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Swarm and Evolutionary Computation

Modeling and designing a robotic swarm: A quantum computing approach

Maria Mannone, Valeria Seidita, Antonio Chella

Published Apr 5, 2023• 45 citations
Publisher
DOI
10.1016/j.swevo.2023.101297

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Computational resources of quantum computing can enhance robotic motion, decision making, and path planning. While the quantum paradigm is being applied to individual robots, its approach to swarms of simple and interacting robots remains largely unexplored. In this paper, we attempt to bridge the gap between swarm robotics and quantum computing, in the framework of a search and rescue mission. We focus on a decision-making and path-planning collective task. Thus, we present a quantum-based path-planning algorithm for a swarm of robots. Quantization enters position and reward information (measured as a robot’s proximity to the target) and path-planning decisions. Pairwise information-exchange is modeled through a logic gate, implemented with a quantum circuit. Path planning draws upon Grover’s search algorithm, implemented with another quantum circuit. Our case study involves a search and rescue scenario, inspired by ant-foraging behavior in nature, as an example of swarm intelligence. We show that our method outperforms two ant-behavior simulations, in NetLogo and Java, respectively, presenting a faster convergence to the target, represented here by the source of food. This study can shed light on future applications of quantum computing to swarm robotics.
Quantum computing is a branch of computer science derived from the fundamental laws of quantum mechanics, such as state superposition, multi-value logic and destructive measure. An open challenge in itself is to re-think in quantum terms classic problems and solving techniques. Another nature-inspired field is the development of swarm-based robotic applications, where the challenge is catching the fundamental laws governing swarm dynamics, such as pattern formation and target reaching. Here, we review some recent approaches on swarm dynamics, the organizational rules of which are formalized according to quantum computing. In this way, the shades of probability in decision-making for multiple-robot systems can be expressed according to the multi-value logic underlying quantum computing. In our review, specific quantum circuits are sketched to give an idea of how these problems have been faced in computational terms. The article is enriched by references to sonification, a strategy adding one more sensory dimension to data representation, a human-friendly tool to navigate the complexity of swarm-robotic movements in a given arena.This article is part of the theme issue 'The road forward with swarm systems'.