This letter proposes a distributed strategy to achieve both persistent monitoring and target detection in a rectangular and obstacle-free environment. Each robot has to repeatedly follow a smooth trajectory and avoid collisions with other robots. To achieve this goal, we rely on the time-inverted Kuramoto dynamics and the use of Lissajous curves. We analyze the resiliency of the system to perturbations or temporary failures, and we validate our approach through both simulations and experiments on real robotic platforms. In the letter, we adopt Model Predictive Contouring Control as a low level controller to minimize the tracking error while accounting for the robots' dynamical constraints and the control inputs saturation. The results obtained in the experiments are in accordance with the simulations.

The paper addresses the problem of multi-robot navigation in human shared spaces. We propose a hierarchical framework that combines global path planning, local path planning and reactive strategies, ensuring a safe and socially-aware navigation. We show through several tests and extensive experiments with a real robotic implementation that our combination of solutions delivers excellent results in terms of robustness and performance even in challenging natural scenarios.

Different authors have addressed a number of problems in the area of distributed control proposing convincing solutions to specific problems such as static coverage, dynamic coverage/exploration, rendezvous, flocking, formation control. However, a major limitation of problem-specific approaches is a fundamental lack of flexibility when the group meets unexpected conditions and has to change its goal on the fly. In this paper, we show that a large class of distributed control problems can be cast into a general framework based on the adoption of the Lloyd methodology. The adoption of a unified framework enables efficient solutions for the specific problems guaranteeing at the same time important safety and functional properties and a large degree of flexibility in the execution of group tasks. The paper sets the theoretical basis for this development and proves the efficacy of the proposed solutions through extensive simulations and experimental results.

Given a team composed of groups of robots with different abilities and different tasks, we propose a control solution that allows maintaining the connectivity between the agents whilst securing the execution of the different tasks assigned to the team. The specific notion of connectivity adopted here is that the different agents have to mutually remain in line-of-sight, and this condition is guaranteed in presence of obstacles. In the computation of the graph topology, based on Minimum Spanning Tree (MST), each agent takes into account the surrounding environment, its own task and the tasks of its neighbours. Our solution permits the execution of heterogeneous tasks and the task dynamic reallocation between the robots when required by the situation. The approach relies on a sound theoretical framework, which is discussed in this letter, while its practical feasibility is shown through simulations and experimental data using real robotic platforms.

In this paper we analyse the equilibrium configurations for the time-inverted Kuramoto Model with homogeneous agents and a fixed ring topology, where time-inverted means that the coupling between the different states is via a negative factor. This model exhibits a dual behaviour with respect to the classic Kuramoto Model with a positive coupling. In the paper, we show the existence of two possible stable equilibrium configurations: the splay state formation (1-clustered coverage) and the deployment in clusters (κ-clustered coverage). We provide sufficient conditions for the splay state formation and a stability analysis for the networked system. Moreover, we provide some initial results towards the controllability of the final equilibrium configurations. In particular, we lay the foundations to understand the conditions to switch between stable equilibria.

In this paper we analyse the equilibrium configurations for the time-inverted Kuramoto Model with homogeneous agents and a fixed ring topology, where time-inverted means that the coupling between the different states is via a negative factor. This model exhibits a dual behaviour with respect to the classic Kuramoto Model with a positive coupling. In the paper, we show the existence of two possible stable equilibrium configurations: the splay state formation (1-clustered coverage) and the deployment in clusters (κ-clustered coverage). We provide sufficient conditions for the splay state formation and a stability analysis for the networked system. Moreover, we provide some initial results towards the controllability of the final equilibrium configurations. In particular, we lay the foundations to understand the conditions to switch between stable equilibria.

The letter proposes a combination of ideas to support navigation for a mobile robot across dynamic environments, cluttered with obstacles and populated by human beings. The combination of the classical potential field methods and limit cycle based approach with an innovative shape for the limit cycles, generates paths which are reasonably short, smooth and comfortable to follow (which can be very important for assistive robots), and it respects the safety and psychological comfort of the bystanders by staying clear of their private space.

We present an algorithm for collision free and socially-aware navigation of multiple robots in an environment shared with human beings, other robots and with the presence of static obstacles. We formulate the problem as a constrained optimization problem, where the cost function is chosen in order for the robotic agents to exhibit bio-inspired behaviors, such as cooperation inside the group and cohesive motion. Some of the constraints are required to avoid collision between the agents and with other obstacles and emanate from the application of Velocity Obstacle approach. The nonholonomic dynamics of the vehicles, is managed through the application of the feedback linearization technique to map the velocities in the control values. In this paper we propose both centralized solution and a completely decentralized solution. The overall strategies are extensively tested in simulations.

We present a Lloyd-based navigation solution for robots that are required to move in a dynamic environment, where static obstacles (e.g, furnitures, parked cars) and unpredicted moving obstacles (e.g., humans, other robots) have to be detected and avoided on the fly. The algorithm can be computed in real-time and falls in the category of the reactive methods. Moreover, we propose an extension to the multi-agent case that deals with cohesion and cooperation between agents. The goodness of the method is proved through extensive simulations and, for the single agent navigation in human-shared environment, also with experiments on a unicycle-like robot.