In a multirobot multitask scenario, redundancy is a fundamental characteristic, arising both from the larger number of agents relative to the tasks to be executed and from the surplus of input resources compared to the controlled variables required. In large-scale applications, scalability becomes critical for enhancing algorithm performance. This article addresses the decentralization of the dynamic control allocation policy in input-to-task redundant multiagent systems. We provide sufficient conditions for both the output and control matrices to assure that the information encoded in both signals depend only on data shared among neighbors. Additionally, we extend the task allocation paradigm to include input redundancy information as key factor in the allocation policy. The resulting framework enables simultaneous task and control allocation in a fully decentralized setting. The performance of the proposed method has been tested and validated by means of small-scale simulation on a heterogeneous multiagent system in a multitask scenario.

This work concerns the implementation of hybrid consensus control for nonholonomic multi-robot systems. It is shown how it is possible to attain a consensus behaviour in a multi-agent system having internal communications characterized by sampling. Furthermore, it is shown how it is possible to make the multi-agent system reach a formation with desired position and orientation by adding a virtual agent within the system. A simulation study supports and validates the theoretical results.

This paper proposes the combination of a fast motion planner, which generates potentially unfeasible trajectories for the center of mass of an underactuated UAV, with a tracking controller integrating a Control Barrier Function. In this way avoidance of both stationary and moving obstacles is guaranteed even when the UAV deviates from the collision-free trajectory issued by the planner, due to its unfeasibility. Simulations in a dynamic and cluttered environment validate the method.

Given a system of overactuated heterogeneous robots executing several tasks, we propose an algorithm that simultaneously assigns the tasks considering the different nature of the robots, and allocates the control while satisfying the limitations on the actuators. The efficiency of the proposed method has been tested in terms of robustness with respect to faults by means of numerical simulations, analysing different scenarios.

This paper focuses on the so-called sensorless speed-tracking control for permanent magnet synchronous motors. A control strategy is proposed allowing to extract rotor position using electrical signals and an observer of electrical variables, and to guarantee the robust asymptotical tracking of a reference speed without measuring rotor velocity and position. Bounded parameter variations are supposed to affect the mechanical system, and a (possibly time-varying) uncertain load torque is considered. Reported experimental evidence shows the effectiveness of the proposed DSP-based control policy.

This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.

In this paper, we study the minimum length paths covered by the center of a unicycle equipped with a limited field-of-view (FOV) camera, which must keep a given landmark in sight. Previous works on this subject have provided the optimal synthesis for the cases in which the FOV is only limited in the horizontal directions (i.e., left and right bounds) or in the vertical directions (i.e., upper and lower bounds). In this paper, we show how to merge previous results and hence obtain, for a realistic image plane modeled as a rectangle, a finite alphabet of extremal arcs and the overall synthesis. As shown, this objective cannot be straightforwardly achieved from previous results but needs further analysis and developments. Moreover, there are initial configurations such that there exists no optimal path. Nonetheless, we are always able to provide an $\varepsilon$-optimal path whose length approximates arbitrarily well any other shorter path. As final results, we provide a partition of the motion plane in regions such that the optimal or $\varepsilon$ -optimal path from each point in that region is univocally determined.