An Optimized Hybrid Approach for Path Planning: A Combination of Lyapunov Functions and High-Level Planning Algorithms

Path planning problem, which involves finding the optimal path from origin to destination, is a challenging problem with broad applications such as autonomous vehicles, mobile robot navigation, and game artificial intelligence. In this paper, we propose an approach that brings together the benefits of high-level planning using Dijkstra’s algorithm and Lyapunov-based control system (LbCS) to address this problem. In our approach, the path planning problem is formulated in two parts. Firstly, it involves an optimization task at a high level, with the objective function representing the overall path cost or distance via landmarks. Following this, a LbCS navigates between these landmarks, ensuring stability and convergence to each point in the sequence. Our approach offers a comprehensive solution to the path planning problem, striking a balance between path efficiency and computational feasibility. This research paves the way for advanced landmark navigation and path planning algorithms, extending potential applications to include both static and dynamic obstacle navigation during path planning. The effectiveness and novelty of our proposed method will be assessed through simulations and comparative analyses using LbCS with and without our proposed high-level planning. We anticipate our findings will significantly contribute to the current body of knowledge and future landmark navigation research. Our plan for future research is to extend this work to include obstacles and refine methodology to achieve better results.