Robot Navigation with Entity-Based Collision Avoidance using Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning method for robot navigation that incorporates entity type information and a new reward function to improve collision avoidance and safety in dynamic environments.

Contribution

It presents a novel entity-based collision avoidance approach with an optimized training algorithm for safer and more efficient robot navigation.

Findings

Outperforms existing navigation methods in complex environments

Accelerates training and validation phases significantly

Ensures safer navigation by considering entity types

Abstract

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.