# A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance

arXiv: https://arxiv.org/abs/1811.05929 · HTML: https://mlsanity.com/paper/1811.05929/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2018.

## Built on by (most cited first, 10 of 10)

- [SACSoN: Scalable Autonomous Control for Social Navigation](https://mlsanity.com/paper/2306.01874/) (2023)
- [Analyzing Human Models that Adapt Online](https://mlsanity.com/paper/2103.05746/) (2021)
- [SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigation](https://mlsanity.com/paper/2403.00991/) (2024)
- [AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks](https://mlsanity.com/paper/2102.04255/) (2021)
- [Axes for Sociotechnical Inquiry in AI Research](https://mlsanity.com/paper/2105.06551/) (2021)
- [SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots](https://mlsanity.com/paper/2108.01262/) (2021)
- [Exact and Bounded Collision Probability for Motion Planning under Gaussian Uncertainty](https://mlsanity.com/paper/2110.06348/) (2021)
- [Stranger Danger! Identifying and Avoiding Unpredictable Pedestrians in RL-based Social Robot Navigation](https://mlsanity.com/paper/2407.06056/) (2024)
- [Distributed Swarm Collision Avoidance Based on Angular Calculations](https://mlsanity.com/paper/2108.12934/) (2021)
- [State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey](https://mlsanity.com/paper/2408.11822/) (2024)

## Abstract (arXiv)

Robust motion planning is a well-studied problem in the robotics literature, yet current algorithms struggle to operate scalably and safely in the presence of other moving agents, such as humans. This paper introduces a novel framework for robot navigation that accounts for high-order system dynamics and maintains safety in the presence of external disturbances, other robots, and non-deterministic intentional agents. Our approach precomputes a tracking error margin for each robot, generates confidence-aware human motion predictions, and coordinates multiple robots with a sequential priority ordering, effectively enabling scalable safe trajectory planning and execution. We demonstrate our approach in hardware with two robots and two humans. We also showcase our work's scalability in a larger simulation.