🤖 AI Summary
To address safety and adaptability challenges in autonomous mobile robot navigation within unknown dynamic environments, this paper proposes a planning–control co-design framework. Methodologically, it integrates directional distance metrics with conical motion prediction to construct a risk assessment model, designs a customized cost map, and combines a reference corrector with control barrier functions (CBFs) to enable adaptive velocity modulation and trajectory tracking under safety-boundary constraints. Key contributions include: (i) the first coupling of directional distance and conical prediction for quantitative dynamic risk assessment; and (ii) a novel joint regulation mechanism integrating safety boundaries and reference correction. Extensive evaluations in simulation and complex real-world scenarios demonstrate that the proposed method significantly improves narrow-passage traversal success rates and reactive obstacle avoidance capability, achieving a favorable balance between high safety assurance and navigation efficiency.
📝 Abstract
This paper considers the problem of autonomous robot navigation in unknown environments with moving obstacles. We propose a new method that systematically puts planning, motion prediction and safety metric design together to achieve environmental adaptive and safe navigation. This algorithm balances optimality in travel distance and safety with respect to passing clearance. Robot adapts progress speed adaptively according to the sensed environment, being fast in wide open areas and slow down in narrow passages and taking necessary maneuvers to avoid dangerous incoming obstacles. In our method, directional distance measure, conic-shape motion prediction and custom costmap are integrated properly to evaluate system risk accurately with respect to local geometry of surrounding environments. Using such risk estimation, reference governor technique and control barrier function are worked together to enable adaptive and safe path tracking in dynamical environments. We validate our algorithm extensively both in simulation and challenging real-world environments.
