Target-Aware Viewpoint Generation for Active Robotic Exploration in Unknown Environments

When entering an unfamiliar environment, animals usually sweep off their surroundings to identify points of interest. In search and rescue robotics, autonomous exploration requires both coarse mapping of unknown areas and detailed target detection, which poses a significant challenge in balancing these tasks. To that end, we propose a target-aware robotic exploration framework that prioritizes both exploration efficiency and search completeness through three components: First, considering the computational limitations of robotic platforms, a lightweight 3D target detection method with post-fusion is introduced to detect target positions in real time. Secondly, we propose a target-aware viewpoint generation approach that integrates information gain and inspection gain to identify promising viewpoints for thorough target searches. Lastly, since a detailed examination of the environment demands numerous viewpoints, we propose a heuristic-based active exploration framework that employs a hierarchical structure to optimize exploration gain, traveling distance, and path smoothness to maximize the utility function of viewpoint sequences and ultimately find the optimal path. Extensive simulations and real-world experiments demonstrate our framework significantly enhances target search capabilities, achieving a 13 % average improvement in exploration efficiency over existing methods.