Obstacle avoidance is a crucial challenge in robotic motion planning, especially for industrial applications requiring precise and collision-free movements. While most studies focus on end-effector trajectory optimization, ensuring full-body collision avoidance in constrained environments remains a significant challenge. This paper introduces a novel obstacle-avoidance strategy for a 3-DOF robotic arm, leveraging an improved A* algorithm to enhance full-configuration motion planning. Our approach optimizes node expansion and integrates an angular penalty heuristic to minimize abrupt path deviations. Additionally, a post-processing step using a Modified Smooth Path Algorithm further refines the trajectory for smoother execution. Experimental validation demonstrates the system’s accuracy and efficiency, achieving a 10% reduction in path length, a 60% decrease in the number of search nodes, and a 30% reduction in computation time while maintaining significantly smoother trajectories. These results highlight the practical benefits of our approach, providing a cost-effective and reliable solution for industrial automation.