Realization Energy Optimization of Complete Path Planning in Differential Drive Based Self-Reconfigurable Floor Cleaning Robot

Anh Vu Le, Ping-Cheng Ku, Thein Than Tun, Nguyen Huu Khanh Nhan, Yuyao Shi and Rajesh Elara Mohan
Additional contact information
Anh Vu Le: ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
Ping-Cheng Ku: ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
Thein Than Tun: ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
Nguyen Huu Khanh Nhan: Optoelectronics Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
Yuyao Shi: ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
Rajesh Elara Mohan: ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore

Energies, 2019, vol. 12, issue 6, 1-23

Abstract: 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.

Keywords: reconfigurable robot; cleaning robot; navigation planning; area coverage; energy aware; evolutionary algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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