Planning for opportunistic surveillance with multiple robots

Abstract

Abstract-We are interested in the multiple robot surveillance problem where robots must allocate waypoints to be visited among themselves and plan paths through different waypoints while avoiding obstacles. Furthermore, the robots are allocated specific times to reach their respective goal locations and as a result they have to decide which robots have to visit which waypoints. Such a problem has the challenge of computing the allocation of waypoints across robots, ordering for these waypoints and dynamical feasibility of the paths between waypoints. We present an algorithm that runs a series of graph searches to solve the problem and provide theoretical analysis that our approach yields an optimal solution. We present simulated results as well as experiments on two UAVs that validate the capability of our algorithm. For a single robot, we can solve instances having 10-15 waypoints and for multiple robots, instances having five robots and 10 waypoints can be solved.

Using Pre-Computed Knowledge for Goal Allocation in Multi-Agent Planning

“…Another similar problem is the Team Orienteering Problem, where robots maximize the number of covered waypoints visited, with constraints on the total travel time of each robot. In order to solve this problem and optimize the overall planning time, an algorithm was proposed that uses a three-tier graph, interleaving the search for optimal waypoint assignment, ordering of the waypoints and also considering feasible paths between waypoints while avoiding obstacles [33]. This algorithm guarantees optimal solutions, while in our work we focus on sub-optimal planning for multi-robot teams.…”

Study, design, and evaluation of exploration strategies for autonomous mobile robots

“…In an even broader perspective, a future objective is to develop a framework for a more general exploration problem where robots can "explore" other features of the environment, including its physical quantities, like in environmental monitoring (Parker, Coogle, and Howard, 2013;Smith, Schwager, Smith, Rus, and Sukhatme, 2011), surveillance (Carpin, Burch, Basilico, Chung, & Kölsch, 2013;Thakur et al, 2013), olfactory exploration (Marjovi & Marques, 2013), and tactile exploration (Pezzementi, Plaku, Reyda, & Hager, 2011), so that the development of a robotic system is eased.…”

State lattice with controllers: Augmenting lattice-based path planning with controller-based motion primitives

“…For example, a controller for FOLLOWLEFTWALL may have a trigger OPENINGONLEFT, whereas that trigger may not be valid for a controller performing VISUALSER-VOTOLANDMARK. 1 We classify triggers into two categories: intrinsic or extrinsic. Intrinsic triggers result from the natural completion of a controller.…”

“…For example, aerial vehicles [1], automobiles [2], boats [3], and all-terrain vehicles [4] have all used state lattices for navigation. These graphs are constructed by applying a set of motion primitives to each state expanded during the search in order to generate valid successor states.…”