SUMMARYRobots are now among us and even though they compete with human beings in terms of performance and efficiency, they still fail to meet the challenge of performing a task optimally while providing strict motion safety guarantees. It is therefore necessary that the future generation of robots evolves in this direction. Generally, in robotics state-of-the-art approaches, the trajectory optimization and the motion safety issues have been addressed separately. An important contribution of this paper is to propose a motion planning method intended to simultaneously solve these two problems in a formal way. This motion planner is dubbed PassPMP-PSO. It is based on a periodic process that interleaves planning and execution for a regular update of the environment’s information. At each cycle, PassPMP-PSO computes a safe near-optimal partial trajectory using a new tree encoding technique based on particle swarm optimization (PSO). The performances of the proposed approach are firstly highlighted in simulation environments in the presence of moving objects that travel at high speed with arbitrary trajectories, while dealing with sensors field-of-view limits and occlusions. The PassPMP-PSO algorithm is tested for different tree expansions going from 13 to more than 200 nodes. The results show that for a population between 20 and 100 particles, the frequency of obtaining optimal trajectory is 100% with a rapid convergence of the algorithm to this solution. Furthermore, an experiment-based comparison demonstrates the performances of PassPMP-PSO over two other motion planning methods (the PassPMP, a previous variant of PassPMP-PSO, and the input space sampling). Finally, PassPMP-PSO algorithm is assessed through experimental tests performed on a real robotic platform using robot operating system in order to confirm simulation results and to prove its efficiency in real experiments.

中文翻译：

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基于粒子群优化树扩展的新编码技术的安全运动规划

摘要机器人现在就在我们中间，尽管它们在性能和效率方面与人类竞争，但它们仍然无法应对在提供严格运动安全保证的同时以最佳方式执行任务的挑战。因此，下一代机器人有必要朝着这个方向发展。通常，在机器人技术最先进的方法中，轨迹优化和运动安全问题已分别解决。本文的一个重要贡献是提出了一种运动规划方法，旨在以正式的方式同时解决这两个问题。这个运动规划器被称为 PassPMP-PSO。它基于一个周期性的过程，该过程将规划和执行交错，以定期更新环境信息。在每个循环中，通过PMP-PSO使用基于粒子群优化 (PSO) 的新树编码技术计算安全的接近最优部分轨迹。所提出方法的性能首先在模拟环境中突出显示，其中存在以任意轨迹高速行进的移动物体，同时处理传感器视场限制和遮挡。这通过PMP-PSO算法针对从 13 到 200 多个节点的不同树扩展进行测试。结果表明，对于 20 到 100 个粒子的种群，获得最优轨迹的频率为 100%，算法快速收敛于该解。此外，基于实验的比较证明了通过PMP-PSO超过其他两种运动规划方法（通过PMP, 的先前变体通过PMP-PSO，以及输入空间采样）。最后，通过PMP-PSO通过在使用机器人操作系统的真实机器人平台上进行的实验测试来评估算法，以确认模拟结果并证明其在实际实验中的效率。