The selective travelling salesman problem
Discrete Applied Mathematics - Southampton conference on combinatorial optimization, April 1987
The prize collecting Steiner tree problem: theory and practice
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Approximation Algorithms for Orienteering and Discounted-Reward TSP
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Traveling Salesman Problems with Profits
Transportation Science
A Recursive Greedy Algorithm for Walks in Directed Graphs
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Near-optimal sensor placements in Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Near-optimal sensor placements: maximizing information while minimizing communication cost
Proceedings of the 5th international conference on Information processing in sensor networks
Trajectory Optimization using Reinforcement Learning for Map Exploration
International Journal of Robotics Research
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
The Journal of Machine Learning Research
Adaptive multi-robot wide-area exploration and mapping
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Efficient Multi-robot Search for a Moving Target
International Journal of Robotics Research
Nonmyopic informative path planning in spatio-temporal models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Near-optimal observation selection using submodular functions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Efficient informative sensing using multiple robots
Journal of Artificial Intelligence Research
Optimal value of information in graphical models
Journal of Artificial Intelligence Research
Efficient viewpoint assignment for urban texture documentation
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Decentralised coordination of mobile sensors using the max-sum algorithm
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Nonmyopic adaptive informative path planning for multiple robots
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multiscale sensing with stochastic modeling
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Decision-theoretic robot guidance for active cooperative perception
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Information-based exploration strategy for mobile robot in dynamic environment
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Improving the Efficiency of Clearing with Multi-agent Teams
International Journal of Robotics Research
Robust sensor placements at informative and communication-efficient locations
ACM Transactions on Sensor Networks (TOSN)
Distributed robotic sensor networks: An information-theoretic approach
International Journal of Robotics Research
Near-optimal continuous patrolling with teams of mobile information gathering agents
Artificial Intelligence
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In many sensing applications, including environmental monitoring, measurement systems must cover a large space with only limited sensing resources. One approach to achieve required sensing coverage is to use robots to convey sensors within this space. Planning the motion of these robots - coordinating their paths in order to maximize the amount of information collected while placing bounds on their resources (e.g., path length or energy capacity) - is aNP-hard problem. In this paper, we present an efficient path planning algorithm that coordinates multiple robots, each having a resource constraint, to maximize the "informativeness" of their visited locations. In particular, we use a Gaussian Process to model the underlying phenomenon, and use the mutual information between the visited locations and remainder of the space to characterize the amount of information collected. We provide strong theoretical approximation guarantees for our algorithm by exploiting the submodularity property of mutual information. In addition, we improve the efficiency of our approach by extending the algorithm using branch and bound and a region-based decomposition of the space. We provide an extensive empirical analysis of our algorithm, comparing with existing heuristics on datasets from several real world sensing applications.