A review on multi-robot systems categorised by application domain

Literature reviews on Multi-Robot Systems (MRS) typically focus on fundamental technical aspects, like coordination and communication, that need to be considered in order to coordinate a team of robots to perform a given task effectively and efficiently. Other reviews only consider works that aim to address a specific problem or one particular application of MRS. In contrast, this paper presents a survey of recent research works on MRS and categorises them according to their application domain. Furthermore, this paper compiles a number of seminal review works that have proposed specific taxonomies in classifying fundamental concepts, such as coordination, architecture and communication, in the field of MRS.peer-reviewe

odNEAT: an algorithm for decentralised online evolution of robotic controllers

Online evolution gives robots the capacity to learn new tasks and to adapt to changing environmental conditions during task execution. Previous approaches to online evolution of neural controllers are typically limited to the optimisation of weights in networks with a prespecified, fixed topology. In this article, we propose a novel approach to online learning in groups of autonomous robots called odNEAT. odNEAT is a distributed and decentralised neuroevolution algorithm that evolves both weights and network topology. We demonstrate odNEAT in three multirobot tasks: aggregation, integrated navigation and obstacle avoidance, and phototaxis. Results show that odNEAT approximates the performance of rtNEAT, an efficient centralised method, and outperforms IM-( mu + 1), a decentralised neuroevolution algorithm. Compared with rtNEAT and IM( mu + 1), odNEAT's evolutionary dynamics lead to the synthesis of less complex neural controllers with superior generalisation capabilities. We show that robots executing odNEAT can display a high degree of fault tolerance as they are able to adapt and learn new behaviours in the presence of faults. We conclude with a series of ablation studies to analyse the impact of each algorithmic component on performance.info:eu-repo/semantics/submittedVersio

Novelty-driven cooperative coevolution

Cooperative coevolutionary algorithms (CCEAs) rely on multiple coevolving populations for the evolution of solutions composed of coadapted components. CCEAs enable, for instance, the evolution of cooperative multiagent systems composed of heterogeneous agents, where each agent is modelled as a component of the solution. Previous works have, however, shown that CCEAs are biased toward stability: the evolutionary process tends to converge prematurely to stable states instead of (near-)optimal solutions. In this study, we show how novelty search can be used to avoid the counterproductive attraction to stable states in coevolution. Novelty search is an evolutionary technique that drives evolution toward behavioural novelty and diversity rather than exclusively pursuing a static objective. We evaluate three novelty-based approaches that rely on, respectively (1) the novelty of the team as a whole, (2) the novelty of the agents’ individual behaviour, and (3) the combination of the two. We compare the proposed approaches with traditional fitness-driven cooperative coevolution in three simulated multirobot tasks. Our results show that team-level novelty scoring is the most effective approach, significantly outperforming fitness-driven coevolution at multiple levels. Novelty-driven cooperative coevolution can substantially increase the potential of CCEAs while maintaining a computational complexity that scales well with the number of populations.info:eu-repo/semantics/publishedVersio

Secure Multi-Robot Adaptive Information Sampling with Continuous, Periodic and Opportunistic Connectivity

Multi-robot teams are an increasingly popular approach for information gathering in large geographic areas, with applications in precision agriculture, natural disaster aftermath surveying, and pollution tracking. In a coordinated multi-robot information sampling scenario, robots share their collected information amongst one another to form better predictions. These robot teams are often assembled from untrusted devices, making the verification of the integrity of the collected samples an important challenge. Furthermore, such robots often operate under conditions of continuous, periodic, or opportunistic connectivity and are limited in their energy budget and computational power. In this thesis, we study how to secure the information being shared in a multi-robot network against integrity attacks and the cost of integrating such techniques. We propose a blockchain-based information sharing protocol that allows robots to reject fake data injection by a malicious entity. However, optimal information sampling is a resource-intensive technique, as are the popular blockchain-based consensus protocols. Therefore, we also study its impact on the execution time of the sampling algorithm, which affects the energy spent. We propose algorithms that build on blockchain technology to address the data integrity problem, but also take into account the limitations of the robots’ resources and communication. We evaluate the proposed algorithms along the perspective of the trade-offs between data integrity, model accuracy, and time consumption under continuous, periodic, and opportunistic connectivity

Cost Adaptation for Robust Decentralized Swarm Behaviour

Decentralized receding horizon control (D-RHC) provides a mechanism for coordination in multi-agent settings without a centralized command center. However, combining a set of different goals, costs, and constraints to form an efficient optimization objective for D-RHC can be difficult. To allay this problem, we use a meta-learning process -- cost adaptation -- which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic. We use this adaptive D-RHC method for control of mesh-networked swarm agents. This formulation allows a wide range of tasks to be encoded and can account for network delays, heterogeneous capabilities, and increasingly large swarms through the adaptation mechanism. We leverage the Unity3D game engine to build a simulator capable of introducing artificial networking failures and delays in the swarm. Using the simulator we validate our method on an example coordinated exploration task. We demonstrate that cost adaptation allows for more efficient and safer task completion under varying environment conditions and increasingly large swarm sizes. We release our simulator and code to the community for future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

Comparison of genetic algorithms used to evolve specialisation in groups of robots

This paper investigates the role of genetic algorithms in determining which kind of specialisation emerges in decentralised simulated teams of robots controlled by evolved neural networks. As shown in previous works, different tasks may be better solved by robots specialized in a particular manner. However it was not clarified how much the genetic algorithm used might drive the evolution of one kind of specialisation or another: this is the goal of this paper. The study is conducted by evolving teams of robots that have to solve two different tasks that are better accomplished by using different types of specialisation (innate versus situated). Results suggest that the type of genetic algorithm employed plays a major role in determining how robots specialize and in most of the cases the algorithms used tend to always yield the same specialization. Only one of the algorithms tested led to the emergence of the most suitable kind of specialisation for each one of the two tasks

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available