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
MEI Yilin, CUI Likun, HU Xueyan, HU Guangqi, WANG Hao. Obstacle avoidance and formation control of multiple unmanned vehicles in complex environments based on artificial potential field method[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2024.05.05.002
Citation: MEI Yilin, CUI Likun, HU Xueyan, HU Guangqi, WANG Hao. Obstacle avoidance and formation control of multiple unmanned vehicles in complex environments 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 environments 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. 修改引力势场函数使引力大小在距离较大或较小时收敛于某一值,解决前期引力过大引起的无人车与障碍物碰撞以及目标点不可达问题;采用更平滑的斥力计算公式对斥力势场函数进行优化,解决无人车距离障碍物过近时斥力过大引起的无人车在障碍物附近徘徊的问题;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,实验结果表明: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;相较于传统算法与文献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: Addressing the increasing complexity of tasks, single unmanned vehicles have become unable to meet actual operational requirements, prompting a shift toward multivehicle formation systems. However, in complex environments, issues such as high collision rates and unstable formations in multivehicle obstacle avoidance and formation control persist. A review of existing literature reveals that most research focuses on static obstacle environments, which do not accurately reflect real-world conditions. To tackle the issues of collision with obstacles and formation instability in dynamic and dense environments, a multivehicle obstacle avoidance and formation control method based on the potential field method was proposed. The attraction potential field function was modified to stabilize the attraction force at certain distances, addressing problems like vehicle–obstacle collisions and target point inaccessibility owing to excessive gravity in the early stage. A smoother repulsive was implemented to optimize the repulsive potential field function, preventing unmanned vehicles from lingering near obstacles caused by excessive repulsive force when too close to the obstacles. The A stability force was defined to maintain stable formations during movement, allowing vehicles to break free from local minima under its influence. The method also incorporated the velocity repulsive potential field for dynamic obstacles and an attraction potential field for sparse obstacles, enhancing the success rate of obstacle avoidance and path planning in complex environments. Compared to traditional artificial potential field methods and the improved algorithms, the simulation results show that the proposed method effectively maintains formation stability and exhibits high anti-interference capabilities in complex environments. Specifically, the success rate of obstacle avoidance in dynamic environments increased by 35% compared to traditional algorithms and by 10% compared to improved algorithms. In dense, dynamic obstacle environments, the success rate increased by 55% and 10%, respectively. The proposed method provides a reference method for multivehicle formation and obstacle avoidance in complex environments.

     

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