Path planning of coal mine underground robot based on improved artificial potential field algorithm
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摘要: 路径规划是煤矿机器人在煤矿井下狭小巷道空间中应用亟待解决的关键技术之一。针对传统人工势场（APF）算法在狭小巷道环境中规划出的路径可能离巷道边界过近，以及在障碍物附近易出现目标不可达和路径振荡等问题，提出了一种基于改进APF算法的煤矿机器人路径规划方法。参考《煤矿安全规程》有关规定建立了巷道两帮边界势场，将机器人行驶路径尽量规划在巷道中间，以提高机器人行驶安全性；在障碍物斥力势场中引入调节因子，以解决目标不可达问题；引入转角限制系数以平滑规划出的路径，减少振荡，提高规划效率，保证规划路径的安全性。仿真结果表明：当目标点离障碍物很近时，改进APF算法可成功规划出能够抵达目标点的路径；改进APF算法规划周期数较传统算法平均减少了14.48%，转向角度变化累计值平均减少了87.41%，曲率绝对值之和平均减少了78.09%，表明改进APF算法规划的路径更加平滑，路径长度更短，规划效率和安全性更高。Abstract: Path planning is one of the key technologies that urgently need to be solved in the application of coal mine robots in narrow underground roadways. A path planning method for coal mine robots based on improved APF algorithm is proposed to address the issues of traditional artificial potential field (APF) algorithms that planning paths in narrow roadway environments may be too close to the roadway boundary, as well as the possibility of unreachable targets and path oscillations near obstacles. Referring to the relevant provisions of the Coal Mine Safety Regulations, the boundary potential field between the two sides of the roadway is established. The robot's path is planned as much as possible in the middle of the roadway to improve the safety of robot travel. The method introduces regulatory factors into the repulsive potential field of obstacles to solve the problem of unreachable targets. The method introduces corner constraint coefficients to smooth the planned path, reduce oscillations, improve planning efficiency, and ensure the safety of the planned path. The simulation results show that when the target point is very close to the obstacle, the improved APF algorithm can successfully plan a path that can reach the target point. The improved APF algorithm reduces the planning cycle by an average of 14.48% compared to traditional algorithms. The cumulative value of steering angle reduces by an average of 87.41%, and the sum of absolute curvature values is reduced by an average of 78.09%. The results indicate that the improved APF algorithm plans smoother paths, shorter path lengths, and has higher planning efficiency and safety.
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图 5 基于改进APF算法的煤矿井下机器人路径规划流程
Figure 5. Path planning flow of coal mine underground robots based on improved artificial potential field algorithm
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图 7 斥力势场修正前后路径规划仿真结果
Figure 7. Simulation results of path planning before and after repulsive potential field correction
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图 8 引入转角限制系数后仿真结果
Figure 8. Simulation results after introducing corner restriction coefficient
表 1 引入转角限制系数前后路径规划性能对比
Table 1 Performance comparison of path planning before and after introducing corner constraint coefficients
评价指标 算法改进前 算法改进后 路径长度/m 27.42 23.83 规划周期数 190 165 转向角度变化累计值/rad 17.45 6.88 曲率绝对值之和/m−1 73.06 12.39 
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表 2 多个障碍物情况下改进前后路径规划仿真结果
Table 2 Simulation results of path planning before and after improvement under multiple obstacles
评价指标 1个障碍物 2个障碍物 3个障碍物 改进前 改进后 改进前 改进后 改进前 改进后 路径长度/m 19.48 16.83 28.25 23.82 24.26 20.83 规划周期数 135 117 196 165 168 144 转向角度变化累计值/rad 29.65 3.87 43.36 5.25 38.31 4.45 曲率绝对值之和/m−1 135.39 36.60 265.09 49.58 215.93 43.08
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