Distributed Multi-Robot Formation Control among Obstacles: A Geometric and Optimization Approach with Consensus

This paper presents a distributed method for navigating a team of robots in formation in 2D and 3D environments with static and dynamic obstacles. The robots are assumed to have a reduced communication and visibility radius and share information with their neighbors. Via distributed consensus the robots compute (a) the convex hull of the robot positions and (b) the largest convex region within free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation within this convex neighborhood of the robots. Reconfiguration is allowed, when required, by considering a set of target formations. The robots navigate towards the target collision-free formation with individual local planners that account for their dynamics. The approach is efficient and scalable with the number of robots and performs well in simulations with up to sixteen quadrotors.United States. Office of Naval Research (pDOT N00014-12-1-1000)United States. Army Research Laboratory (Grant W911NF-08-2-0004)Boeing CompanySingapore-MIT Alliance for Research and Technology Center (Future of Urban Mobility Project)Spanish Government (Project DPI2012-32100)Spanish Government (Project DPI2015-69376-R)Spanish Government (Project CUD2013-05)Spanish Government (Grant CAS14/00205

Distributed Algorithms for Stochastic Source Seeking With Mobile Robot Networks

Autonomous robot networks are an effective tool for monitoring large-scale environmental fields. This paper proposes distributed control strategies for localizing the source of a noisy signal, which could represent a physical quantity of interest such as magnetic force, heat, radio signal, or chemical concentration. We develop algorithms specific to two scenarios: one in which the sensors have a precise model of the signal formation process and one in which a signal model is not available. In the model-free scenario, a team of sensors is used to follow a stochastic gradient of the signal field. Our approach is distributed, robust to deformations in the group geometry, does not necessitate global localization, and is guaranteed to lead the sensors to a neighborhood of a local maximum of the field. In the model-based scenario, the sensors follow a stochastic gradient of the mutual information (MI) between their expected measurements and the expected source location in a distributed manner. The performance is demonstrated in simulation using a robot sensor network to localize the source of a wireless radio signal

Optimal role and position assignment in multi-robot freely reachable formations

Many multi-robot problems require the achievement of formations as part of the overall mission. This work considers a scenario in which unlabeled homogeneous robots must adopt a given formation pattern buildable anywhere in the environment. This involves finding the relative pose of the formation in regard to the initial robot positions, understood as a translation and a rotation; and the optimal assignment of the role of each robot within the formation. This paper provides an optimal solution for the combined parameters of translation, rotation and assignment that minimizes total displacement. To achieve this objective we first formally prove that the three decision variables are separable. Since computing the optimal assignment without accounting for the rotation is a computationally expensive problem, we propose an algorithm that efficiently computes the optimal roles together with the rotation. The algorithm is provably correct and finds the optimal solution in finite time. A distributed implementation is also discussed. Simulation results characterize the complexity of our solution and demonstrate its effectiveness

A Tutorial on Distributed Optimization for Cooperative Robotics: from Setups and Algorithms to Toolboxes and Research Directions

Several interesting problems in multi-robot systems can be cast in the framework of distributed optimization. Examples include multi-robot task allocation, vehicle routing, target protection and surveillance. While the theoretical analysis of distributed optimization algorithms has received significant attention, its application to cooperative robotics has not been investigated in detail. In this paper, we show how notable scenarios in cooperative robotics can be addressed by suitable distributed optimization setups. Specifically, after a brief introduction on the widely investigated consensus optimization (most suited for data analytics) and on the partition-based setup (matching the graph structure in the optimization), we focus on two distributed settings modeling several scenarios in cooperative robotics, i.e., the so-called constraint-coupled and aggregative optimization frameworks. For each one, we consider use-case applications, and we discuss tailored distributed algorithms with their convergence properties. Then, we revise state-of-the-art toolboxes allowing for the implementation of distributed schemes on real networks of robots without central coordinators. For each use case, we discuss their implementation in these toolboxes and provide simulations and real experiments on networks of heterogeneous robots