )] 2 , θ p is the longitudinal inclination angle of the ocean energy driven robot, θ ws is the angle between the apparent wind flow direction and the south, θ ns is the angle between the bow of the ocean energy driven robot and the south; Solar input power P of the ocean energy driven robot θ It is obtained through the solar energy conversion function, and the expression of the solar energy conversion function is: P θ =21.5 G θ ·or STC ·A sp , Among them, η STC A is the light energy conversion factor that takes into account the battery charging loss. sp is the area of ​​the photovoltaic panel, G θ is the solar radiation intensity on the photovoltaic panel plane of the ocean energy driven robot, and: G θ =G DH ·(cosθ i / sinθ α ), cosθ i =sinθ α cosθ p +cosθ α sinθ As sinθ p sinθ ns +cosθ α cosθ As sinθ p cosθ ns , G DH is the solar radiation intensity at the earth’s horizontal surface, θ i is the incident angle of sunlight to the photovoltaic panel surface, θ α is the solar altitude angle, θ As is the solar azimuth; Predicted power P of the energy system of the ocean energy driven robot Npr It is obtained through the energy system energy consumption conversion function, and the expression of the energy system energy consumption conversion function is: P ei =P wb +P θ , P eo =P ru +P eps +P snc +P ce , Among them, U bat is the current voltage of the battery, U max and U min are the maximum and minimum voltages of the battery, P ei is the energy input power of the ocean energy driven robot, P eo is the output power of the energy system of the ocean energy driven robot, P ru is the predicted output power of the servo, P eps is the predicted output power of the environment perception system, P snc is the predicted output power of the satellite navigation and communication system, P ce It is the output power of the electronic components of the control system.

7. The multi-source energy-saving obstacle avoidance control method for an ocean energy-driven robot according to claim 5, characterized in that: The expression of the evaluation function G(v,ω) is: G(v,ω)=σ[α·heading(v w ,ω)+β·dist(v w ,ω)+γ·velocity(v w ,ω)+λ·energy(v w ,ω)], Among them, σ is the normalization coefficient; α is the azimuth evaluation coefficient, heading(v w ,ω) is the azimuth evaluation function; β is the obstacle distance evaluation coefficient, dist(v w ,ω) is the obstacle distance evaluation function; γ is the speed evaluation coefficient, velocity(v w ,ω) is the speed evaluation function; λ is the energy evaluation coefficient, energy(v w ,ω) is the energy evaluation function.

8. A multi-source energy-saving obstacle avoidance control system for ocean energy-driven robots, characterized in that: include: Information collection unit: collects the marine environment information of the navigation area of ​​the ocean energy driven robot in the current decision cycle and the position, posture and speed information of the ocean energy driven robot itself; Window calculation unit: obtains the speed limit set, collision avoidance action set and speed set that can be achieved in the next decision cycle of the ocean energy-driven robot based on the collected information, and takes the intersection of all sets to obtain the action space window of the current decision cycle; Trajectory cluster calculation unit: sampling in the action space window of the current decision cycle, obtaining the candidate waterline velocity and angular velocity of the ocean energy-driven robot in the current decision cycle, and obtaining the candidate trajectory cluster of the ocean energy-driven robot in the current decision cycle through the state transition equation; Optimization unit: Use the evaluation function to select the optimal speed and angular velocity in the candidate trajectory cluster to achieve multi-source energy-saving obstacle avoidance control of the ocean energy-driven robot in the current decision cycle.

9. A multi-source energy-saving obstacle avoidance control device for ocean energy-driven robots, characterized in that: The preset performance control device includes a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the multi-source energy-saving obstacle avoidance control method for an ocean energy-driven robot as described in one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer storage medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the multi-source energy-saving obstacle avoidance control method for an ocean energy-driven robot as described in any one of claims 1 to 7.

Images