TY - GEN
T1 - Anytime planning of optimal schedules for a mobile sensing robot
AU - Yu, Jingjin
AU - Aslam, Javed
AU - Karaman, Sertac
AU - Rus, Daniela
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/11
Y1 - 2015/12/11
N2 - We study the problem in which a mobile sensing robot is tasked to travel among and gather intelligence at a set of spatially distributed points-of-interest (POIs). The quality of the information collected at a POI is characterized by some sensory (reward) function of time. With limited fuel, the robot must balance between spending time traveling to more POIs and performing time-consuming sensing activities at POIs to maximize the overall reward. In a dual formulation, the robot is required to acquire a minimum amount of reward with the least amount of time. We propose an anytime planning algorithm for solving these two NP-hard problems to arbitrary precision for arbitrary reward functions. The algorithm is effective on large instances with tens to hundreds of POIs, as demonstrated with an extensive set of computational experiments. Besides mobile sensor scheduling, our algorithm also applies to automation scenarios such as intelligent and optimal itinerary planning.
AB - We study the problem in which a mobile sensing robot is tasked to travel among and gather intelligence at a set of spatially distributed points-of-interest (POIs). The quality of the information collected at a POI is characterized by some sensory (reward) function of time. With limited fuel, the robot must balance between spending time traveling to more POIs and performing time-consuming sensing activities at POIs to maximize the overall reward. In a dual formulation, the robot is required to acquire a minimum amount of reward with the least amount of time. We propose an anytime planning algorithm for solving these two NP-hard problems to arbitrary precision for arbitrary reward functions. The algorithm is effective on large instances with tens to hundreds of POIs, as demonstrated with an extensive set of computational experiments. Besides mobile sensor scheduling, our algorithm also applies to automation scenarios such as intelligent and optimal itinerary planning.
KW - Approximation algorithms
KW - Approximation methods
KW - Computational modeling
KW - Planning
KW - Robot sensing systems
UR - http://www.scopus.com/inward/record.url?scp=84958163750&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958163750&partnerID=8YFLogxK
U2 - 10.1109/IROS.2015.7354122
DO - 10.1109/IROS.2015.7354122
M3 - Conference contribution
AN - SCOPUS:84958163750
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 5279
EP - 5286
BT - IROS Hamburg 2015 - Conference Digest
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015
Y2 - 28 September 2015 through 2 October 2015
ER -