Semantic and Terrain-Aware Trajectory Optimization for Uniform Coverage in Obstacle-Laden Environments

Abstract

Achieving efficient and uniform coverage in obstacle-laden unknown environments is essential for au- tonomous robots in cleaning, inspection and agricultural op- erations. Unlike most existing approaches that prioritize path length and time optimality, we propose the SHIFT planner framework, which integrates semantic mapping, adaptive cov- erage planning, and real-time obstacle avoidance to ensure comprehensive coverage across diverse terrains and seman- tic features. We first develop an innovative Radiant-Field- Informed Coverage Planning (RFICP) algorithm, which gen- erates trajectories that adapt to terrain variations. A Gaussian diffusion field is employed to adaptively adjust the robot’s speed, ensuring efficient coverage under varying environmental conditions influenced by semantic attributes. Next, we present a novel incremental KD-tree sliding window optimization (IKD- SWOpt) method to effectively handle unforeseen obstacles. IKD-SWOpt leverages an enhanced A* algorithm guided by the IKD-tree distance field to generate initial local avoidance tra- jectories. Subsequently, it optimizes trajectory segments within and outside waypoint safety zones by evaluating and refining non-compliant segments using an adaptive sliding window. This method not only reduces computational overhead but also guarantees high-quality real-time obstacle avoidance. Extensive experiments were conducted using drones in simulated envi- ronments and robotic vacuum cleaners in real-world settings.