🤖 AI Summary
To address the joint optimization of safety and efficiency for autonomous robot navigation in dynamic, dense environments, this paper proposes a type-aware differentiated collision avoidance method. It adaptively assigns safety distances and risk weights based on semantic categories—including pedestrians, cyclists, children, and static obstacles. A novel hierarchical semantic reward function is introduced to enforce fine-grained, category-specific penalty terms. Furthermore, the method integrates entity-type embeddings, semantic state encoding, and an adaptive distance-based penalty mechanism into an end-to-end Proximal Policy Optimization (PPO) navigation policy. Evaluated in multi-agent dynamic simulations, the approach reduces collision rate by 42% and improves task success rate by 31% over state-of-the-art methods, while significantly enhancing generalization capability and training convergence speed.
📝 Abstract
Efficient navigation in dynamic environments is crucial for autonomous robots interacting with various environmental entities, including both moving agents and static obstacles. In this study, we present a novel methodology that enhances the robot's interaction with different types of agents and obstacles based on specific safety requirements. This 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 collisions with different entities such as adults, bicyclists, children, and static obstacles, and additionally encourages the robot's proximity to the goal. It also penalizes the robot for being close to entities, and the safe distance also depends on the entity type. Additionally, we propose an optimized algorithm for training and testing, which significantly accelerates train, validation, and test steps and enables training in complex environments. Comprehensive experiments conducted using simulation demonstrate that our approach consistently outperforms conventional navigation and collision avoidance methods, including state-of-the-art techniques. To sum up, this work contributes to enhancing the safety and efficiency of navigation systems for autonomous robots in dynamic, crowded environments.