Distributionally Robust Safe Motion Planning with Contextual Information

2025-09-23

AI Summary

This paper addresses safe motion planning in dynamic environments with contextual information integration. Methodologically, it proposes a distributionally robust obstacle avoidance framework that introduces conditional kernel mean embedding (CKME) into motion planning for the first time. Leveraging reproducing kernel Hilbert spaces (RKHS), the approach models the conditional distribution of obstacle trajectories given contextual variables (e.g., traffic flow, weather), and constructs a data-driven uncertainty set based on empirical distributions. This set is embedded into a receding-horizon optimization framework via distributionally robust obstacle avoidance constraints. The key contribution lies in enabling context-aware, risk-anticipatory modeling, substantially enhancing collision avoidance robustness under uncertainty. Simulation results demonstrate significant improvements in collision-free success rates across diverse complex dynamic scenarios compared to conventional methods, validating both effectiveness and generalizability.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Natural Language Processing: Safety and Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

We present a distributionally robust approach for collision avoidance by incorporating contextual information. Specifically, we embed the conditional distribution of future trajectory of the obstacle conditioned on the motion of the ego agent in a reproducing kernel Hilbert space (RKHS) via the conditional kernel mean embedding operator. Then, we define an ambiguity set containing all distributions whose embedding in the RKHS is within a certain distance from the empirical estimate of conditional mean embedding learnt from past data. Consequently, a distributionally robust collision avoidance constraint is formulated, and included in the receding horizon based motion planning formulation of the ego agent. Simulation results show that the proposed approach is more successful in avoiding collision compared to approaches that do not include contextual information and/or distributional robustness in their formulation in several challenging scenarios.

Problem

• Ensuring safe robot motion planning under distributional uncertainty
• Incorporating contextual information for collision avoidance prediction
• Handling conditional obstacle trajectory distributions using RKHS embeddings

Innovation

• Distributionally robust collision avoidance with contextual information
• Conditional kernel mean embedding in RKHS for trajectory prediction
• Ambiguity set formulation for robust motion planning constraints