Complete Coverage Path Planning for Intelligent Sweeping Robot

As a universal optimization problem in automatic control, complete coverage path planning is one of the useful functions for intelligent sweeping robot, which makes sweeping robot complete sweeping more quickly and efficiently. Though conventional unit decomposition method has got solution, excessive decomposition for complex concave polygon environment and ignoring turning degrade the optimization effect. Considering the practical application of sweeping robot, improvements in three aspects are proposed that the cost function is added with the cost of turning, concave areas which can be combined planning with adjacent area are retained no decomposition, and global planning of unit path with transfer between units are considered by exhaustive search and ant colony algorithm, which can be applied to whole complex environment with better performance. Simulation experimental results show that the proposed approach can effectively reduce turning and total cost, make sweeping robot complete coverage more effectively.

I. Introduction

Intelligent manufacturing and smart home have great demand and dependence on robots, whereas intelligent technology promotes the progress of robot technology at the same time. Sweeping robot is a widely used robot product, whose further intelligent improvements are more efficient and convenient. Specifically, through intelligent identification of cleaning object environment, judgment of cleaning state, optimization of cleaning path, optimization of cleaning times to achieve energy saving, efficient, clean and low noise cleaning task. Among them, the full coverage path planning of all cleaning areas is one of the research contents of intelligent sweeper. This optimization problem is a common problem in the application of automatic control technology such as remote sensing shooting, automatic detection of large area objects, automatic spraying, etc., which is collectively referred to as complete coverage path planning (CCPP) [1], [2]. The solutions to CCPP problems are mainly divided into three categories: grid map method, element decomposition method and the combination of the two methods [3 – 6]. As the grid map method still needs to solve the problem of moving dead zone, the unit decomposition method is the best method to solve the CCPP problem [7 – 9].