BATO: Bayesian Trajectory Optimization for Active Environmental Mapping Using Autonomous Robots

Le résumé fourni par la source

Active mapping is a critical capability for robotic exploration tasks such as field rescue and environmental monitoring, where the objective is to plan informative trajectories that maximize environmental information acquisition under resource constraints in unknown environments. However, existing approaches often suffer from myopic planning and inefficient evaluation of informative trajectories. To address these challenges, we propose a Bayesian trajectory optimization (BATO) framework that integrates global trajectory optimization with local path smoothing for online active mapping. Specifically, we formulate the informative trajectory optimization problem as a heuristic search for target poses and leverage an efficient kernel-based Bayesian optimization (BO) approach to generate optimal pose suggestions over multiple epochs. Our BO method incorporates previously unexplored informative poses to improve solution optimality and introduces a novel information-theoretic objective function that effectively balances exploration and exploitation. Comprehensive evaluations in Gazebo simulations and real-world experiments demonstrate that BATO outperforms state-of-the-art methods in unstructured and cluttered environments, achieving superior active mapping performance.

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