Multi-Target Coverage Trajectory Planning for Ceiling Painting Robot Chassis via Two-Stage Optimization

Abstract

Ceiling painting is an essential yet labor-intensive task in the construction industry, making automation necessary to overcome issues such as inconsistent manual quality and associated health hazards. This paper addresses the multi-target coverage trajectory planning problem for ceiling painting robots operating in complex indoor environments, aiming to generate efficient, collision-free, and kinematically feasible trajectories that fully cover all designated peeling areas. The planning problem is first formulated as a mixed-integer optimal control model and then discretized into a mixed-integer nonlinear program. Conventional solvers struggle with prohibitive computational complexity due to integer variables and non-convex constraints arising from kinematic feasibility, collision avoidance, and coverage requirements. To overcome these computational challenges, we propose a two-stage planning structure. In the first stage, the planner constructs a coarse trajectory by integrating waypoint clustering, probabilistic roadmap-based collision-free path planning, and Traveling Salesman Problem optimization. The second stage addresses the non-convexity of the coverage constraints through an alternating optimization approach, iteratively fixing integer and continuous variables to achieve convexification and refine the trajectory. To further improve computational efficiency and scalability, the trajectory is segmented and each segment optimized independently. Simulation results demonstrate the effectiveness and reliability of the proposed planner, highlighting significant reductions in computational time compared with baseline methods.