梅艺林, 崔立堃, 胡雪岩, 胡广琦, 王浩. 基于人工势场法的复杂环境下多无人车避障与编队控制[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2024.05.05.002
引用本文: 梅艺林, 崔立堃, 胡雪岩, 胡广琦, 王浩. 基于人工势场法的复杂环境下多无人车避障与编队控制[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2024.05.05.002
Obstacle avoidance and formation control of multiple unmanned vehicles in complex environment based on artificial potential field method[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2024.05.05.002
Citation: Obstacle avoidance and formation control of multiple unmanned vehicles in complex environment based on artificial potential field method[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2024.05.05.002

基于人工势场法的复杂环境下多无人车避障与编队控制

Obstacle avoidance and formation control of multiple unmanned vehicles in complex environment based on artificial potential field method

  • 摘要: 针对动态、密集障碍物等复杂环境下多车避障与编队控制存在的容易与障碍物碰撞、编队不稳定等问题,提出一种基于势场法的多车避障与编队控制方法。对传统人工势场法的引力与斥力势场函数进行修改,解决目标点不可达问题。通过定义编队稳定力使编队前进过程中保持稳定队形的同时解决传统人工势场法存在的局部极小值问题。引入动态障碍物速度斥力势场与障碍物数量稀疏区域引力势场使编队在复杂环境下具有更高的避障与路径规划成功率。通过仿真实验与传统人工势场法以及改进后的算法进行对比,试验结果表明:本文方法在复杂环境下能够维持编队稳定性,具有较高的抗干扰能力;相较于传统算法与改进算法在动态障碍物环境下避障成功率分别提高了35%与10%,在密集动态障碍物环境下分别提高了55%与10%;能够在密集动态障碍物环境下躲避障碍物规划出合理的路径。

     

    Abstract: With the increase of task complexity, a single unmanned vehicle has gradually been unable to meet the actual requirements,The multi-vehicle formation system is gradually coming into people's vision. However, in complex environments, issues such as high collision rates and unstable formations in multi-vehicle obstacle avoidance and formation control still need to be addressed. Through the literature review study, it is found that most scholars focus on obstacle avoidance and multi-vehicle formation control in static obstacle environment, which is not consistent with the actual situation. Therefore, aiming at the problems of collision with obstacles and formation instability of multi-vehicle obstacle avoidance and formation control in complex environments such as dynamic and dense obstacles, a multi-vehicle obstacle avoidance and formation control method based on potential field method was proposed. The gravitational potential field function was modified to make the gravitational force converge to a certain value when the distance was large or small, so as to solve the problems of collision between unmanned vehicle and obstacles and inaccessibility of target point caused by excessive gravity in the early stage. A smoother repulsive force calculation formula was used to optimize the repulsive potential field function to solve the problem that the unmanned vehicle lingered near the obstacle caused by excessive repulsive force when the unmanned vehicle was too close to the obstacle. The formation stability force is defined, so that the formation can maintain a stable formation in the process of advancing and the unmanned vehicle can successfully break away under the action of the force when it falls into a local minimum. The velocity repulsive potential field of dynamic obstacles and the gravitational potential field of the sparse number of obstacles were introduced to make the formation have a higher success rate of obstacle avoidance and path planning in complex environments. Compared with the traditional artificial potential field method and the improved algorithm, the simulation results show that the proposed method can maintain the formation stability and has high anti-interference ability in complex environments. Compared with the traditional algorithm and the improved algorithm, the success rate of obstacle avoidance in dynamic obstacle environment is increased by 35% and 10% respectively. 55% and 10% respectively in dense dynamic obstacle environment, which can avoid obstacles and plan a reasonable path in dense dynamic obstacle environment. The proposed method provides a reference method for multi-vehicle formation and obstacle avoidance in complex environments.

     

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