Autonomous exploration is an important application of multi-vehicle systems, where a team of networked robots are coordinated to explore an unknown environment collaboratively. This technique has earned significant research interest due to its usefulness in search and rescue, fault detection and monitoring, localization and mapping, etc. In this paper, a novel cooperative exploration strategy is proposed for multiple mobile robots, which reduces the overall task completion time and energy costs compared to conventional methods. To efficiently navigate the networked robots during the collaborative tasks, a hierarchical control architecture is designed which contains a high-level decision making layer and a low-level target tracking layer. The proposed cooperative exploration approach is developed using dynamic Voronoi partitions, which minimizes duplicated exploration areas by assigning different target locations to individual robots. To deal with sudden obstacles in the unknown environment, an integrated deep reinforcement learning based collision avoidance algorithm is then proposed, which enables the control policy to learn from human demonstration data and thus improve the learning speed and performance. Finally, simulation and experimental results are provided to demonstrate the effectiveness of the proposed scheme.

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Mobile robots are playing a significant role in Higher Education science and engineering teaching, as they offer a flexible platform to explore and teach a wide-range of topics such as mechanics, electronics and software. Unfortunately the widespread adoption is limited by their high cost and the complexity of user interfaces and programming tools. To overcome these issues, a new affordable, adaptable and easy-to-use robotic platform is proposed. Mona is a low-cost, open-source and open-hardware mobile robot, which has been developed to be compatible with a number of standard programming environments. The robot has been successfully used for both education and research at The University of Manchester, UK.

Automatic cruise control of a platoon of multiple connected vehicles in an automated highway system has drawn significant attention of the control practitioners over the past two decades due to its ability to reduce traffic congestion problems, improve traffic throughput and enhance safety of highway traffic. This paper proposes a two-layer distributed control scheme to maintain the string stability of a heterogeneous and connected vehicle platoon moving in one dimension with constant spacing policy assuming constant velocity of the lead vehicle. A feedback linearization tool is applied first to transform the nonlinear vehicle dynamics into a linear heterogeneous state-space model and then a distributed adaptive control protocol has been designed to keep equal inter-vehicular spacing between any consecutive vehicles while maintaining a desired longitudinal velocity of the entire platoon. The proposed scheme utilizes only the neighbouring state information (i.e. relative distance, velocity and acceleration) and the leader is not required to communicate with each and every one of the following vehicles directly since the interaction topology of the vehicle platoon is designed to have a spanning tree rooted at the leader. Simulation results demonstrated the effectiveness of the proposed platoon control scheme. Moreover, the practical feasibility of the scheme was validated by hardware experiments with real robots.