# Mobile Robot Path Planning in Static Environments using Particle Swarm Optimization

arXiv: https://arxiv.org/abs/2008.10000 · HTML: https://mlsanity.com/paper/2008.10000/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning based Air Combat Maneuver Generation](https://mlsanity.com/paper/2201.05528/) (2022)

## Abstract (arXiv)

Motion planning is a key element of robotics since it empowers a robot to navigate autonomously. Particle Swarm Optimization is a simple, yet a very powerful optimization technique which has been effectively used in many complex multi-dimensional optimization problems. This paper proposes a path planning algorithm based on particle swarm optimization for computing a shortest collision-free path for a mobile robot in environments populated with static convex obstacles. The proposed algorithm finds the optimal path by performing random sampling on grid lines generated between the robot start and goal positions. Functionality of the proposed algorithm is illustrated via simulation results for different scenarios.