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

2020 ◽  
Author(s):  
Qile He ◽  
Yu Sun

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.

2001 ◽  
Author(s):  
Howie Choset ◽  
Ercan U. Acar ◽  
Yangang Zhang ◽  
Mark Schervish

Abstract Coverage path planning is the determination of a path that a robot must take in order to pass itself, a detector, or some other effector over each point in an environment. Applications include demining, floor scrubbing, and inspection. In previous work, we developed the boustrophedon cellular decomposition, an exact cellular decomposition approach, for the purposes of coverage. Each cell in the boustrophedon decomposition is covered with simple back and forth motions. Therefore, coverage is reduced to finding an exhaustive path through a graph that represents the adjacency relationships of the cells in the boustrophedon decomposition. Such a path will ensure that a detector passes over all points in the environment, but it does not guarantee that all ordnance is indeed detected because mine detectors have error. Therefore, we also consider probabilistic methods to determine paths for the robot to maximize the likelihood of detecting all ordnance in a target location using a priori known information.


Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1136 ◽  
Author(s):  
Anh Le ◽  
Ping-Cheng Ku ◽  
Thein Than Tun ◽  
Nguyen Huu Khanh Nhan ◽  
Yuyao Shi ◽  
...  

The efficiency of energy usage applied to robots that implement autonomous duties such as floor cleaning depends crucially on the adopted path planning strategies. Energy-aware for complete coverage path planning (CCPP) in the reconfigurable robots raises interesting research, since the ability to change the robot’s shape needs the dynamic estimate energy model. In this paper, a CCPP for a predefined workspace by a new floor cleaning platform (hTetro) which can self-reconfigure among seven tetromino shape by the cooperation of hinge-based four blocks with independent differential drive modules is proposed. To this end, the energy consumption is represented by travel distances which consider operations of differential drive modules of the hTetro kinematic designs to fulfill the transformation, orientation correction and translation actions during robot navigation processes from source waypoint to destination waypoint. The optimal trajectory connecting all pairs of waypoints on the workspace is modeled and solved by evolutionary algorithms of TSP such as Genetic Algorithm (GA) and Ant Optimization Colony (AC) which are among the well-known optimization approaches of TSP. The evaluations across several conventional complete coverage algorithms to prove that TSP-based proposed method is a practical energy-aware navigation sequencing strategy that can be implemented to our hTetro robot in different real-time workspaces. Moreover, The CCPP framework with its modulation in this paper allows the convenient implementation on other polynomial-based reconfigurable robots.


Author(s):  
Qingzeng Ma ◽  
Dongbin Zhang ◽  
Shuo Jin ◽  
Yuan Ren ◽  
Wei Cheng ◽  
...  

1989 ◽  
Author(s):  
Jerome Barraquand ◽  
Bruno Langlois ◽  
Jean-Claude Latombe

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