交互式水域环卫机器人的避障路径规划算法

Obstacle Avoidance Path Planning Algorithm for Interactive Water Sanitation Vehicle

为使交互式水域环卫机器人(Interactive Water Sanitation Vehicle,IWSV)在进行垃圾收集时成功捕获水中浮动垃圾并顺利规避水域障碍物,提出一种将基于采样的快速搜索随机树(Rapidly-exploring Random Tree,RRT)算法与速度障碍模型相结合的路径规划算法。利用双目摄像头基于视差定位法获取水域动态障碍物的位置坐标,利用IWSV搭载的感应元件获取其自身与障碍物的相对方位角,基于速度障碍法计算可成功避开障碍物的移动角度调整范围,对更优的RRT算法中的随机采样过程进行进一步优化,得到改进的避障路径规划算法。考虑实际应用场景,引入抗积分饱和比例积分微分控制(Proportional Integral Differentiational Control,PID Control)法使航向控制器的控制效果更为精准有效。在实景测试时避障路径规划算法存在稳健性,基于到达时间(Time of Arrival,TOA)定位法进行仿真分析。仿真试验结果表明,该路径规划算法比RRT算法和改进前的RRT算法路径规划效果更优,可靠性更好,可在较短时间内避障并得到较优移动路径。在实景测试时基于TOA的Chan算法更加符合定位估计需求,且IWSV本体感应装置的噪声测算宜在10 m以内。

In order to enable the Interactive Water Sanitation Vehicle(IWSV)to capture the floating garbage and avoid the obstacles in the water smoothly during the garbage collection,a path planning algorithm combined Rapidly-exploring Random Tree(RRT)algorithm based on the sampling with the speed obstacle model is proposed.The position coordinates of dynamic obstacles in the water are obtained based on the parallax positioning method with the binocular camera,the relative azimuth between the sensing element carried by IWSV and obstacles is obtained,the adjustable range of movement angle to avoid the obstacles successfully is calculated based on the speed obstacle method,the random sampling process in the better RRT algorithm is further optimized,and an improved obstacle avoidance path planning algorithm is obtained.In view of the practical application scenes,the anti-integral saturation Proportional Integral Differential Control(PID Control)method is introduced to make the control effect of course controller more precise and effective.In the real scene test,the obstacle avoidance path planning algorithm is of the robustness,and the simulation analysis is conducted based on Time of Arrival(TOA)positioning method.The simulation results show that the proposed path planning algorithm is of better effect and reliability than the RRT algorithm and the improved RRT algorithm,and it can avoid the obstacles in a short time and get a better moving path.In the real scene test,the Chan algorithm based on TOA is more accordant with the positioning estimation requirements,and the noise measurement of sensing device of IWSV itself should be within 10 m.