CN-121540156-B - Three-dimensional path planning method for unmanned aerial vehicle wireless charging

Abstract

The invention provides a three-dimensional path planning method for unmanned aerial vehicle wireless charging, and belongs to the technical field of unmanned aerial vehicle control. The method comprises the steps of S1, constructing a system model, S2, modeling a problem, establishing a residual energy maximization problem, S3, optimizing the problem, and adjusting a sampling strategy according to the current energy state of the unmanned aerial vehicle, namely, performing path planning by applying an energy perception EA-RRT algorithm. According to the hybrid charging system, the hybrid charging system is built by combining SWIPT wireless energy transmission with the fixed charging station, so that the endurance of the unmanned aerial vehicle in a complex urban environment is remarkably improved. The proposed energy sensing EA-RRT algorithm can dynamically plan a path, preferentially guide a charging area when the electric quantity is insufficient, and guide a target when the electric quantity is sufficient Gao Xiaofei, so that energy autonomous management is realized. Meanwhile, the algorithm gives consideration to the turning angle, obstacle avoidance and task coverage efficiency, and ensures flight safety and task quality.

Inventors

• WEI QING
• LI JINGYI
• BAI WENLE
• Dang Xiangwei
• LI YUNDONG

Assignees

• 北方工业大学

Dates

Publication Date

20260508

Application Date

20251126

Claims (2)

1. 1. The three-dimensional path planning method for unmanned aerial vehicle wireless charging is characterized by comprising the following steps of: S1, constructing a system model The UAV navigates through an environment involving building obstacles and a plurality of wireless charging stations, communicates with the base station BS while harvesting energy during the communication using SWIPT technology; through the application of the hybrid wireless energy charging model, the unmanned aerial vehicle is in a time slot The total energy collected during the period is Remaining energy of UAV in time slot Is updated as: , Wherein, the And Is the remaining energy of the drone in two consecutive time slots, Is the energy consumed by the unmanned aerial vehicle at time slot t; The construction of the system model is specifically as follows: The location of the base station is Acquiring energy through wireless power transfer when the UAV is located near the wireless charging station WCS; the collection of wireless charging station WCSs is referred to as ; The total mission duration is divided into T time slots, each time slot duration In time slot Defining the position coordinates of the unmanned aerial vehicle as The continuous three track points of the unmanned aerial vehicle are: , the horizontal projection vectors are respectively as follows: , maximum turning angle constraint in horizontal plane The method comprises the following steps: , Wherein the method comprises the steps of Is the maximum rotation angle; Enabling track section Length of (2) The requirements are as follows: , Wherein the method comprises the steps of Representing the shortest distance; Defining a scout efficiency metric The method comprises the following steps: , Wherein the method comprises the steps of Is the power received by the unmanned aerial vehicle from the base station at time slot t, Is the energy collected by the unmanned aerial vehicle in the current time slot; The constraints of preferential coverage are: , Wherein, the And The initial scout efficiency and the minimum scout efficiency, Is at the time of Standard deviation of signal intensity, and Is the gain factor; Base station to drone channel power gain Expressed as: , Wherein, the Is the channel power gain at a reference distance of 1 meter; when the unmanned aerial vehicle communicates with the base station, the unmanned aerial vehicle is in a time slot The power received from the base station is: , Wherein the method comprises the steps of Representing the transmission power of the base station; with a nonlinear energy harvesting circuit, the harvested energy is represented as: , Wherein, the Is the upper limit value for the saturation of the energy harvesting circuit, And Respectively representing a circuit sensitivity parameter and a circuit offset parameter; In each time slot there is The proportional time is used for the transmission of information, Time is used for energy collection, then the energy collected by the drone in a certain time slot is noted as: , wherein, the information transmission time length needs to satisfy ; The speed of the unmanned aerial vehicle is V, and the flight power consumption is expressed as: , Wherein, the Are constant parameters and are related to the weight, wing area and air density factors of the unmanned aerial vehicle; The transmitting power of the unmanned aerial vehicle is In two periods of Between successive time slots of the unmanned plane, the flight energy consumption and the transmission energy consumption are respectively The hybrid energy management architecture is employed to integrate dynamic revenue energy harvesting from fixed WCS and swit technologies, charged energy when the drone hovers over a wireless charging station: , Wherein, the Is a binary variable; indicating that the drone is charged from WCS k, otherwise ; For the charging efficiency of the WCS, For the transmit power of the WCS, The proportion of time slots spent by the unmanned aerial vehicle at the charging station; through the application of the hybrid wireless energy charging model, the unmanned aerial vehicle is in a time slot The total energy collected during the period is Remaining energy of UAV in time slot Is updated as: , Wherein, the Is the remaining energy of the drone in two consecutive time slots, Is the energy consumed by the unmanned aerial vehicle at time slot t; S2, problem modeling Establishing a residual energy maximization problem model; s3, problem optimization Adjusting a sampling strategy according to the current energy state of the unmanned aerial vehicle, namely applying an energy perception EA-RRT algorithm to carry out path planning; The core objective function is: , Wherein, the Weights respectively representing energy harvesting, target guiding, steering angle constraint and task coverage efficiency, path cost is determined by the distance from the current node to the target, constraint penalty terms are used for normalizing the turning angle overrun behavior, The energy benefit is integrated into a node selection strategy, and a bimodal energy gain function is defined: , Energy return function Is composed of charging station benefits and SWIPT benefits, the charging station benefits are composed of indication functions Combining charging efficiency, transmitting power and time, wherein SWIPT gain is determined by an energy collection model, and channel gain from a base station to an unmanned aerial vehicle and base station transmitting power; time allocation factor Is a dynamic parameter based on the real-time energy state self-adaptive adjustment of the UAV, and is defined as follows: , Wherein, the Ensuring that the drone always reserves a minimum portion of time for energy harvesting while maintaining sufficient time for communication tasks; Combining a probability model adapting to the residual energy of the unmanned aerial vehicle: , wherein, the Indicating the nearest charging station Is a central Gaussian distribution, favors energy replenishment regions during low energy states, and Maintaining global exploration at sufficient energy; For the current position node Generating new nodes via bootstrap rules : , Wherein the method comprises the steps of The step size is dynamically adjusted based on the local obstacle density, Is a randomly generated node; The best location node is selected as: , At the same time, to bypass obstacles in the 3D urban environment, no collision is selected The remaining energy is then calculated.
2. 2. The three-dimensional path planning method for unmanned aerial vehicle wireless charging according to claim 1, wherein S2, the establishment of the residual energy maximization problem model is as follows: constraint C3 requires that the length of the UAV's motion trajectory in each slot should be at a minimum length And maximum length Between, constraint C4 indicates that the UAV should reach its destination Constraint C5 is that UAVs are connected to at most one WCS per slot, C6 represents Is a binary variable, C7 represents that the residual energy of the unmanned aerial vehicle is greater than or equal to the minimum energy And is not greater than the maximum energy C8 represents a time allocation factor.

