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.

翻译：工业机器人应用要求具备先进导航能力以确保安全平稳运动。光滑路径规划对移动机器人至关重要，因其能最小化抖动行为并提升整体性能，是实现稳定高效导航的关键。实现此目标需要生成光滑无碰撞路径。粒子群优化（PSO）和势场法（PF）是显著的路径规划技术，然而受限于其固有算法，它们在生成光滑路径方面存在困难，可能导致次优的机器人运动并增加能耗。此外，PSO虽能高效探索解空间，但会产生较长路径且全局搜索能力有限；而PF方法虽能生成简洁路径，却难以应对远距离目标或障碍物。针对上述问题，我们提出融合两种方法的改进势场光滑粒子群优化算法（SPSO-IPF），该算法能够生成光滑安全的路径。研究表明，相较于单独使用PSO或PF方法，SPSO-IPF在静态与动态环境中均表现出显著优越性，验证了其有效性。