Recent advancements in autonomous swarm systems have made a pivotal point in robotic science. Utilising a large-scale swarm of simple robots to accomplish complex tasks offers efficient, robust, and reliable solutions inspired by natural phenomena. Although bio-inspired methodologies have presented competent algorithms, those approaches inspired by the physical interactions in viscoelastic materials demonstrate more structured methods to prove the stability and robust performance of the algorithms mathematically. This paper proposes a new viscoelastic swarm algorithm which applies to heterogeneous swarm systems. In this paper, the algorithm development utilises the Lyapunov method to determine stability criteria and corresponding conditions. Therefore, the resulting approach does not rely on complex optimisation to obtain the parameters that guarantee stable performance. In addition to the theoretical framework, a series of Monte Carlo simulations have been conducted to assess the algorithm’s performance and its sensitivity to the key variables. Furthermore, the algorithm’s performance has been evaluated by a series of experiments with real robots to examine the effect of different variables, such as neighbourhood conditions and the stiffness coefficient, on the algorithm’s output. The results obtained from the simulations and experiments demonstrate the stable and bounded performance of the algorithm and how the key variables, such as stiffness coefficient and number of neighbours for each robot, affect the swarm performance. The comparison results, obtained from real-world experiments with a state-of-the-art algorithm, show that the proposed framework significantly reduces the control effort for the robots while improving the swarm behaviour.

Confined and cluttered aquatic environments present a number of significant challenges with respect to inspection by robotic platforms, including localisation and communications. Some of these can be mitigated by using collaborative heterogeneous multi-robot teams. An important element of such a system is collaborative control. This paper addresses this challenge by presenting an Image-Based Visual Servoing (IBVS), leader-follower control system for heterogeneous aquatic robots. Experiments were conducted in an uncluttered pond to demonstrate the capabilities of the system. The results show robots can maintain tracking each other with maximum$x$and$y$displacements of 0.42 m and 0.41 m, the maximum projection distance in the xy-plane of maintaining formation is 0.45 m, showing the stability and feasibility of deploying such system on underwater platforms.

The development of mobile robot platforms for inspection has gained traction in recent years. However, conventional mobile robots are unable to address the challenge of operating in extreme environments where the robot is required to traverse narrow gaps in highly cluttered areas with restricted access, typically through narrow ports. This article presents MIRRAX, a robot designed to meet these challenges by way of its reconfigurable capability. Controllers for the robot are detailed, along with an analysis on the controllability of the robot given the use of mecanum wheels in a variable configuration. Characterization on the robot's performance identified suitable configurations for operating in narrow environments. The experimental validation of the robot's controllability shows good agreement with the theoretical analysis and the capability to address the challenges of accessing entry ports as small as 150-mm diameter, as well as navigating through cluttered environments. This article also presents results from a deployment in a Magnox facility at the Sellafield nuclear site in the U.K.—the first robot to ever do so, for remote inspection and mapping.

This work presents a practical method of obtaining a dynamic system model for small omnidirectional aquatic vehicles. The models produced can be used to improve vehicle localisation, aid in the design or tuning of control systems and facilitate the development of simulated environments. The use of a dynamic model for onboard real-time velocity prediction is of particular importance for aquatic vehicles because, unlike ground vehicles, fast and direct measurement of velocity using encoders is not possible. Previous work on model identification of aquatic vehicles has focused on large vessels that are typically underactuated and have low controllability in the sway direction. In this paper it is demonstrated that the procedure for identifying the model coefficients can be performed quickly, without specialist equipment and using only onboard sensors. This is of key importance because the dynamic model coefficients will change with the payload. Two different thrust allocation schemes are tested, one of which is a known method and another is proposed here. Validation tests are performed and the models are shown to be suitable for their intended applications. Significant reduction in model error is demonstrated using the novel thrust allocation method that is designed to avoid deadbands in the thruster responses.