SHIFT Planner

Achieving uniform coverage in dynamic environments is essential for autonomous robots tasked with cleaning, inspection, and agricultural operations. However, existing approaches often prioritize path length and time optimality, neglecting environmental attributes such as terrain, dirtiness, dryness, and varying elevations in coverage trajectory planning. To address this limitation, we propose the SHIFT Planner framework, which integrates semantic mapping, adaptive coverage planning, and real-time obstacle avoidance to enable comprehensive coverage across diverse terrains and semantic attributes. Key innovations include:

• Radiant Field-Informed Coverage Planning (RFICP) algorithm: Generates trajectories that adapt to terrain variations by aligning with environmental changes. Additionally, a Gaussian diffusion field is employed to ensure efficient and uniform coverage under varying environmental conditions influenced by target semantic attributes.
• Incremental IKD-tree Sliding Window Optimization (IKD-SWOpt): Optimizes trajectory segments within and outside waypoint safety zones by evaluating and refining non-compliant segments through an adaptive sliding window approach. This method not only reduces computational overhead but also guarantees the quality of real-time obstacle avoidance trajectories.

RFICP: Surface Extraction

We use a differential geometry approach to fit a smooth surface to the sensor-derived point cloud, then compute and filter curvature metrics (Gaussian and mean curvatures) to remove noise, slender obstacles, or protrusions that do not contribute to meaningful coverage. This yields a continuous surface representation that can be efficiently queried for elevation values during path planning.

A smooth parametric surface ready for elevation extraction

RFICP: Gaussian Diffusion Kernel

RFICP: Speed Adaptive Coverage Path

RFICP: Boustrophedon Coverage Path

Sequence the adjusted landmark points in a boustrophedon (zigzag) pattern to ensure systematic and complete coverage of the terrain.

RFICP Pipeline

RFICP generates a terrain-conforming coverage trajectory with explicit time and speed modulation based on environmental semantics.

Circular Judgement and Sliding Window

Illustration of Circular Judgement in a 2D environment. For each waypoint, a circular region is defined. We compute a safety score based on obstacle proximity and trajectory continuity. If the score is below a threshold, we perform a sliding window optimization.

3D Optimization Animation

Optimization process of trajectory segments within the sliding window of the IKD-SWOpt framework.

Results on 3D and 2D Path

Illustration of the SHIFT Planner path optimization. The red dashed line represents the initial path, blue represents the optimized path, and green represents the smooth path.