Coordinating a fleet of robots in unstructured, human-shared environments is challenging. Human behavior is hard to predict, and its uncertainty impacts the performance of the robotic fleet. Various multi-robot planning and coordination algorithms have been proposed, including Multi-Agent Path Finding (MAPF) methods to precedence-based algorithms. However, it is still unclear how human presence impacts different coordination strategies in both simulated environments and the real world. With the goal of studying and further improving multi-robot planning capabilities in those settings, we propose a method to develop and benchmark different multi-robot coordination algorithms in realistic, unstructured and human-shared environments. To this end, we introduce a multi-robot benchmark framework that is based on state-of-the-art open-source navigation and simulation frameworks and can use different types of robots, environments and human motion models. We show a possible application of the benchmark framework with two different environments and three centralized coordination methods (two MAPF algorithms and a loosely-coupled coordination method based on precedence constraints). We evaluate each environment for different human densities to investigate its impact on each coordination method. We also present preliminary results that show how informing each coordination method about human presence can help the coordination method to find faster paths for the robots.

For robots navigating in dynamic environments, exploiting and understanding uncertain human motion prediction is key to generate efficient, safe and legible actions. The robot may perform poorly and cause hindrances if it does not reason over possible, multi-modal future social interactions. With the goal of enhancing autonomous navigation in cluttered environments, we propose a novel formulation for nonlinear model predictive control including multi-modal predictions of human motion. As a result, our approach leads to less conservative, smooth and intuitive human-aware navigation with reduced risk of collisions, and shows a good balance between task efficiency, collision avoidance and human comfort. To show its effectiveness, we compare our approach against the state of the art in crowded simulated environments, and with real-world human motion data from the THOR dataset. This comparison shows that we are able to improve task efficiency, keep a larger distance to humans and significantly reduce the collision time, when navigating in cluttered dynamic environ-ments. Furthermore, the method is shown to work robustly with different state-of-the-art human motion predictors.

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 infrastructure or 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.