Improving Continuous Coverage Path Planning through Subpath Selection and Multi-Objective Bilevel Optimization

Coverage Path Planning (CPP) is a fundamental problem in robotics with diverse applications, including area surveillance or search and rescue. The weighted continuous CPP problem extends the classical CPP by focusing on maximizing the coverage of a value function using continuous and free-form paths. This extension introduces unique challenges, particularly the computational effort required for equidistant path evaluation and the difficulty of achieving locally optimal paths. To address these challenges, this work proposes a bilevel optimization approach. In this framework, the upper-level decision-maker (DM) solves the continuous CPP problem, while the lower-level DM periodically optimizes sub-elements of the solutions. By leveraging this hierarchical structure, the proposed approach aims to achieve improved computational efficiency and enhanced adaptation to local scenario variations.