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This article proposes the application of a distributed containment control algorithm to a team of mobile robots. This paper builds on the containment controller developed by Yuan et al. (2019) for generic linear multi-agent system and tested in simulation only. In this article, we particularize the controller for the case of multiple mobile robots by including it into a two-layer control scheme. The high-level controller computes a desired position for the mobile robots, that is then used as reference trajectory for the low-level controller. The resulting control system is implemented as a fully distributed system on a team of mobile robots and validated in simulations and experiments. 

In this paper we propose a landmark-based map localization system for robotic swarms. The proposed system leverages the capabilities of a distributed landmark identification algorithm developed for robotic swarms presented in [1]. The output of the landmark identification consists of a vector of probabilities that each individual robot is looking at a particular landmark in the environment. In this work, this vector is used individually by each component of the swarm to feed the measurement update of a particle filter to estimate the robot location. The system was tested in simulation to validate its performance. 

This paper addresses the problem of hybrid control for a class of switched uncertain systems. The switched system under consideration is subject to structured uncertain dynamics in a linear fractional transformation (LFT) form and time-varying input delays. A novel hybrid controller is proposed, which consists of three major components: the integral quadratic constraint (IQC) dynamics, the continuous dynamics, and the jump dynamics. The IQC dynamics are developed by leveraging methodologies from robust control theory and are utilised to address the effects of time-varying input delays. The continuous dynamics are structured by feeding back not only measurement outputs but also some system's internal signals. The jump dynamics enforce a jump (update/reset) at every switching time instant for the states of both IQC dynamics and continuous dynamics. Based on this, robust stability of the overall hybrid closed-loop system is established under the average dwell time framework with multiple Lyapunov functions. Moreover, the associated control synthesis conditions are fully characterised as linear matrix inequalities, which can be solved efficiently. An application example on regulation of a nonlinear switched electronic circuit system has been used to demonstrate effectiveness and usefulness of the proposed approach. 

In this paper we propose a study on landmark identification as a step towards a localization setup for real-world robotic swarms setup. In real world, landmark identification is often tackled as a place recognition problem through the use of computationally intensive Convolutional Neural Networks. However, the components of a robotic swarm usually have limited computational and sensing capabilities that allows only for the application of relatively shallow networks that results in large percentage of recognition errors. In a previous attempt of solving a similar setup - cooperative object recognition - the authors of [1] have demonstrated how the use of communication among a swarm and a naive Bayes classifier was able to substantially improve the correct recognition rate. An assumption of that paper not compatible with a swarm localization setup was that all swarm components would be looking at the same object. In this paper, we propose the use of a weighting factor to relapse this assumption. Through the use of simulation data, we show that our approach provides high recognition rates even in situations in which the robots would look at different objects.