Spatially-distributed coverage optimization and control with limited-range interactions

This paper presents coordination algorithms for groups of mobile agents performing deployment and coverage tasks. As an important modeling constraint, we assume that each mobile agent has a limited sensing/communication radius. Based on the geometry of Voronoi partitions and proximity graphs, we analyze a class of aggregate objective functions and propose coverage algorithms in continuous and discrete time. These algorithms have convergence guarantees and are spatially distributed with respect to appropriate proximity graphs. Numerical simulations illustrate the results.Comment: 31 pages, some figures left out because of size limits. Complete preprint version available at http://motion.csl.uiuc.ed

Computing Convex Coverage Sets for Faster Multi-objective Coordination

In this article, we propose new algorithms for multi-objective coordination graphs (MO- CoGs). Key to the efficiency of these algorithms is that they compute a convex coverage set (CCS) instead of a Pareto coverage set (PCS). Not only is a CCS a sufficient solution set for a large class of problems, it also has important characteristics that facilitate more efficient solutions. We propose two main algorithms for computing a CCS in MO-CoGs. Convex multi-objective variable elimination (CMOVE) computes a CCS by performing a series of agent eliminations, which can be seen as solving a series of local multi-objective subproblems. Variable elimination linear support (VELS) iteratively identifies the single weight vector w that can lead to the maximal possible improvement on a partial CCS and calls variable elimination to solve a scalarized instance of the problem for w. VELS is faster than CMOVE for small and medium numbers of objectives and can compute an ε-approximate CCS in a fraction of the runtime. In addition, we propose variants of these methods that employ AND/OR tree search instead of variable elimination to achieve memory efficiency. We analyze the runtime and space complexities of these methods, prove their correctness, and compare them empirically against a naive baseline and an existing PCS method, both in terms of memory-usage and runtime. Our results show that, by focusing on the CCS, these methods achieve much better scalability in the number of agents than the current state of the art

Route Swarm: Wireless Network Optimization through Mobility

In this paper, we demonstrate a novel hybrid architecture for coordinating networked robots in sensing and information routing applications. The proposed INformation and Sensing driven PhysIcally REconfigurable robotic network (INSPIRE), consists of a Physical Control Plane (PCP) which commands agent position, and an Information Control Plane (ICP) which regulates information flow towards communication/sensing objectives. We describe an instantiation where a mobile robotic network is dynamically reconfigured to ensure high quality routes between static wireless nodes, which act as source/destination pairs for information flow. The ICP commands the robots towards evenly distributed inter-flow allocations, with intra-flow configurations that maximize route quality. The PCP then guides the robots via potential-based control to reconfigure according to ICP commands. This formulation, deemed Route Swarm, decouples information flow and physical control, generating a feedback between routing and sensing needs and robotic configuration. We demonstrate our propositions through simulation under a realistic wireless network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on Intelligent Robots and Systems (IROS) 201

Linear Support for Multi-Objective Coordination Graphs

Many real-world decision problems require making trade-offs among multiple objectives. However, in some cases, the relative importance of these objectives is not known when the problem is solved, precluding the use of single-objective methods. Instead, multi-objective methods, which compute the set of all potentially useful solutions, are required. This paper proposes variable elimination linear support (VELS), a new multi-objective algorithm for multi-agent coordina-tion that exploits loose couplings to compute the convex coverage set (CCS): the set of optimal solutions for all pos-sible weights for linearly weighted objectives. Unlike ex-isting methods, VELS exploits insights from POMDP solu-tion methods to build the CCS incrementally. We prove the correctness of VELS and show that for moderate numbers of objectives its complexity is better than that of previous methods. Furthermore, we present empirical results showing that VELS can tackle both random and realistic problems with many more agents than was previously feasible. The incremental nature of VELS also makes it an anytime al-gorithm, i.e., its intermediate results constitute ε-optimal approximations of the CCS, with ε decreasing the longer it runs. Our empirical results show that, by allowing even very small ε, VELS can enable large additional speedups