Towards Developing Efficient Area Coverage Approach in Domestic Robots

東京電機大学201

Multi-Robot Complete Coverage Using Directional Constraints

Complete coverage relies on a path planning algorithm that will move one or more robots, including the actuator, sensor, or body of the robot, over the entire environment. Complete coverage of an unknown environment is used in applications like automated vacuum cleaning, carpet cleaning, lawn mowing, chemical or radioactive spill detection and cleanup, and humanitarian de-mining. The environment is typically decomposed into smaller areas and then assigned to individual robots to cover. The robots typically use the Boustrophedon motion to cover the cells. The location and size of obstacles in the environment are unknown beforehand. An online algorithm using sensor-based coverage with unlimited communication is typically used to plan the path for the robots. For certain applications, like robotic lawn mowing, a pattern might be desirable over a random irregular pattern for the coverage operation. Assigning directional constraints to the cells can help achieve the desired pattern if the path planning part of the algorithm takes the directional constraints into account. The goal of this dissertation is to adapt the distributed coverage algorithm with unrestricted communication developed by Rekleitis et al. (2008) so that it can be used to solve the complete coverage problem with directional constraints in unknown environments while minimizing repeat coverage. It is a sensor-based approach that constructs a cellular decomposition while covering the unknown environment. The new algorithm takes directional constraints into account during the path planning phase. An implementation of the algorithm was evaluated in simulation software and the results from these experiments were compared against experiments conducted by Rekleitis et al. (2008) and with an implementation of their distributed coverage algorithm. The results of this study confirm that directional constraints can be added to the complete coverage algorithm using multiple robots without any significant impact on performance. The high-level goals of complete coverage were still achieved. The work was evenly distributed between the robots to reduce the time required to cover the cells

Circle Packing as a Space Decomposition Method for Robot Path Planning and Coverage

One of the most important tasks for an autonomous robot is figuring out how to move from its current position and orientation to a new position and orientation. Despite significant progress in this area, active research is constantly looking for better ways to plan paths and traverse them. A specific type of path planning called coverage creates a path so that when the robot traverses the path its footprint covers the entire space. Most classical path planning and coverage path planning algorithms have some form of decomposition method for the target space. This work aims to provide a general way to decompose and represent the space so that it can be used both for classical path planning and for coverage. This work accomplishes the aim through using circle packing to tile a space with tangent circles that are the same size as the robot that will traverse the path. These circles are then converted into a planar graph which can be used for path planning and for coverage. The representation created by this decomposition uses less memory than traditional decomposition methods in practice and allows for shorter paths with less curvature to be created in most situations. These statements are shown to hold true for both planning a path between two points in an environment and planning a path to cover the entire environment