An Optimized Cleaning Robot Path Generation and Execution System using Cellular Representation of Workspace

Abstract

Many robot applications depend on solving the Complete Coverage Path Problem (CCPP). Specifically, robot vacuum cleaners have seen increased use in recent years, and some models offer room mapping capability using sensors such as LiDAR. With the addition of room mapping, applied robotic cleaning has begun to transition from random walk and heuristic path planning into an environment-aware approach. In this paper, a novel solution for pathfinding and navigation of indoor robot cleaners is proposed. The proposed solution plans a path from a priori cellular decomposition of the work environment. The planned path achieves complete coverage on the map and reduces duplicate coverage. The solution is implemented inside the ROS framework, and is validated with Gazebo simulation. Metrics to evaluate the performance of the proposed algorithm seek to evaluate the efficiency by speed, duplicate coverage and distance travelled.

Optimization of the initial position selection based on the Coverage Path Planning (CPP) algorithm

“…CPP is used in an autonomous vacuum with pathfinding and navigation functions [11]. The solution provided by Qile He and Yu Sun plans a path from a prior cellular decomposition of the work environment.…”