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

Abstract

The application relates to a multi-source energy-saving obstacle avoidance control method and system for a marine energy-driven robot, and relates to the field of marine energy-driven robots. The application is aimed at solving the problem that the existing danger avoidance method does not consider the energy capture and consumption of the marine energy-driven robot in the danger avoidance process. The application collects the marine environment information of the sailing area of the marine energy-driven robot in the current decision cycle, the position, attitude and speed information of the marine energy-driven robot, obtains a speed limit set, an obstacle avoidance action set and a speed set that can be reached in the next decision cycle, takes the intersection of all sets to obtain an action space window in the current decision cycle, samples the current candidate waterline speed and angular velocity in the action space window, and obtains a current candidate trajectory cluster through a state conversion equation; and the optimal speed and angular velocity are selected from the candidate trajectory cluster by using an evaluation function, so that the multi-source energy-saving obstacle avoidance control of the marine energy-driven robot in the current decision cycle is realized.

Description

Technical Field

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

[0002] As a new concept in ocean-going vehicles, ocean-powered robots can utilize various onboard devices to harvest various energies from the surrounding ocean and atmosphere. This allows for high endurance, adaptability, and energy conservation. These robots rely on various devices to convert ocean energy into their own power or electricity, and are equipped with propellers to ensure smooth navigation in harsh environments. Due to their high endurance and adaptability, ocean-powered robots are poised to play an irreplaceable role in missions such as large-scale ocean exploration, patrolling, and surveying.

[0003] Path planning is a core technology in the research of ocean-powered robots. A well-defined navigation route helps ocean-powered robots complete their missions efficiently and with high quality. During actual navigation, ocean-powered robots may encounter unknown obstacles and other vessels not marked on maps, necessitating the development of hazard avoidance methods for ocean-powered robots.

[0004] The paper "Exploiting ocean energy for improved AUV persistent presence: path planning based on spatiotemporal current forecasts" proposes an energy-efficient path planning method based on a particle swarm optimization algorithm in a time-varying and uncertain ocean current environment. This method, combined with an ocean current forecast model, achieves a more optimized trajectory. However, the paper does not consider the conversion and utilization of other ocean energy sources by the vehicle.

[0005] The paper "Research on Wave Glider Speed ​​Prediction and Path Planning" incorporates the predicted wave glider speed into its path planning algorithm. This means that the impact of ocean environmental factors such as significant wave height, surface currents, and wind speed on the wave glider is considered during the path planning process. However, the model proposed in this paper calculates the offline speed of the wave glider using an offline speed prediction model, which lacks the real-time performance required for practical 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, the more complex ocean environment has multiple energy sources that can be captured and utilized by ocean energy-driven robots. Summary of the Invention

[0007] The present application is to solve the problem that the existing danger avoidance method does not consider the energy capture and consumption of the ocean energy driven robot in the danger avoidance process, and provides a multi-source energy-saving obstacle avoidance control method and system for an ocean energy driven robot.

[0008] The multi-source energy-saving obstacle avoidance control method for the ocean energy driven robot comprises:

[0009] Collecting ocean environment information of a navigation area of the ocean energy driven robot in a current decision period and self position, attitude and speed information of the ocean energy driven robot;

[0010] Obtaining a speed limit set, a collision avoidance action set and a speed set that can be reached in a next decision period of the ocean energy driven robot according to the collected information, and taking the intersection of all sets to obtain an action space window of the current decision period;

[0011] Sampling in the action space window of the current decision period to obtain candidate waterline speed and angular velocity of the ocean energy driven robot in the current decision period, and obtaining a candidate trajectory cluster of the ocean energy driven robot in the current decision period through a state transition equation;

[0012] Selecting optimal speed and angular velocity in the candidate trajectory cluster by using an evaluation function to realize multi-source energy-saving obstacle avoidance control of the ocean energy driven robot in the current decision period.

[0013] Further, the ocean environment information of the navigation area of the ocean energy driven robot comprises speed and direction information of sea wind and sea current, light radiation information of sunlight, wave direction, wave period and wave height information, and static obstacle information around the ocean energy driven robot without prior labeling.

[0014] Further, the speed limit set, the collision avoidance action set and the speed set that can be reached in the next decision period of the ocean energy driven robot are respectively expressed as:

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

[0016]

[0017] wherein V S , V a and V d are respectively the speed limit set, the collision avoidance action set and the speed set that can be reached in the next decision period of the ocean energy driven robot, vw and ω respectively represent the candidate pair of surge and angular velocities sampled from the set V wmax and v wmin are the maximum and minimum allowable surge velocities of the OEDR, ω max and ω min are the maximum and minimum allowable angular velocities of the OEDR, dist(v w , ω) denotes the obstacle distance evaluation function, and are the pair of surge and angular accelerations of the OEDR, v wa and ω a are the pair of surge and angular velocities of the OEDR at the decision period, and are the maximum pair of surge and angular accelerations of the OEDR, T DWA is the next decision period.

