Autonomous Cost-Effective Robotic Exploration and Mapping for Disaster Reconnaissance

Seenu, N; Manohar, Lokeshwaran; Stephen, Nikhit Mathew; Ramanathan, Kuppan Chetty; Ramya, M. M.

doi:10.1109/icetet-sip-2254415.2022.9791800

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…During the autonomous exploration process, robots, without any prior knowledge, rely only on their own sensors and exploration strategies and move through the unknown environment to build a map of the whole circumstance. Autonomous exploration has a wide range of real-life applications, such as the exploration and mapping of unknown environments by rescue robots [ 1 , 2 ], sweeping robots [ 3 , 4 ], and disaster reconnaissance robots [ 5 , 6 ]. In recent years, some remarkable techniques like frontier-based [ 7 , 8 ] and information-based [ 9 , 10 ] methods have emerged.…”

Section: Introductionmentioning

confidence: 99%

Efficient Autonomous Exploration and Mapping in Unknown Environments

Feng

Xie

Sun

et al. 2023

Sensors

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Autonomous exploration and mapping in unknown environments is a critical capability for robots. Existing exploration techniques (e.g., heuristic-based and learning-based methods) do not consider the regional legacy issues, i.e., the great impact of smaller unexplored regions on the whole exploration process, which results in a dramatic reduction in their later exploration efficiency. To this end, this paper proposes a Local-and-Global Strategy (LAGS) algorithm that combines a local exploration strategy with a global perception strategy, which considers and solves the regional legacy issues in the autonomous exploration process to improve exploration efficiency. Additionally, we further integrate Gaussian process regression (GPR), Bayesian optimization (BO) sampling, and deep reinforcement learning (DRL) models to efficiently explore unknown environments while ensuring the robot’s safety. Extensive experiments show that the proposed method could explore unknown environments with shorter paths, higher efficiencies, and stronger adaptability on different unknown maps with different layouts and sizes.

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