Smooth coverage path planning and control of mobile robots based on high-resolution grid map representation

This paper presents a new approach to a time and energy efficient online complete coverage solution for a mobile robot. While most conventional approaches strive to reduce path overlaps, this work focuses on smoothing the coverage path to reduce accelerations and yet to increase the average velocity for faster coverage. The proposed algorithm adopts a high-resolution grid map representation to reduce directional constraints on path generation. Here, the free space is covered by three independent behaviors: spiral path tracking, wall following control, and virtual wall path tracking. Regarding the covered region as a virtual wall, all the three behaviors adopt a common strategy of following the (physical or virtual) wall or obstacle boundaries for close coverage. Wall following is executed by a sensor-based reactive path planning control process, whereas the spiral (filling) path and virtual wall path are first modeled by their relevant parametric curves and then tracked via dynamic feedback linearization. For complete coverage, these independent behaviors are linked through a new path linking strategy, called a coarse-to-fine constrained inverse distance transform (CFCIDT). CFCIDT reduces the computational cost compared to the conventional constrained inverse distance transform (CIDT), which applies a region growing starting from the current robot position to find the nearest unexplored cell as well as the shortest path to it while constraining the search space. As for experimental validation, performance of the proposed algorithm is compared to those of conventional coverage techniques to demonstrate its completeness of coverage, energy and time efficiency, and robustness to the environment shape or the initial robot pose.

► We develop an online complete coverage path planning algorithm for smooth path.
► Our algorithm is based on a new high-resolution grid map representation.
► A path is modeled by Bezier splines, smoothed by particle swarm optimization (PSO).
► We propose a coarse-to-fine constrained inverse distance transform for completeness.
► Our algorithm enhances energy and time efficiency, verified by simulations.