# Distributed Swarm Collision Avoidance Based on Angular Calculations

arXiv: https://arxiv.org/abs/2108.12934 · HTML: https://mlsanity.com/paper/2108.12934/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 29 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning](https://mlsanity.com/paper/1709.10082/) (2017)
- [A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance](https://mlsanity.com/paper/1811.05929/) (2018)
- [Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control](https://mlsanity.com/paper/1809.04230/) (2018)
- [Force-based Algorithm for Motion Planning of Large Agent Teams](https://mlsanity.com/paper/1909.05415/) (2019)
- [Realtime Collision Avoidance for Mobile Robots in Dense Crowds using Implicit Multi-sensor Fusion and Deep Reinforcement Learning](https://mlsanity.com/paper/2004.03089/) (2020)
- [Swarm Formation Morphing for Congestion Aware Collision Avoidance](https://mlsanity.com/paper/2011.03883/) (2020)

## Abstract (arXiv)

Collision avoidance is one of the most important topics in the robotics field. The goal is to move the robots from initial locations to target locations such that they follow shortest non-colliding paths in the shortest time and with the least amount of energy. In this paper, a distributed and real-time algorithm for dense and complex 2D and 3D environments is proposed. This algorithm uses angular calculations to select the optimal direction for the movement of each robot and it has been shown that these separate calculations lead to a form of cooperative behavior among agents. We evaluated the proposed approach on various simulation and experimental scenarios and compared the results with FMP and ORCA, two important algorithms in this field. The results show that the proposed approach is at least 25% faster than ORCA and at least 7% faster than FMP and also more reliable than both methods. The proposed method is shown to enable fully autonomous navigation of a swarm of crazyflies.