TY - GEN
T1 - Frontier-based RTDP
T2 - 14th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015
AU - Yehoshua, Roi
AU - Agmon, Noa
AU - Kaminka, Gal A.
N1 - Publisher Copyright: Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
PY - 2015
Y1 - 2015
N2 - Area coverage is an important problem in robotics, where one or more robots axe required to visit all points in a given area. In this paper we consider a recently introduced version of the problem, adversarial coverage, in which the covering robot operates in an environment that contains threats that might stop it. The objective is to cover the target area as quickly as possible, while minimizing the probability that the robot will be stopped before completing the coverage. We first model this problem as a Markov Decision Process (MDP), and show that finding an optimal policy of the MDP also provides an optimal solution to this problem. Since the state space of the MDP is exponential in the size of the target area's map, we use real-time dynamic programming (RTDP), a well-known heuristic search algorithm for solving MDPs with large state spaces. Although RTDP achieves faster convergence than value iteration on this problem, practically it cannot handle maps with sizes larger them 7×7. Hence, we introduce the use of frontiers, states that separate the covered regions in the search space from those uncovered, into RTDP. Frontier-Based RTDP (FBRTDP) converges orders of magnitude faster than RTDP, and obtains significant improvement over the state-of-the-art solution for the adversarial coverage problem.
AB - Area coverage is an important problem in robotics, where one or more robots axe required to visit all points in a given area. In this paper we consider a recently introduced version of the problem, adversarial coverage, in which the covering robot operates in an environment that contains threats that might stop it. The objective is to cover the target area as quickly as possible, while minimizing the probability that the robot will be stopped before completing the coverage. We first model this problem as a Markov Decision Process (MDP), and show that finding an optimal policy of the MDP also provides an optimal solution to this problem. Since the state space of the MDP is exponential in the size of the target area's map, we use real-time dynamic programming (RTDP), a well-known heuristic search algorithm for solving MDPs with large state spaces. Although RTDP achieves faster convergence than value iteration on this problem, practically it cannot handle maps with sizes larger them 7×7. Hence, we introduce the use of frontiers, states that separate the covered regions in the search space from those uncovered, into RTDP. Frontier-Based RTDP (FBRTDP) converges orders of magnitude faster than RTDP, and obtains significant improvement over the state-of-the-art solution for the adversarial coverage problem.
KW - Adversarial coverage
KW - Markov Decision Process
KW - Mobile robot coverage
KW - Motion and path planning
KW - Real-time dynamic programming
UR - http://www.scopus.com/inward/record.url?scp=84945199141&partnerID=8YFLogxK
M3 - منشور من مؤتمر
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 861
EP - 869
BT - AAMAS 2015 - Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems
A2 - Elkind, Edith
A2 - Weiss, Gerhard
A2 - Yolum, Pinar
A2 - Bordini, Rafael H.
Y2 - 4 May 2015 through 8 May 2015
ER -