基于改进算法融合与切换的采摘机械臂路径动态规划

Dynamic Path Planning for Picking Robot Arm Based on Improved Algorithm Fusion and Switching

摘要:

针对苹果采摘任务中因自然环境复杂造成的机械臂路径规划时间长、效率低、成功率低等问题，提出一种改进的融合切换路径动态规划算法。该算法以RRT算法为基础，引入动态阈值的目标偏置采样策略与人工势场法，通过引力场与斥力场改变新节点的生成位置，增加采样的目的性并提高收敛速度；在斥力势场系数中引入相对距离，通过与目标点之间的距离来克服目标不可达的问题；为增强算法的鲁棒性，设定阈值划分区域空间，根据当前节点所在位置动态切换至FGA-RRT(Failure-guided adaptive sampling region RRT algorithm)算法搜索，解决狭窄通道的问题，提高规划成功率；基于贪心算法对所得路径树进行优化处理，去除冗余节点，进一步缩短路径长度并优化路径平滑度，保证采摘机械臂运动的平稳性。分别对RRT算法、RRT*算法、GB-RRT算法、普通融合算法和改进的融合切换算法，在简单障碍、狭窄通道、复杂障碍以及简单三维空间等不同环境中进行仿真分析，结果表明：改进的融合切换算法在不同环境中都具有良好的适应性，规划效率高，迭代次数少，路径质量高。基于林间苹果园生长环境，搭建6自由度机械臂仿真实验室环境，进行避障采摘路径规划试验，改进的融合切换算法采摘效率比RRT算法提升74.74%，路径长度减少32.03%，采摘成功率提高8个百分点。试验结果表明本文算法在多变的苹果采摘场景中有更强的搜索能力。

关键词:采摘机械臂;路径规划;改进RRT算法;改进人工势场法;算法融合与切换

Abstract:

Aiming at the issues such as prolonged path planning time, low efficiency and poor success rate of the picking manipulator in the apple picking task as a consequence of the complex natural picking environment, an improved fusion and switching path dynamic planning algorithm was proposed. The algorithm introduced a dynamic threshold goal bias sampling strategy and artificial potential field to alter the generation position of new nodes, increasing the purposiveness of sampling and improving convergence speed. A relative distance was incorporated into the repulsive potential field coefficient to overcome the problem of unreachable targets by considering the distance to the goal. To enhance the algorithm’s robustness, a threshold was set to partition the spatial region, dynamically switching to the failure-guided adaptive sampling region RRT algorithm (FGA-RRT) based on the current node expansion state to address narrow passage issues and increase planning success rates. The greedy algorithm was utilized to optimize the resulting path tree, removing redundant nodes, further shortening the path length, and optimizing path smoothness to ensure the stable movement of the picking robot arm. Simulation experiments were conducted for the RRT algorithm, RRT* algorithm, GB-RRT algorithm, common fusion algorithm and the improved fusion and switching algorithm respectively in simple obstacles, narrow channels, complex obstacles and simple three-dimensional spaces. The results showed that the improved fusion and switching algorithm had good adaptability in different environments, with high planning efficiency, few iterations and high path quality. Based on the established 6-DOF robot arm motion planning simulation environment and laboratory environment, obstacle avoidance picking tests were conducted. The improved hybrid switching algorithm’s picking efficiency was increased by 74.74%, path length was decreased by 32.03%, and picking success rate was improved by 8 percentage points compared with that of the RRT algorithm. The experimental results demonstrated that the proposed algorithm had stronger search capabilities in apple-picking scenarios, providing a reference for improving the operational efficiency of picking robot arms.

Key words:picking robot arm;path planning;improved RRT algorithm;improved artificial potential field method;algorithm fusion and switching