Abstract
Robotic applications across industries demand advanced navigation for safe and smooth movement. Smooth path planning is crucial for mobile robots to ensure stable and efficient navigation, as it minimizes jerky movements and enhances overall performance Achieving this requires smooth collision-free paths. Partial Swarm Optimization (PSO) and Potential Field (PF) are notable path-planning techniques, however, they may struggle to produce smooth paths due to their inherent algorithms, potentially leading to suboptimal robot motion and increased energy consumption. In addition, while PSO efficiently explores solution spaces, it generates long paths and has limited global search. On the contrary, PF methods offer concise paths but struggle with distant targets or obstacles. To address this, we propose Smoothed Partial Swarm Optimization with Improved Potential Field (SPSO-IPF), combining both approaches and it is capable of generating a smooth and safe path. Our research demonstrates SPSO-IPF's superiority, proving its effectiveness in static and dynamic environments compared to a mere PSO or a mere PF approach.
Abstract (translated)
机器人应用在多个行业中需要先进的导航来实现安全和平稳的运动。平滑路径规划对于移动机器人来说至关重要，因为它可以最小化剧烈运动并提高整体性能。要实现这一点，需要平滑的冲突free路径。部分聚类优化（PSO）和势场（PF）是著名的路径规划技术，然而，由于其固有算法，它们可能无法产生平滑的路径，从而导致机器人运动 suboptimal 和能源消耗增加。此外，尽管PSO有效地探索解决方案空间，但它生成长路径，全局搜索有限。相反，PF方法提供简洁的路径，但与远距离目标或障碍物 struggle。为了应对这个问题，我们提出了平滑部分聚类优化和改进势场（SPSO-IPF）的方法，结合两种方法，它能够生成平滑和安全路径。我们的研究证明了SPSO-IPF的优越性，证明了与仅仅使用PSO或仅仅使用PF方法相比，其在静态和动态环境中的有效性。
URL
https://arxiv.org/abs/2405.01794
