Artificial Intelligence
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
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
Multi-robot adaptive exploration and mapping for environmental sensing applications
Multi-robot adaptive exploration and mapping for environmental sensing applications
Active Markov information-theoretic path planning for robotic environmental sensing
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Active Markov information-theoretic path planning for robotic environmental sensing
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part II
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Recent research in multi-robot exploration and mapping has focused on sampling environmental fields, which are typically modeled using the Gaussian process (GP). Existing information-theoretic exploration strategies for learning GP-based environmental field maps adopt the non-Markovian problem structure and consequently scale poorly with the length of history of observations. Hence, it becomes computationally impractical to use these strategies for in situ, realtime active sampling. To ease this computational burden, this paper presents a Markov-based approach to efficient information-theoretic path planning for active sampling of GP-based fields. We analyze the time complexity of solving the Markov-based path planning problem, and demonstrate analytically that it scales better than that of deriving the non-Markovian strategies with increasing length of planning horizon. For a class of exploration tasks called the transect sampling task, we provide theoretical guarantees on the active sampling performance of our Markov-based policy, from which ideal environmental field conditions and sampling task settings can be established to limit its performance degradation due to violation of the Markov assumption. Empirical evaluation on real-world temperature and plankton density field data shows that our Markov-based policy can generally achieve active sampling performance comparable to that of the widely-used non-Markovian greedy policies under less favorable realistic field conditions and task settings while enjoying significant computational gain over them.