2024/08/26 by Yury Kolomeytsev, Kolomeytsev, Yury, Dmitry Golembiovsky +1 · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Robotic Path Planning Algorithms #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2408.14183
openalex publication_date 2024/08/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Efficient navigation in dynamic environments is crucial for autonomous robots interacting with moving agents and static obstacles. We present a novel deep reinforcement learning approach that improves robot navigation and interaction with different types of agents and obstacles based on specific safety requirements. Our approach uses information about the entity types, improving collision avoidance and ensuring safer navigation. We introduce a new reward function that penalizes the robot for being close to or colliding with different entities such as adults, bicyclists, children, and static obstacles, while also encouraging the robot's progress toward the goal. We propose an optimized algorithm that significantly accelerates the training, validation, and testing phases, enabling efficient learning in complex environments. Comprehensive experiments demonstrate that our approach consistently outperforms state-of-the-art navigation and collision avoidance methods.