Path Planning Algorithms for Autonomous Robot Navigation

Autonomous robots require robust path planning algorithms to navigate complex environments efficiently. This study compares A*, Breadth-First Search (BFS), and Depth-First Search (DFS) for optimal path planning in diverse scenarios. Using a 10 × 10 grid with static and dynamic obstacles, we assess these algorithms based on computation time, path length, memory usage, success rate, and adaptability to dynamic conditions. A∗ employs heuristic optimization for balanced performance, BFS guarantees shortest paths in uniform grids, and DFS prioritizes deep exploration for specific use cases. Results show A∗ achieves 78.5% path optimality with a 94.2% success rate, BFS ensures 100 % optimality but with higher memory demands, and DFS offers rapid execution (28.4 ms) but only 65.4 % optimality. These findings provide practical insights for algorithm selection in real-time robotic applications, with A∗ being optimal for dynamic environments, while BFS and DFS excel in specific structured settings.