目的 针对码垛机器人在运动过程中存在的避障路径规划与运动效率不足的问题,开展码垛机器人避障路径规划与运动轨迹优化研究。方法 首先,采用五次B样条曲线构建码垛机器人运动轨迹,并提出一种两阶段RRT算法进行避障路径规划。该算法在探索阶段引入启发式采样策略和优先队列策略,以提高搜索效率;在优化阶段采用即时传播成本更新机制,以加速最优路径的收敛。在所得避障路径的基础上,以时间最优为目标,提出一种多策略改进粒子群算法,该算法融合了多模态自适应参数调整机制、量子行为增强策略和分层精英学习策略,对码垛机器人运动轨迹进行优化。结果 实验结果表明,两阶段RRT算法可使码垛机器人在无碰撞条件下从起点运动至目标点,其搜索效率和路径长度均优于传统RRT及其改进算法;经多策略改进粒子群算法优化后,码垛机器人运动时间为37.42 s,相比于自适应权重粒子群算法与标准粒子群算法,运动效率分别提高了23.29%和26.41%。结论 两阶段RRT*算法与多策略改进粒子群算法的协同作用,为码垛机器人规划出一条合理可行的作业路径,在确保避障安全的同时显著缩短了运动时间。
Abstract
The work aims to optimize both obstacle avoidance path planning and motion trajectory of palletizing robots so as to address the issues of insufficient obstacle avoidance path planning and motion efficiency in palletizing robots during operation. Firstly, the motion trajectory of a palletizing robot was constructed using quintic B-spline curves. A two-stage RRT* algorithm was proposed for obstacle avoidance path planning. In the exploration phase, a heuristic sampling strategy and a priority queue strategy were introduced to enhance search efficiency. In the optimization phase, an anytime cost updating mechanism was adopted to accelerate the convergence to the optimal path. Based on the obtained collision-free path and with the goal of time optimality, a multi-strategy improved particle swarm optimization (PSO) algorithm was proposed to optimize the motion trajectory. This enhanced algorithm integrated a multimodal adaptive parameter adjustment mechanism, a quantum-behaved enhancement strategy, and a hierarchical elite learning strategy to optimize the motion trajectory of the palletizing robot. Experimental results demonstrated that the two-stage RRT* algorithm successfully guided the palletizing robot from the start point to the target point without collision, outperforming both the traditional RRT and an improved RRT algorithm in terms of search efficiency and path length. After optimization by the multi-strategy improved PSO algorithm, the total motion time of the palletizing robot was reduced to 37.42 s. Compared with the adaptive weight PSO and the standard PSO algorithms, the motion efficiency was improved by 23.29% and 26.41%, respectively. In conclusion, the synergistic application of the two-stage RRT* algorithm and the multi-strategy improved PSO algorithm generates a feasible and efficient operational path for the palletizing robot, ensuring obstacle avoidance safety while significantly shortening the motion time.
关键词
码垛机器人 /
避障路径优化 /
轨迹优化 /
粒子群算法 /
RRT*
Key words
palletizing robot /
obstacle avoidance path optimization /
trajectory optimization /
particle swarm algorithm /
RRT*
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