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Publications

2018

Heterogeneous Multi-Agent Planning Using Actuation Maps

Authors
Pereira, T; Luis, N; Moreira, A; Borrajo, D; Veloso, M; Fernandez, S;

Publication
2018 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC)

Abstract
Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach that considers in the same search space all combinations of robots and goals could lead to inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides a good framework to solve this kind of tasks efficiently. Some MAP techniques have proposed to previously assign goals to agents (robots) so that the planning effort decreases. However, these techniques do not scale when the number of agents and goals grow, as in most real world scenarios with big maps or goals that cannot be reached by subsets of robots. In this paper we propose to help the computation of which goals should be assigned to each agent by using Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. They help on alleviating the effort of MAP techniques knowing which goals can be tackled by each agent, as well as cheaply estimating the cost of using each agent to achieve every goal. Experiments show that when information extracted from AMs is provided to the Multi Agent planner, goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning.

2017

Multi-Robot Planning for Perception of Multiple Regions of Interest

Authors
Pereira, T; Mendes Moreira, APG; Veloso, MM;

Publication
ROBOT 2017: Third Iberian Robotics Conference - Volume 1, Seville, Spain, November 22-24, 2017

Abstract
In this paper we address the allocation of perception tasks among a set of multiple robots, for tasks such as inspection, surveillance, or search in structured environments. We consider a set of target regions of interest in a mapped environment that need to be sensed by any of the robots, and the problem is to find paths for the robots that cover all the target regions with minimal cost. We consider not only sensing range when determining paths for the robots to perceive the targets, but also a sensor cost function that can be adapted to each robot’s sensor. Thus the planning has to search for paths with minimal motion and perception cost, instead of the traditional approach where line-of-sight is the only requirement in a motion cost minimization problem. Our contribution is to use planning to determine possible perception positions for every robot, which we cluster and then use as possible waypoints that can be used to construct paths for all the robots. Given the combinatorial characteristics of path determination in this setting, we contribute a construction heuristic to find paths that guarantee full coverage of all the feasible perception target regions, while minimizing the overall cost. We assume robots are heterogeneous regarding their geometric properties, such as size and maximum perception range. We consider simulated scenarios where we show the benefits of our approach, enabling multi-robot path planning for perception of multiple regions of interest. © Springer International Publishing AG 2018.

2016

Multi-robot Planning Using Robot-Dependent Reachability Maps

Authors
Pereira, T; Veloso, M; Moreira, A;

Publication
ROBOT 2015: SECOND IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, VOL 1

Abstract
In this paper we present a new concept of robot-dependent reachability map (RDReachMap) for mobile platforms. In heterogeneous multi-robot systems, the reachability limit of robots motion and actuation must be considered when assigning tasks to them. We created an algorithm that generates those reachability maps, separating regions that can be covered by a robot from the unreachable ones, using morphological operations. Our method is dependent on the robot position, and is parameterized with the robot's size and actuation radius. For this purpose we introduce a new technique, the partial morphological closing operation. The algorithm was tested both in simulated and real environment maps. We also present a common problem of multi robot routing, which we solve with a planner that uses our reachability maps in order to generate valid plans. We contribute a heuristic that generates paths for two robots using the reachability concept.

2016

Visibility Maps for Any-Shape Robots

Authors
Pereira, T; Veloso, M; Moreira, A;

Publication
2016 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2016)

Abstract
We introduce in this paper visibility maps for robots of any shape, representing the reachability limit of the robot's motion and sensing in a 2D gridmap with obstacles. The brute-force approach to determine the optimal visibility map is computationally expensive, and prohibitive with dynamic obstacles. We contribute the Robot-Dependent Visibility Map (RDVM) as a close approximation to the optimal, and an effective algorithm to compute it. The RDVM is a function of the robot's shape, initial position, and sensor model. We first overview the computation of RDVM for the circular robot case in terms of the partial morphological closing operation and the optimal choice for the critical points position. We then present how the RDVM for any-shape robots is computed. In order to handle any robot shape, we introduce in the first step multiple layers that discretize the robot orientation. In the second step, our algorithm determines the frontiers of actuation, similarly to the case of the the circular robot case. We then derive the concept of critical points to the any-shape robot, as the points that maximize expected visibility inside unreachable regions. We compare our method with the ground-truth in a simulated map compiled to capture a variety of challenges of obstacle distribution and type, and discuss the accuracy of our approximation to the optimal visibility map.

2016

PA*: Optimal Path Planning for Perception Tasks

Authors
Pereira, T; Veloso, M; Moreira, A;

Publication
ECAI 2016: 22ND EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE

Abstract
In this paper we introduce the problem of planning for perception of a target position. Given a sensing target, the robot has to move to a goal position from where the target can be perceived. Our algorithm minimizes the overall path cost as a function of both motion and perception costs, given an initial robot position and a sensing target. We contribute a heuristic search method, PA*, that efficiently searches for an optimal path. We prove the proposed heuristic is admissible, and introduce a new goal state stopping condition.