Uncertainty aware navigation improves robot motion in busy places
UCON: Uncertainty-aware Navigation with Historical Re-association in Dynamic Environments
Robotics
Summary
Robots navigating busy and changing environments often lose track of moving obstacles or get confused about their motion, leading to unsafe paths. The authors developed a method called UCON that helps robots remember past observations of moving objects and better estimate their movements with uncertainty. This method integrates uncertainty information directly into planning safe routes, helping robots avoid collisions more reliably. Tests show UCON improves stability in recognizing moving things and navigates more safely in dynamic areas than previous approaches.
What this means in practice
- •For robotics engineers: Improve robot navigation in environments with moving people or vehicles by reducing errors in tracking and modeling moving obstacles.
- •For warehouse automation teams: Enhance autonomous robot navigation to reliably avoid dynamic obstacles such as workers or moving equipment in busy warehouses.
Authors
Bing Sun, Yue Lin, Yongsheng Yuan, Yang Liu, Dong Wang, Huchuan Lu
Abstract
Autonomous navigation in dynamic environments is hindered by two fundamental challenges: perception instability and uncertainty-optimization mismatch. The former leads to identity switches and unreliable motion estimation, while the latter prevents principled incorporation of motion uncertainty into trajectory optimization. To address these challenges, we propose UCON, an uncertainty-aware navigation algorithm in dynamic environments. For perception instability, we present a point-level historical re-association mechanism that leverages historical point cloud fragments to recover lost targets while maintaining identity continuity. Subsequently, a Kalman filter is employed to provide anisotropic motion state estimation and covariance propagation. To resolve the uncertainty-optimization mismatch, we transform predicted states and their covariances into uncertainty sectors, which are embedded as differentiable cost terms within a trajectory optimization framework. This achieves consistent uncertainty-aware dynamic obstacle avoidance while maintaining smoothness and feasibility. Extensive simulations and real-world experiments demonstrate that, while maintaining high computational efficiency, UCON achieves superior perception stability and robust navigation performance in dynamic environments compared to state-of-the-art methods. The code will be open-sourced to facilitate further research.