Description

Three-dimensional path planning method for unmanned aerial vehicle wireless charging Technical Field The invention provides a three-dimensional path planning method for unmanned aerial vehicle wireless charging, and belongs to the technical field of unmanned aerial vehicle control. Background In recent years, unmanned aerial vehicle technology development is rapid, and by virtue of the characteristics of strong maneuverability and convenient deployment, unmanned aerial vehicle technology is widely applied in the fields of logistics, monitoring, communication and the like. However, the unmanned aerial vehicle is insufficient in terms of dynamic obstacle avoidance capability and limited in endurance capability, so that the continuous operation capability of the unmanned aerial vehicle in a complex scene is restricted. In order to improve the dynamic obstacle avoidance performance of the unmanned aerial vehicle, various improved path planning algorithms, such as a fast search random tree algorithm, an ant colony algorithm and the like, are proposed, and performances of various methods in terms of path quality and planning efficiency are explored by comparing the performances of the algorithms such as EA-RRT, astar and ACO in a three-dimensional multi-obstacle environment. Nevertheless, these approaches focus mainly on the enhancement of path planning capability and do not effectively solve the energy consumption problem of the unmanned aerial vehicle. With the introduction of wireless energy-carrying communication (SWIPT) technology, unmanned aerial vehicles have new potential in parallel processing of energy collection and information transmission. Related researches improve the energy efficiency level of the system by optimizing SWIPT power distribution and unmanned aerial vehicle track or combining non-orthogonal multiple access technology, intelligent reflecting surface and other means. However, energy collected by means of SWIPT technology alone is still difficult to meet the continuous operational demands of unmanned aerial vehicles. Disclosure of Invention The invention provides a three-dimensional path planning method for unmanned aerial vehicle wireless charging, which mainly adopts an energy perception type EA-RRT algorithm to realize the fusion of SWIPT technology and a fixed wireless charging station, thereby improving the stability of energy supply. By constructing an optimization model which aims at maximizing the residual energy of the unmanned aerial vehicle. A three-dimensional path planning method for unmanned aerial vehicle wireless charging comprises the following steps: S1, constructing a system model The system model comprises a wireless charging model and a track model. In a three-dimensional (3D) urban environment, UAVs navigate through tasks involving building obstructions and many wireless charging stations. The UAV communicates with a Base Station (BS) while harvesting energy during such interactions using swit technology. The location of the base station is. When the UAV is located near a Wireless Charging Station (WCS), energy is harvested by wireless power transfer. The collection of Wireless Charging Stations (WCS) is referred to as. The total mission duration is divided into T time slots, each time slot duration. At time slot t, defining the position coordinates of the unmanned aerial vehicle asThe continuous three track points of the unmanned aerial vehicle are: the horizontal projection vectors are respectively as follows: maximum turning angle constraint in horizontal plane The method comprises the following steps: Wherein the method comprises the steps of Is the maximum rotation angle; Enabling track section Length of (2)The requirements are as follows: Wherein the method comprises the steps of Representing the shortest distance; Defining a scout efficiency metric The method comprises the following steps: Wherein the method comprises the steps of Is the power received by the unmanned aerial vehicle from the base station at time slot t,Is the energy collected by the unmanned aerial vehicle in the current time slot; The constraints of preferential coverage are: Wherein, the AndThe initial scout efficiency and the minimum scout efficiency,Is at the time ofStandard deviation of signal intensity, andIs the gain factor; Base station to drone channel power gain Expressed as: Wherein, the Is the channel power gain at a reference distance of 1 meter; when the unmanned aerial vehicle communicates with the base station, the unmanned aerial vehicle is in a time slot The power received from the base station is: Wherein the method comprises the steps of Representing the transmission power of the base station; with a nonlinear energy harvesting circuit, the harvested energy is represented as: Wherein, the Is the upper limit value for the saturation of the energy harvesting circuit,AndRespectively representing a circuit sensitivity parameter and a circuit offset parameter; In each time s