Enhancing Path Planning with Obstacles Via a Combined Dijkstra-LbCS Methodology

In this study, we present an innovative approach to path planning that integrates Dijkstra’s algorithm with a Lyapunov-based control system (LbCS), focusing on incorporating unforeseen obstacle navigation. Path planning is an essential aspect of autonomous systems, such as self-driving cars and robotic navigators, requiring the capability to adapt to changing environments. Our approach utilizes the efficiency of Dijkstra’s algorithm for high-level path planning, combined with the stability and precision of LbCS, and extends it to adeptly handle obstacles. We also integrate obstacle detection and avoidance mechanism within the path planning framework. This addition allows the algorithm to dynamically adjust paths in response to unforeseen static obstacles, ensuring continuous and safe navigation. The LbCS component has been adapted to manage sudden changes in the planned route, maintaining smoothness and stability in the robot’s movement. Through comprehensive simulations, our methodology demonstrated a significant improvement in path planning efficiency, reducing total travel distance by around 4%, compared to traditional LbCS methods without Dijkstra’s integration. These improvements underscore the potential of our hybrid approach to provide a versatile and robust solution for autonomous navigation across a wide range of scenarios. Future work will explore the integration of advanced machine learning techniques for predictive obstacle detection and further optimization of path planning strategies.