A Multi-Robotic System for Environmental Dirt Cleaning

Abstract

There is a lot of waste in an industrial environment that could cause harmful effects to both the products and the workers resulting in product defects, itchy eyes or chronic obstructive pulmonary disease, etc. While automative cleaning robots could be used, the environment is often too big for one robot to clean alone in addition to the fact that it does not have adequate stored dirt capacity. We present a multi-robotic dirt cleaning system algorithm for multiple automatic iRobot Creates teaming to efficiently clean an environment. Moreover, since some spaces in the environment are clean while others are dirty, our multi-robotic system possesses a path planning algorithm to allow the robot team to clean efficiently by spending more time on the area with higher dirt level. Overall, our multi-robotic system outperforms the single robot system in time efficiency while having almost the same total battery usage and cleaning efficiency result.

A Survey on Swarm Robotics for Area Coverage Problem

“…According to [100], cleaning an industrial environment by using a multi-robotic dirtcleaning algorithm requires a team of many iRobots. The experiment was performed for two iRobots on cardboard boxes as an environment.…”

“…Using iRobot gear in an unfamiliar setting and cleaning industrial areas through multi-robotic algorithms demonstrate poor coordination and centralization. As opposed to a single robot, the system is more efficient in cleaning, but it cannot handle obstacles that change position without rerouting [100]. Applying a visibility-based strategy with strong coordination and minimal centralization, executed on AmigoBot hardware in a known environment, solves non-convex area coverage challenges.…”

Consensus, cooperative learning, and flocking for multiagent predator avoidance

“…[14][15][16] Movement control of multiagent systems can come in the form of cooperatively doing path planning as in the literature. [17][18][19] Alternatively, there are means of control through flocking in varying formations [20][21][22] to achieve a variety of tasks. 23,24 Reinforcement learning has been implemented cooperatively in a variety of ways for multiagent environments, such as a GridWorld 25,26 and box pushing.…”

Speeding Up On-Line Route Scheduling for an Autonomous Robot Through Pre-Built Paths

“…Le et al 16 present a TSP-based path planning algorithm, also for multiple robots, to create a path planning for each robot in the team in order to clean industrial environments. Usually, industrial environments are much too large for one robot to clean by itself in addition to the fact that the amount of dirt will exceed the dirt stored capacity of one robot.…”

“…If the map did not have enough strategic points, these previous approaches may fail or yield not very reasonable paths. So before the end of algorithm, if the shortestPath is still empty, a dynamic path is created from scratch with A* (lines [14][15][16] to guarantee that a path will be returned no matter where s and g are on the map. This scenario is illustrated by the last frame of Fig.…”