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.
Highlights
Household robotics have recently become a favorite research topic with the intense focus of the development and deployment of robotic vacuum cleaners
Path planning algorithms implemented for most mobile robots focus on maneuvering the robot from a starting point to the destination with minimal distance traveled or energy consumed; in the case of vacuum cleaning robots, complete coverage path planning (CCPP) algorithms are implemented that attempt to maximize the area covered by the robot throughout the process
The two evolutionary algorithms implemented, Genetic Algorithm (GA) and ant colony optimization (ACO), are being compared with precedented algorithms such as zigzag, spiral, greedy search, and algorithms introduced in the work of [22], which are all valid approaches to solve Travelling Salesman Problem (TSP)-based problems
Summary
Household robotics have recently become a favorite research topic with the intense focus of the development and deployment of robotic vacuum cleaners. In order to solve TSP, the cost function as the objective function in terms of total distance travel is derived from the actual navigation mechanism by differential drive module mounted at each block of new tetromino platform hTetro To this end, the outline of this paper is presented as follows. The steering ability that provided by four differential drives hTetro helps the robot successfully execute three types of action: (1) transformation between different morphologies, (2) translation locomotion to connect waypoints in the workspace, and (3) adjustment of orientation around the center of robot mass (COM) while maintaining its morphology The operations of these modules all contribute to the energy consumption of the system during the navigation and have to be evaluated independently. The energy consuming by each operation of the robot and the optimal sequence concerning energy efficient will be modeled
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