Informative Path Planning for Autonomous Mapping of Unknown Non-Convex Environments: Design, Benchmarking, and Validation

Rapid exploration of unknown environments is critical in engineering applications such as disaster response and autonomous inspection. This paper presents an informative path planning approach for autonomous mapping of fully unknown, non-convex environments using a mobile robot with an uncertain narrow-beam range sensor. The artificial intelligence contribution lies in approximating the global optimal exploration solution under uncertainty using a sequential decision-making algorithm. The engineering contribution is the formulation and introduction of a benchmark solution, and the validation of the proposed algorithm against this benchmark through simulation and real-world experiments. Results show that the method achieves approximately 70% of the benchmark efficiency, measured as map expansion per unit distance travelled, with near-linear map growth. Sensitivity analysis demonstrates robust performance under varying initial conditions, confirming its applicability for real-world autonomous robotic systems.