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

[0019] V r = V s ∩ V a ∩ V d ,

[0020] where V r is the action space window of the current decision period.

[0021] Further, the expression of the 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 OEDR at time t, θ is the heading of the OEDR relative to the ocean current, v w and ω respectively represent the candidate pair of surge and angular velocities sampled from the set V and are the pair of surge and angular accelerations of the OEDR, E thrust is the estimated energy consumption of the propeller for the sampled predicted trajectory, E wind is the estimated energy capture of the wind energy capture system, E solar is the estimated energy capture of 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 propeller, Pwb 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] Among them, f[] represents the function symbol, R tol () represents the total longitudinal 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 current 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 is obtained by considering the influence of the pitch angle of the ocean energy driven robot, 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 pitch 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 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 coefficient 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] Where 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] A multi-source energy-saving obstacle avoidance control system for ocean-powered robots, including:

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

[0052] 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;

[0053] Trajectory cluster calculation unit: sampling within 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 from 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 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-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.

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

[0058] The multi-source energy-saving obstacle avoidance control method and system for ocean-powered robots described in this invention establish an energy system energy consumption conversion function, fully balance the states of energy capture and consumption, optimize the traditional DWA collision avoidance algorithm, and screen out danger avoidance paths with lower overall system energy consumption while ensuring navigation safety, thereby improving the endurance of the ocean-powered robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a principle flow chart of a multi-source energy-saving obstacle avoidance control method for ocean-powered robots;

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

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

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

[0063] Figure 5 This is a graph showing the relationship 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 Cluster diagram of candidate trajectories for ocean-powered robots;

[0066] Figure 8 Schematic diagram of azimuth angle evaluation of ocean energy driven robot;

[0067] Figure 9 Block diagram of a multi-source energy-saving obstacle avoidance control system for ocean-powered robots. 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 embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within 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 in the absence of 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 ocean energy-driven robots 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 includes:

[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 all-in-one machine, an integrated 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, and the action set that enables the ocean energy driven robot to avoid collision with obstacles by emergency stopping. Under the maximum acceleration constraint, the ocean energy driven robot will DWA The set of speeds that can be achieved internally.

[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] Where, 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 allowable angular velocities of the ocean energy driven robot, respectively.

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

[0079]

[0080] Where, 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 and angular velocity ωa And the ocean energy driven robot at maximum linear acceleration and 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] Where, 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 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, 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 plane; 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 taking 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 are the speed of the ocean-powered 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 flow meter 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 These 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 voltage is lower than the operating voltage, all output systems of the ocean energy-driven robot except the control system electronic components will stop working, so that energy can be stored 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 velocities 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 used to select the optimal speed and angular velocity in the candidate trajectory cluster to achieve multi-source energy-saving obstacle avoidance for the ocean energy-driven robot in the current decision cycle.

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

[0117] In a very short 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-powered robot at a certain moment is represented by p(t) and v(t). Then, the two variables of the sampling action (v, ω) in the DWA algorithm are added to obtain the state. The 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 moment, 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 velocity of the ocean current over the land.

[0123] Estimated propulsion energy consumption E of the sampled predicted trajectory thrust, Estimated energy capture E 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 respectively Tpr , the charging power P of the battery charged by the wind energy capture system wb , Solar input power P of 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] Where, σ represents the normalization coefficient;

[0127] heading(v w ω) is the azimuth evaluation function, used to evaluate the angular difference between the ocean-powered robot's heading at the end of the simulated trajectory and the target at the currently set sampling speed. A smaller angular difference indicates a smaller deviation between the ocean-powered robot's heading at the end of the trajectory and the target heading. However, the evaluation function seeks the maximum value, so the angular difference needs to be compensated. α is the azimuth evaluation coefficient; a larger α indicates a trajectory closer 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 is, the faster the selected trajectory speed is, and the faster it can approach the target point.

[0130] energy(v wω) is the energy evaluation function, which is used to evaluate the estimated energy consumption on the current trajectory. The smaller the value of this energy evaluation function, the less energy the ocean energy-driven robot consumes. λ is the energy evaluation coefficient. The larger the λ, the trajectory selected will have the least energy consumption and the most energy capture.

