Improved Hybrid A* Algorithm Based on Iterative Acceleration for Autonomous Vehicles

Reverse Search Heuristic Sampling-Based Path Planning Algorithm With Path Smoothing for Autonomous Vehicles

Path planning is a critical component of autonomous driving, particularly in complex unstructured environments characterized by the absence of clearly defined drivable corridors and the presence of irregular obstacles. A widely adopted two-stage framework under such conditions involves generating an initial path follow...

A multi-strategy improved A* algorithm for intelligent vehicle path planning based on dynamic adaptive weights and kinematic constraints

In this study, a multi-strategy improved A* algorithm based on dynamic adaptive weights and kinematic constraints is proposed, which can solve the problems of high collision risk, path jitter, and the difficulty of balancing search efficiency and optimality in traditional A* algorithms for intelligent vehicle path plan...

Real-time path planning framework for autonomous vehicles using hybrid fuzzy optimization and obstacle avoidance via Kolmogorov-Arnold Networks

Research on UAV path planning based on an improved A* algorithm

UAV path planning is a crucial component of autonomous navigation and logistics delivery, and its performance directly affects both operational safety and efficiency. However, the standard A* algorithm often suffers from redundant node expansions and limited path smoothness in cluttered environments, which constrains r...

Research on Robot Path Planning Based on Improved A* Algorithm

Robot path planning is a key core technology for realizing autonomous navigation of robots. Among them, the A* algorithm has been widely used due to its good balance between path optimality and search efficiency. However, the traditional A* algorithm faces problems such as excessive expansion nodes, numerous path infle...

MEBA*: a Multi-Strategy Enhanced Bidirectional A* algorithm for global path planning of mobile robots