Path planning of coal mine foot robot by integrating improved A* algorithm and dynamic window approach
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摘要: 为提高煤矿足式机器人路径规划算法的运行效率、搜索精度及避障灵活性，提出了一种融合改进A*算法与动态窗口法（DWA）的煤矿足式机器人路径规划方法。首先对A*算法进行改进，通过去冗余节点策略减短规划路径的长度，通过改进邻域搜索方式和代价函数提高路径规划速度，采用分段二阶贝塞尔曲线进行路径平滑。将改进A*算法规划出的路径节点依次作为局部路径规划DWA的局部目标点进行算法融合，筛选邻近的障碍物节点，从而再次缩短路径长度，并通过调整DWA代价函数中的权值比例提升避障性能。针对机器人遇到无法避开的障碍物而陷入“假死”状态的问题，以当前初始点为起点，重新调用融合算法，即重新进行全局路径规划，将得到的新节点代替原有的局部目标点，按照新路径进行后续工作。仿真结果表明：在保证机器人行走安全稳定的基础上，改进A*算法较传统A*算法的计算时间缩短了65%，路径长度缩短了24.1%，路径节点数量减少了27.65%，最终得出的路径更为平滑；融合算法进一步提升了全局路径规划能力，在多障碍物环境下能够绕开新增的动态和静态障碍物；机器人遇到“L”型障碍物进入“假死”状态时，在“假死”位置重新进行全局路径规划，更新行走路径，成功到达了最终目标点。基于融合算法的JetHexa六足机器人路径规划实验结果验证了融合算法的有效性和优越性。Abstract: In order to improve the operational efficiency, search precision, and obstacle avoidance flexibility of the path planning algorithm for coal mine foot robot, a path planning method for coal mine foot robots is proposed, which integrates the improved A* algorithm and the dynamic window approach (DWA). Firstly, the A* algorithm is improved by reducing the length of the planned path through a redundant node removal strategy. The method improves the neighborhood search method and cost function to increase the speed of path planning, and uses segmented second-order Bessel curves for path smoothing. The path nodes planned by the improved A* algorithm are sequentially used as local target points for local path planning DWA for algorithmic fusion. The method filters neighboring obstacle nodes to shorten the path length again, and improves obstacle avoidance performance by adjusting the weight ratio in the DWA cost function. In response to the problem of robots falling into a "feigned death" state when encountering unavoidable obstacles, the method starts from the current initial point, the fusion algorithm is called up again. The global path planning is carried out again, and the new nodes obtained replace the original local target points, and the subsequent work is carried out according to the new route. The simulation results show that, while ensuring the safety and stability of robot walking, the improved A* algorithm reduces the calculation time by 65%, the path length by 24.1%, and the number of path nodes by 27.65% compared to the traditional A* algorithm, resulting in a smoother path. The fusion algorithm further enhances the global path planning capability, enabling it to bypass newly added dynamic and static obstacles in multi obstacle environments. When the robot encounters an L-shaped obstacle and enters a "feigned death" state, it reconducts global path planning at the "feigned death" position, updates its walking path, and successfully reaches the final target point. The experimental results of path planning for JetHexa hexapod robot based on fusion algorithm have verified the effectiveness and superiority of the fusion algorithm.
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图 8 融合算法避障性能验证
Figure 8. Verification of obstacle avoidance performance of fusion algorithm
图 9 单一路径规划算法避障性能
Figure 9. Obstacle avoidance performance of single path planning algorithm
图 11 “假死”应对方案路径规划结果
Figure 11. Path planning results of the response plan for "feigned death"
图 14 算法改进前后路径规划结果
Figure 14. Path planning results before and after algorithm improvement
图 15 融合算法避障性能验证
Figure 15. Verification of obstacle avoidance performance of fusion algorithm
表 1 邻域删除策略
Table 1. Neighborhood deletion strategy
角度区间 删除邻域 −22.5°≤θ＜22.5° 出发点左侧邻域 22.5°≤θ＜67.5° 出发点左下侧邻域 67.5°≤θ＜112.5° 出发点下侧邻域 112.5°≤θ＜157.5° 出发点右下侧邻域 −180°≤θ＜−157.5° 或 157.5°≤θ＜180° 出发点右侧邻域 −157.5°≤θ＜−112.5° 出发点右上侧邻域 −112.5°≤θ＜−67.5° 出发点上侧邻域 −67.5°≤θ＜−22.5° 出发点左上侧邻域 
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表 2 A*算法改进前后性能对比
Table 2. Comparison of performance before and after A* algorithm improvement
算法 规划时间/s 规划路径长度/cm 路径节点数/个 传统A*算法 1.20 55.6 47 应用去冗余节点 1.19 38.8 31 改进A*算法 0.42 42.2 34
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表 3 算法改进前后性能对比
Table 3. Comparison of performance before and after algorithm improvement
算法 规划时间/s 路径长度/cm A*算法+DWA 1.97 424 改进A*算法+DWA 1.10 368
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