[0131] Because energy capture and energy consumption for ocean-powered robots vary greatly in different ocean environments, the value of the energy evaluation function can be both positive and 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 before accumulation. 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] In summary, this embodiment establishes wind energy conversion functions, solar energy conversion functions, energy input conversion functions, propeller energy consumption conversion functions, energy output conversion functions, and energy system energy consumption conversion functions, respectively, and optimizes the traditional DWA collision avoidance algorithm. While ensuring navigation safety, it screens out danger avoidance paths with lower overall energy consumption of the system, thereby improving the endurance of the ocean energy-driven robot, saving energy, and reducing the cost of using the ocean energy-driven robot.

[0137] Specific implementation method 2: refer to Figure 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 ocean environment information of the navigation area of ​​the ocean energy-driven robot in the current decision cycle, as well as the position, posture and speed information of the ocean energy-driven robot itself;

[0139] The information acquisition unit can transmit the marine environment information of the sea area where the ocean energy driven robot sails, receive task information, and process and optimize various data, and update the marine environment information regularly to provide time-varying marine environment information and current position information for the danger avoidance module. The above-mentioned marine environment information refers to the speed and direction information of sea wind and current, the light radiation information of sunlight, the wave direction, wave period and wave height information of wave, and the static obstacle information around the ocean energy driven robot without prior labeling.

[0140] The window calculation unit obtains the speed limit set, the collision avoidance action set, and the speed set that can be reached in the next decision period of the ocean energy driven robot according to the collected information, and obtains the action space window of the current decision period by taking the intersection of all sets.

[0141] The trajectory cluster calculation unit samples in the action space window of the current decision period to obtain the candidate waterline speed and angular velocity of the ocean energy driven robot in the current decision period, and obtains the candidate trajectory cluster of the ocean energy driven robot in the current decision period through the state transition equation.

[0142] The optimization unit selects the optimal speed and angular velocity in the candidate trajectory cluster by using the evaluation function, and realizes the multi-source energy-saving obstacle avoidance control of the ocean energy driven robot in the current decision period. The energy-saving avoidance action that the ocean energy driven robot should take is determined through optimization, and the candidate trajectory cluster and the current position of the ocean energy driven robot are updated regularly according to the information transmitted by the information data processing module, and the energy-saving danger avoidance path is searched according to the method. The danger avoidance module provides the latest optimal energy-saving danger avoidance action scheme according to the updated marine environment information and the real-time position of the ocean energy driven robot.

[0143] The embodiment also includes:

[0144] The environment perception module obtains the obstacle information around the current position through the laser radar and binocular vision system; the binocular vision system can realize day and night conversion function, using visible light camera in daytime and infrared imaging camera at night. The speed and direction information of sea wind and current, the light radiation information of sunlight, the wave direction, wave period and wave height information of wave are obtained through the weather station, electromagnetic current meter, wave sensor and silicon light total radiation sensor.

[0145] The positioning module obtains the position and orientation information of the ocean energy driven robot through the Beidou integrated machine and integrated navigation, and transmits the information to the information data processing module to provide time-varying position information for the danger avoidance module.

[0146] The ocean energy driven robot receives the avoidance action instruction, executes the steering mechanism, and sails according to the specified instruction.

[0147] Specific embodiment three: The multi-source energy-saving obstacle avoidance control device for the ocean energy-driven robot described in this embodiment, 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 embodiment one.

[0148] Specific embodiment 4: A computer storage medium stores at least one instruction, 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 embodiment 1.

[0149] The present invention uses sensors carried by the ocean energy-driven robot to obtain the ocean energy-driven robot's position, posture, speed information, surrounding obstacle information, sea breeze and current speed and direction information, sunlight radiation information, wave direction, wave period and wave height information. As the dynamic window approach (DWA) method cannot directly solve the problem of danger avoidance of the ocean energy-driven robot under high endurance requirements, the present invention fully considers the energy capture and consumption issues of the ocean energy-driven robot during the danger avoidance process. Under the premise of ensuring navigation safety, the present invention screens out danger avoidance paths with lower total system energy consumption, 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 illustrative of the principles and applications of the invention. It should be understood that many modifications may be made to the illustrative embodiments, and that other arrangements may be devised, without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in ways other than those described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in conjunction with other described embodiments.

