  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.
