Risk-Aware Informative Path Planning for Information Gathering of a 3D Surface

Surface information acquisition by robots faces challenges such as sensor uncertainty, limited resources, and dynamic environment, all of which often lead to reduced collection efficiency and accuracy. To address these issues, this paper proposes a Risk-aware Informative Path Planning (RIPP) framework. The framework is capable of adaptively selecting the target region according to the mutual information between the expected detection viewpoints. The uncertainty risk caused by noisy sensing can be effectively managed using the Conditional Value at Risk (CVaR)-based method. Therefore, a CVaR-based Greedy Algorithm (CGA) is proposed to select the optimal set of inspection viewpoints. To further enhance information acquisition efficiency, the drone’s path is optimized using a novel Adaptive Fractional Particle Swarm Optimization (AFPSO) algorithm. This approach enables the drone to autonomously select trajectories rich in high-value information. This framework is evaluated in the context of 3D surface temperature inspection of large storage tanks. Simulation and experimental results show that RIPP framework significantly reduces information reconstruction errors in tank surface inspections by demonstrating clear advantages over existing methods. The effectiveness and feasibility of RIPP framework in surface information acquisition task are verified, which provides a new solution for efficient monitoring in complex environment. Note to Practitioners—This study is to propose a risk-aware online information path planning method to enhance the efficiency of information collection by autonomous aerial vehicles (AAVs) under uncertainty-induced risks. Conventional information path planning methods typically overlook the correlation between observation viewpoints and fail to address the risks posed by uncertainties, focusing merely on uncertainty estimation. In contrast, the proposed method effectively manages risks associated with uncertainties and optimally plans AAV information collection paths. Performance evaluations demonstrate that the proposed approach outperforms several existing methods.