Advancing Multi-Robot Path Planning through Nature Inspired Algorithms

Abstract

In multi-robot systems, robots need to move efficiently without colliding with each other, which is called path planning. The challenge is to find the best way for multiple robots to reach their destinations while avoiding obstacles and each other. Traditional methods can struggle with complex environments, so researchers are turning to nature-inspired computing and machine learning algorithms to solve this problem. Nature-inspired algorithms, like those based on the behaviour of ants, bees, or birds, Firefly Algorithm (FA)[1], Bat Algorithm [2]help robots make decisions similar to how animals navigate in the natural world. These techniques can optimize robot paths by mimicking processes found in nature, such as how ants find the shortest route to food or how birds flock together without crashing [3]. Machine learning algorithms, on the other hand, enable robots to learn from their environment and past experiences [4]. By continuously learning, robots can adapt and improve their path planning over time [5]. Combining these two approaches – nature-inspired computing and machine learning – offers a powerful way to tackle the complex problem of multi-robot path planning. This study explores how these advanced techniques can work together to make robot teams more efficient, reducing travel time, avoiding collisions, and navigating through dynamic environments.