Avoiding Dense and Dynamic Obstacles in Enclosed Spaces: Application to Moving in Crowds
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Abstract (arXiv)
This paper presents a closed-form approach to constrain a flow within a given volume and around objects. The flow is guaranteed to converge and to stop at a single fixed point. We show that the obstacle avoidance problem can be inverted to enforce that the flow remains enclosed within a volume defined by a polygonal surface. We formally guarantee that such a flow will never contact the boundaries of the enclosing volume and obstacles, and will asymptotically converge towards an attractor. We further create smooth motion fields around obstacles with edges (e.g. tables). Both obstacles and enclosures may be time-varying, i.e. moving, expanding and shrinking. The technique enables a robot to navigate within an enclosed corridor while avoiding static and moving obstacles. It was applied on an autonomous robot (QOLO) in a static complex indoor environment, and also tested in simulations with dense crowds. The final proof of concept was performed in an outdoor environment in Lausanne. The QOLO-robot successfully traversed a marketplace in the center of town in presence of a diverse crowd with a non-uniform motion pattern.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 May 2021.
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Citing papers per year: 2024: 6; 2025: 2; 2026: 1.
9 papers.
- 2024Multi-Agent Dynamic Relational Reasoning for Social Robot Navigationcited by 20
- 2024Neural Contractive Dynamical Systemscited by 14
- 2024HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environmentscited by 9
- 2024Mixed Strategy Nash Equilibrium for Crowd Navigationcited by 3
- 2024HeR-DRL:Heterogeneous Relational Deep Reinforcement Learning for Decentralized Multi-Robot Crowd Navigationcited by 3
- 2024Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regionscited by 1
- 2025Diffeomorphic Obstacle Avoidance for Contractive Dynamical Systems via Implicit Representationscited by 1
- 2025Future-Oriented Navigation: Dynamic Obstacle Avoidance with One-Shot Energy-Based Multimodal Motion Predictioncited by 0
- 2026Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigmscited by 0
- 2026Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigms1 Aug 2026
- 2025Future-Oriented Navigation: Dynamic Obstacle Avoidance with One-Shot Energy-Based Multimodal Motion Prediction1 May 2025
- 2025Diffeomorphic Obstacle Avoidance for Contractive Dynamical Systems via Implicit Representations26 Apr 2025
- 2024HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments19 Nov 2024
- 2024Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions18 Nov 2024
- 2024HeR-DRL:Heterogeneous Relational Deep Reinforcement Learning for Decentralized Multi-Robot Crowd Navigation15 Mar 2024
- 2024Mixed Strategy Nash Equilibrium for Crowd Navigation3 Mar 2024
- 2024Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation22 Jan 2024
- 2024Neural Contractive Dynamical Systems17 Jan 2024