An Extension of Pathfinding Algorithms for Randomly Determined Speeds
Visvam Rajesh, Chase Q. Wu
Abstract
Pathfinding is the search of an optimal path between two points on a graph. This paper investigates the performance of pathfinding algorithms in 3D voxel environments, focusing on optimizing paths for both time and distance. Utilizing computer simulations in Unreal Engine 5, four algorithms—A*, Dijkstra’s algorithm, Dijkstra’s algorithm with speed consideration, and a novel adaptation referred to as Time*—are tested across various environment sizes. Results indicate that while Time* exhibits a longer execution time than A*, it significantly outperforms all other algorithms in traversal time optimization. Despite slightly longer path lengths, Time* can compute more efficient paths. Statistical analysis of the results suggests consistent performance of Time* across trials. Implications highlight the significance of speed-based pathfinding algorithms in practical applications and suggest further research into optimizing algorithms for variable speed environments.
Citation
V. Rajesh and C. Q. Wu, "An Extension of Pathfinding Algorithms for Randomly Determined Speeds," 2024 IEEE International Performance, Computing, and Communications Conference (IPCCC), 2024.
Research Interests
pathfinding algorithms
developing efficient 3D voxel-based pathfinding algorithms for dynamic environments with variable movement speeds.
autonomous systems
path planning and control systems for driverless racing vehicles using SLAM and trajectory optimization.
machine learning
vision Transformers and neural networks for image classification and computer vision tasks.
robotics software
real-time control systems and trajectory planning for competitive FIRST Robotics Competition robots.