| 凸优化与A*算法结合的路径避障算法 |
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| 引用本文： | 陈光荣,郭盛,王军政,曲海波,陈亚琼,侯博文.凸优化与A*算法结合的路径避障算法[J].控制与决策,2020,35(12):2907-2914. |
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| 作者姓名： | 陈光荣 郭盛 王军政 曲海波 陈亚琼 侯博文 |
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| 作者单位： | 北京交通大学机械与电子控制工程学院,北京100044;北京理工大学自动化学院,北京100081;北京市轨道交通线路安全与防灾工程技术研究中心,北京100044 |
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| 基金项目： | 中央高校基本科研业务费专项资金项目(2019JBM051)；北京市轨道交通线路安全与防灾工程技术研究中心开放研究基金项目(RRC201701)；国家自然科学基金项目(51875033). |
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| 摘 要： | 为提高足式移动机器人的避障能力和路径规划效率,提出一种凸优化与A*算法结合的路径避障算法.首先,基于半定规划的迭代区域膨胀方法IRI-SDP(iterative regional inflation by semi-definite programming),通过交替使用两种凸优化算法快速计算出地面环境中无障碍凸多边形及其最大面积内切椭圆,用于移动机器人的局部避障和任务动作规划;然后,结合经典的A*算法,建立机器人局部和世界坐标系、机器人质心轨迹转换模型、碰撞模型和启发式代价函数,在全局环境中寻找最优成本最小的路径;最后,通过仿真实验验证该算法的有效性.
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| 关 键 词： | 凸优化 A*算法 路径规划 避障 足式移动机器人 无障碍空间 |
Convex optimization and A-star algorithm combined path planning and obstacle avoidance algorithm |
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| CHEN Guang-rong,GUO Sheng,WANG Jun-zheng,QU Hai-bo,CHEN Ya-qiong,HOU Bo-wen.Convex optimization and A-star algorithm combined path planning and obstacle avoidance algorithm[J].Control and Decision,2020,35(12):2907-2914. |
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| Authors: | CHEN Guang-rong GUO Sheng WANG Jun-zheng QU Hai-bo CHEN Ya-qiong HOU Bo-wen |
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| Affiliation: | School of Mechanical,Electronic and Control Engineering,Beijing Jiaotong University,Beijing100044,China;School of Automation,Beijing Institute of Technology,Beijing100081,China; Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention,Beijing100044,China |
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| Abstract: | To improve the obstacle avoidance ability and path planning efficiency of mobile legged robots, a convex optimization and A-star algorithm combined path planning and obstacle avoidance algorithm is proposed. Firstly, a method of iterative regional inflation by semi-definite programming(IRI-SDP) is presented to quickly compute out a large convex polygon of obstacle-free and its largest inscribed ellipse in the given ground environment through alternating two convex optimizations. The obstacle-free region is utilized for obstacle avoidance and task motion planning locally. Then, combining with the classical A-star algorithm via establishing the local and world coordinate system of mobile robots, the transfer model of the mass center of mobile robots, the impact model and the heuristics cost function, the optimal minimum-cost path in the global environment can be found. Finally, simulation results validate the effectiveness of proposed method. |
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