Dynamic Resilient Containment Control in Multirobot Systems

In this article, we study the dynamic resilient containment control problem for continuous-time multirobot systems (MRSs), i.e., the problem of designing a local interaction protocol that drives a set of robots, namely the followers, toward a region delimited by the positions of another set of robots, namely the leaders, under the presence of adversarial robots in the network. In our setting, all robots are anonymous, i.e., they do not recognize the identity or class of other robots. We consider as adversarial all those robots that intentionally or accidentally try to disrupt the objective of the MRS, e.g., robots that are being hijacked by a cyber–physical attack or have experienced a fault. Under specific topological conditions defined by the notion of (r,s)-robustness, our control strategy is proven to be successful in driving the followers toward the target region, namely a hypercube, in finite time. It is also proven that the followers cannot escape the moving containment area despite the persistent influence of anonymous adversarial robots. Numerical results with a team of 44 robots are provided to corroborate the theoretical findings

Multilayer proportional-integral consensus of heterogeneous multi-agent systems

A distributed proportional-integral multilayer strategy is proposed, to achieve consensus in networks of heterogeneous first-order linear systems. The closed-loop network can be seen as an instance of so-called multiplex networks currently studied in network science. The strategy is able to guarantee consensus, even in the presence of constant disturbances and heterogeneous node dynamics. Contrary to previous approaches in the literature, the proportional and integral actions are deployed here on two different layers across the network, each with its own topology. Explicit expressions for the consensus values are obtained together with sufficient conditions guaranteeing convergence. The effectiveness of the theoretical results are illustrated via numerical simulations using a power network example

Multilayer proportional-integral consensus of heterogeneous multi-agent systems

A distributed proportional-integral multilayer strategy is proposed, to achieve consensus in networks of heterogeneous first-order linear systems. The closed-loop network can be seen as an instance of so-called multiplex networks currently studied in network science. The strategy is able to guarantee consensus, even in the presence of constant disturbances and heterogeneous node dynamics. Contrary to previous approaches in the literature, the proportional and integral actions are deployed here on two different layers across the network, each with its own topology. Explicit expressions for the consensus values are obtained together with sufficient conditions guaranteeing convergence. The effectiveness of the theoretical results are illustrated via numerical simulations using a power network example

Connectivity Preservation in Distributed Control of Multi-Agent Systems

The problem of designing bounded distributed connectivity preserving control strategies for multi-agent systems is studied in this work. In distributed control of multi-agent systems, each agent is required to measure some variables of other agents, or a subset of them. Such variables include, for example, relative positions, relative velocities, and headings of the neighboring agents. One of the main assumptions in this type of systems is the connectivity of the corresponding network. Therefore, regardless of the overall objective, the designed control laws should preserve the network connectivity, which is usually a distance-dependent condition. The designed controllers should also be bounded because in practice the actuators of the agents can only handle finite forces or torques. This problem is investigated for two cases of single-integrator agents and unicycles, using a novel class of distributed potential functions. The proposed controllers maintain the connectivity of the agents that are initially in the connectivity range. Therefore, if the network is initially connected, it will remain connected at all times. The results are first developed for a static information flow graph, and then extended to the case of dynamic edge addition. Connectivity preservation for problems involving static leaders is covered as well. The potential functions are chosen to be smooth, resulting in bounded control inputs. These functions are subsequently used to develop connectivity preserving controllers for the consensus and containment problems. Collision avoidance is investigated as another relevant problem, where a bounded distributed swarm aggregation strategy with both connectivity preservation and collision avoidance properties is presented. Simulations are provided throughout the work to support the theoretical findings

Heterogeneous robots: Model Predictive Control for bearing-only formation and tracking

openMulti-agent systems are systems composed by more than one autonomous robots which usually work under the assumption that they can communicate sending and receiving positions of other robots that operate in the network. The introduction of this kind of systems is due to the fact that in many situations it is preferable to use more than one robot in order to reach more complex goal without the help of the humans, especially in dangerous situations. In this thesis, the focus is on the heterogeneous robots which are robots whose components are heterogeneous in terms of actuation capabilities, even if it is assumed they can receive bearing information with respect to the other agents in the network. Hence, it is developed an heterogeneous MAS composed by 2 UGVs and 2 UAVs. The goals of the thesis is that the formation has to be maintained and the four agents has also to track a desired trajectory through a leader follower approach based on bearing-only implemented using MPC controllers. The role of the leader is to track the desired trajectory while the followers have to form and maintain the formation also during the tracking. The followers do not know the trajectory to be tracked, nor the distance to the other agents and the leader. The approach is based on decentralized leader follower control with bearing-only. The controllers used are the Model Predictive ones since this type of control allow to prevent the critical situations, solving an online optimization problem at each time instant to select the best control action that drives the predicted output to the reference. The proposed approach is implemented in Matlab and Simulink and the results obtained by the simulations will be discussed.Multi-agent systems are systems composed by more than one autonomous robots which usually work under the assumption that they can communicate sending and receiving positions of other robots that operate in the network. The introduction of this kind of systems is due to the fact that in many situations it is preferable to use more than one robot in order to reach more complex goal without the help of the humans, especially in dangerous situations. In this thesis, the focus is on the heterogeneous robots which are robots whose components are heterogeneous in terms of actuation capabilities, even if it is assumed they can receive bearing information with respect to the other agents in the network. Hence, it is developed an heterogeneous MAS composed by 2 UGVs and 2 UAVs. The goals of the thesis is that the formation has to be maintained and the four agents has also to track a desired trajectory through a leader follower approach based on bearing-only implemented using MPC controllers. The role of the leader is to track the desired trajectory while the followers have to form and maintain the formation also during the tracking. The followers do not know the trajectory to be tracked, nor the distance to the other agents and the leader. The approach is based on decentralized leader follower control with bearing-only. The controllers used are the Model Predictive ones since this type of control allow to prevent the critical situations, solving an online optimization problem at each time instant to select the best control action that drives the predicted output to the reference. The proposed approach is implemented in Matlab and Simulink and the results obtained by the simulations will be discussed