Deep Local Trajectory Replanning and Control for Robot Navigation

Abstract

We present a navigation system that combines ideas from hierarchical planning and machine learning. The system uses a traditional global planner to compute optimal paths towards a goal, and a deep local trajectory planner and velocity controller to compute motion commands. The latter components of the system adjust the behavior of the robot through attention mechanisms such that it moves towards the goal, avoids obstacles, and respects the space of nearby pedestrians. Both the structure of the proposed deep models and the use of attention mechanisms make the system's execution interpretable. Our simulation experiments suggest that the proposed architecture outperforms baselines that try to map global plan information and sensor data directly to velocity commands. In comparison to a hand-designed traditional navigation system, the proposed approach showed more consistent performance.

References in corpus (3)

• Social Force Model for Pedestrian Dynamics
• From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots
• Driving Policy Transfer via Modularity and Abstraction

Cited by in corpus (7)

• DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles
• Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusion
• Improving Social Awareness Through DANTE: A Deep Affinity Network for Clustering Conversational Interactants
• Learn to Navigate Maplessly with Varied LiDAR Configurations: A Support Point-Based Approach
• HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments
• AVOCADO: Adaptive Optimal Collision Avoidance driven by Opinion
• MfNeuPAN: Proactive End-to-End Navigation in Dynamic Environments via Direct Multi-Frame Point Constraints