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This paper addresses the Informative Path Planning (IPP) algorithm for autonomous robots to explore unknown 2D environments for mapping purposes. IPP can be beneficial to many applications such as search and rescue and cave exploration, where mapping an unknown environment is necessary. Autonomous robots' limited operation time due to their finite battery necessitates an efficient IPP algorithm, however, it is challenging because autonomous robots may not have any information about the environment. In this paper, we formulate a mathematical structure of the IPP problem along with the derivation of the optimal control input. Then, a discretized model for the IPP algorithm is presented as a solution for exploring an unknown environment. The proposed approach provides relatively fast computation time while being applicable to broad robot and sensor platforms. Various simulation results are provided to show the performance of the proposed IPP algorithm.
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Bayesian reinforcement learning for navigation planning in unknown environments
This study focuses on a rescue mission problem, particularly enabling agents/robots to navigate efficiently in unknown environments. Technological advances, including manufacturing, sensing, and communication systems, have raised interest in using robots or drones for rescue operations. Effective rescue operations require quick identification of changes in the environment and/or locating the victims/injuries as soon as possible. Several techniques have been developed in recent years for autonomy in rescue missions, including motion planning, adaptive control, and more recently, reinforcement learning techniques. These techniques rely on full knowledge of the environment or the availability of simulators that can represent real environments during rescue operations. However, in practice, agents might have little or no information about the environment or the number or locations of injuries, preventing/limiting the application of most existing techniques. This study provides a probabilistic/Bayesian representation of the unknown environment, which jointly models the stochasticity in the agent's navigation and the environment uncertainty into a vector called the belief state. This belief state allows offline learning of the optimal Bayesian policy in an unknown environment without the need for any real data/interactions, which guarantees taking actions that are optimal given all available information. To address the large size of belief space, deep reinforcement learning is developed for computing an approximate Bayesian planning policy. The numerical experiments using different maze problems demonstrate the high performance of the proposed policy.
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- Award ID(s):
- 2311969
- PAR ID:
- 10596255
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Artificial Intelligence
- Volume:
- 7
- ISSN:
- 2624-8212
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/frai.2024.1308031
