# Force-based Algorithm for Motion Planning of Large Agent Teams

arXiv: https://arxiv.org/abs/1909.05415 · HTML: https://mlsanity.com/paper/1909.05415/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Attention and Prediction Guided Motion Detection for Low-Contrast Small Moving Targets](https://mlsanity.com/paper/2104.13018/) (2021)
- [Nearest-Neighbor-based Collision Avoidance for Quadrotors via Reinforcement Learning](https://mlsanity.com/paper/2104.14912/) (2021)
- [Distributed Swarm Collision Avoidance Based on Angular Calculations](https://mlsanity.com/paper/2108.12934/) (2021)
- [Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigms](https://mlsanity.com/paper/2608.00625/) (2026)

## Abstract (arXiv)

This paper presents a distributed, efficient, scalable and real-time motion planning algorithm for a large group of agents moving in 2 or 3-dimensional spaces. This algorithm enables autonomous agents to generate individual trajectories independently with only the relative position information of neighboring agents. Each agent applies a force-based control that contains two main terms: collision avoidance and navigational feedback. The first term keeps two agents separate with a certain distance, while the second term attracts each agent toward its goal location. Compared with existing collision-avoidance algorithms, the proposed force-based motion planning (FMP) algorithm is able to find collision-free motions with lower transition time, free from velocity state information of neighbouring agents. It leads to less computational overhead. The performance of proposed FMP is examined over several dense and complex 2D and 3D benchmark simulation scenarios, with results outperforming existing methods.