Claims

1. A multi-source energy-saving obstacle avoidance control method for ocean-powered robots, characterized in that: include: Collecting ocean environment information of the ocean energy-driven robot's navigation area and its own position, posture and speed information during the current decision cycle; 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; 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 from the candidate trajectory cluster to achieve multi-source energy-saving obstacle avoidance control of the ocean energy-driven robot in the current decision cycle; The expression of the state transition equation is: ， in, For the current moment, is the time interval of the action space window, Powering robots with ocean energy The Cartesian coordinates of the moment, The heading of the ocean-powered robot relative to the ocean current, and They represent the candidate waterline velocity and angular velocity obtained by sampling the ocean energy driven robot in the set, and are respectively the waterline acceleration and angular acceleration of the ocean energy driven robot, Estimated thruster energy consumption for sampling predicted trajectories, is the estimated energy capture of the wind energy capture system, is the estimated energy captured by the solar energy capture system, For the estimated energy capture of the NSV energy system, is the predicted input power of the thruster, Charging power for batteries charged by wind energy capture system, Solar input power for ocean-powered robots, Predicting power for ocean-powered robotic energy systems; The predicted input power of the thruster It is obtained through the propeller energy consumption conversion function, and the expression of the propeller energy consumption conversion function is: ， in, Represents a function symbol, represents the total longitudinal resistance function of the ocean energy driven robot, For the sake of righteousness, is the average wave period, is the wave encounter angle, The induced velocity of the ocean current in the X-axis direction of the geodetic coordinate system measured by the electromagnetic current meter carried by the ocean energy driven robot; Charging power of batteries charged by wind energy capture system The wind energy conversion function is obtained by considering the influence of the pitch angle of the ocean energy driven robot, and the expression of the wind energy conversion function is: ， in, is the air density, is the composite wind energy utilization coefficient, is the apparent wind speed, is the angle between the apparent wind flow direction and the wind turbine rotation axis, and: ， is the pitch angle of the ocean energy driven robot, is the angle between the apparent wind flow direction and the south, is the angle between the bow of the ocean energy driven robot and the south; Solar input power for ocean-powered robots It is obtained through the solar energy conversion function, and the expression of the solar energy conversion function is: ， in, In order to consider the light energy conversion coefficient of battery charging loss, is the area of ​​the photovoltaic panel, is the solar radiation intensity on the photovoltaic panel plane of the ocean energy driven robot, and: ， ， is the solar radiation intensity at the Earth's horizontal surface, is the incident angle of sunlight onto the photovoltaic panel surface, is the solar altitude angle, is the solar azimuth; Predicted power of energy systems for ocean-powered robots It is obtained through the energy system energy consumption conversion function, and the expression of the energy system energy consumption conversion function is: ， ， ， in, is the current voltage of the battery, and are the maximum and minimum voltages of the battery, The energy input power for the ocean energy driven robot, Output power to the energy system of the ocean energy-driven robot, is the predicted output power of the servo, is the predicted output power of the environmental perception system, For the predicted output power of satellite navigation and communication systems, It is the output power of the electronic components of the control system.

2. The multi-source energy-saving obstacle avoidance control method for an ocean-powered 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 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.

3. The multi-source energy-saving obstacle avoidance control method for an ocean-powered 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: ， ， in, 、 and They 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. and They represent the candidate waterline velocity and angular velocity obtained by sampling the ocean energy driven robot in the set, and are the maximum and minimum permissible linear speeds of the ocean energy driven robot, and are the maximum and minimum permissible angular velocities of the ocean energy driven robot, represents the obstacle distance evaluation function, and are respectively the waterline acceleration and angular acceleration of the ocean energy driven robot, and are the waterline velocity and angular velocity of the ocean energy driven robot during the decision cycle, and are the maximum waterline acceleration and maximum angular acceleration of the ocean energy driven robot, for the next decision cycle.

4. The multi-source energy-saving obstacle avoidance control method for an ocean-powered 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: ， in, 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 evaluation function The expression is: ， in, is the normalization coefficient; is the azimuth evaluation coefficient, is the azimuth evaluation function; is the obstacle distance evaluation coefficient, is the obstacle distance evaluation function; is the speed evaluation coefficient, is the speed evaluation function; is the energy evaluation coefficient, is the energy evaluation function.

6. A multi-source energy-saving obstacle avoidance control system for ocean-powered robots, characterized by: The multi-source energy-saving obstacle avoidance control system is used to implement the multi-source energy-saving obstacle avoidance control method for an ocean energy-driven robot according to any one of claims 1 to 5, comprising: Information collection unit: collects the ocean environment information of the navigation area of ​​the ocean energy-driven robot in the current decision cycle, as well as the position, posture and speed information of the ocean energy-driven robot itself; 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; Trajectory cluster calculation unit: sampling within 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 from the candidate trajectory cluster to achieve multi-source energy-saving obstacle avoidance control of the ocean energy-driven robot in the current decision cycle.

7. Multi-source energy-saving obstacle avoidance control equipment for ocean energy-driven robots, characterized by: The multi-source energy-saving obstacle avoidance 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 5.

8. A computer storage medium, characterized in that The computer storage medium stores at least one instruction, which 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 claimed in any one of claims 1 to 5.

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