The selective travelling salesman problem
Discrete Applied Mathematics - Southampton conference on combinatorial optimization, April 1987
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
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
Data gathering tours in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Deploying wireless sensors to achieve both coverage and connectivity
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Algorithms for subset selection in linear regression
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Efficient planning of informative paths for multiple robots
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Approximating sensor network queries using in-network summaries
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
Efficient informative sensing using multiple robots
Journal of Artificial Intelligence Research
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
A parametric POMDP framework for efficient data acquisition in error prone wireless sensor networks
ISWPC'09 Proceedings of the 4th international conference on Wireless pervasive computing
Quality assurance for data acquisition in error prone WSNs
ICUFN'09 Proceedings of the first international conference on Ubiquitous and future networks
Robust sensor placements at informative and communication-efficient locations
ACM Transactions on Sensor Networks (TOSN)
Bounded approximate decentralised coordination via the max-sum algorithm
Artificial Intelligence
A cooperative architecture for target localization using multiple AUVs
Intelligent Service Robotics
Near-optimal continuous patrolling with teams of mobile information gathering agents
Artificial Intelligence
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In many sensing applications we must continuously gather information to provide a good estimate of the state of the environment at every point in time. A robot may tour an environment, gathering information every hour. In a wireless sensor network, these tours correspond to packets being transmitted. In these settings, we are often faced with resource restrictions, like energy constraints. The users issue queries with certain expectations on the answer quality. Thus, we must optimize the tours to ensure the satisfaction of the user constraints, while at the same time minimize the cost of the query plan. For a single timestep, this optimization problem is NP-hard, but recent approximation algorithms with theoretical guarantees provide good solutions. In this paper, we present a new efficient algorithm, exploiting dynamic programming and submodularity of the information collected, that efficiently plans data collection tours for an entire (finite) horizon. Our algorithm can use any single step procedure as a black box, and, based on its properties, provides strong theoretical guarantees for the solution. We also provide an extensive empirical analysis demonstrating the benefits of nonmyopic planning in two real world sensing applications.