To Örebro University

Multirobot fleets play an important role in industrial logistics, surveillance, and exploration applications. A wide literature exists on the topic, both resorting to reactive (i.e. collision avoidance) and to deliberative (i.e. motion planning) techniques. In this work, null space-based inverse kinematics (NSB-IK) methods are applied to the problem of fleet management. Several NSB-IK approaches existing in the literature are reviewed, and compared with a reverse priority approach, which originated in manipulator control, and is here applied for the first time to the considered problem. All NSB-IK approaches are here described in a unified formalism, which allows (i) to encode the property of each controller into a set of seven main key features, (ii) to study possible new control laws with an opportune choice of these parameters. Furthermore, motivated by the envisioned application scenario, we tackle the problem of task-switching activation. Leveraging on the iCAT TPC technique Simetti and Casalino, 2016, in this article, we propose a method to obtain continuity in the control in face of activation or deactivation of tasks, and subtasks by defining suitable damped projection operators. The proposed approaches are evaluated formally, and via simulations. Performances with respect to standard methods are compared considering a specific case study for multivehicles management.

A standing challenge in multirobot systems is to realize safe and efficient motion planning and coordination methods that are capable of accounting for uncertainties and contingencies. The challenge is rendered harder by the fact that robots may be heterogeneous and that their plans may be posted asynchronously. Most existing approaches require constraints on the infrastructureor unrealistic assumptions on robot models. In this article, we propose a centralized, loosely-coupled supervisory controller that overcomes these limitations. The approach responds to newly posed constraints and uncertainties during trajectory execution, ensuring at all times that planned robot trajectories remain kinodynamically feasible, that the fleet is in a safe state, and that there are no deadlocks or livelocks. This is achieved without the need for hand-coded rules, fixed robot priorities, or environment modification. We formally state all relevant properties of robot behavior in the most general terms possible, without assuming particular robot models or environments, and provide both formal and empirical proof that the proposed fleet control algorithms guarantee safety and liveness.

We propose a loosely-coupled framework for integrated task assignment, motion planning, coordination and contro of heterogeneous fleets of robots subject to non-cooperative tasks. The approach accounts for the important real-world requiremen that tasks can be posted asynchronously. We exploit systematic search for optimal task assignment, where interference is considered as a cost and estimated with knowledge of the kinodynamic models and current state of the robots. Safety is guaranteed by an online coordination algorithm, where the absence of collisions is treated as a hard constraint. The relation between the weight of interference cost in task assignment and computational overhead is analyzed empirically, and the approach is compared against alternative realizations using local search algorithms for task assignment.

A standing challenge in current intralogistics is to reliably, effectively yet safely coordinate large-scale, heterogeneous multi-robot fleets without posing constraints on the infrastructure or unrealistic assumptions on robots. A centralized approach, proposed by some of the authors in prior work, allows to overcome these limitations with medium-scale fleets (i.e., tens of robots). With the aim of scaling to hundreds of robots, in this paper we explore a de-centralized variant of the same approach. The proposed framework maintains the key features of the original approach, namely, ensuring safety despite uncertainties on robot motions, and generality with respect to robot platforms, motion planners and controllers. We include considerations on liveness and solutions to prevent or recover from deadlocks in specific situations are reported and discussed. We validate the approach empirically with simulated, large, heterogeneous multi-robot fleets (up to 100 robots tested) operating both in benchmark and realistic environments.

This paper presents a robotics laboratory originated by the collaboration between the university and high school within the Italian school-work transition program. The educational objective of the proposed lab is twofold: 1) ease the transfer of robotic researchers’ expertise into useful means for the students’ learning; 2) teaching by practice the multidisciplinarity of robotics. We exploited the RoboCup Junior Race as a useful scenario to cover topics from 3D printing for fast prototyping to low-level and high-level controller design. An ad-hoc end-of-term student survey confirms the effectiveness of the approach. Finally, the paper includes some considerations on how general problems in the robotic and scientific community, such as gender issues and COVID-19 restrictions, can impact the educational robotics activities.

Warehouse mobile robotics is nowadays entering the mass-production market. Increasing the number of mobile robots up to decades raises new challenges: current industrial practice relies on centralized fleet management, which might hinder efficacy in the case of large fleets. This paper proposes and discusses a partially and a fully distributed extension of a centralized loosely coupled algorithm for multi-robot coordination. In particular, we aim at investigating: 1) how coordination can be distributed among robots, and 2) which is the minimum amount of local information required to enforce safety. Simulation results show that a partial distribution may improve performance in terms of arrival times while preserving safety and liveness.