Abstract
Applying the automation in covering the areas entirely eases manual jobs in various domestic fields such as site investigation, search, rescue, security, cleaning, and maintenance. A self-reconfigurable robot with adjustable dimensions is a viable answer to improve the coverage percentage for predefined map areas. However, the shape-shifting of this robot class also adds to the complexity of locomotion components and the need for an optimal complete coverage strategy for this new type of robot. The typical complete coverage route, including the least times of shape-shifting, the shortest navigation route, and the minimum travel time, is presented in the article. By splitting the map into the sub-areas similar to the self-reconfigurable robot's available shapes, the robot can design the ideal tileset and optimal navigation strategies to cover the workspace. To this end, we propose a Complete Tileset Energy-Aware Coverage Path Planning (CTPP) framework for a tiling self-reconfigurable robot named hRombo with four rhombus-shaped modules. The robot can reconfigure its base structure into seven distinct forms by activating the servo motors to drive the three robot hinges connecting robot modules. The problem of optimal path planning assisting the proposed hRombo robot to clear optimally all predefined tiles within the arbitrary workspace is considered a classic Travel Salesman Problem (TSP), and this TSP is solved by the reinforcement learning (RL) approach. The RL's reward function and action space are based on robot kinematic and the required energies, including transformation, translation, and orientation actions, to move the robot inside the workspace. The CTPP for the hRombo robot is validated with conventional complete coverage methods in simulation and real workspace conditions. The results showed that the CTPP is suitable for producing Pareto plans that enable robots to navigate from source to target in different workspaces with the least consumed energy and time among considered methods.
Highlights
Autonomous systems have been developing for both home and industrial appliances as their consumer demand witnesses a huge increase during recent years
There are threefold as the contributions of this article: (1) We proposed a complete tileset coverage CTPP approach developed for rhombus shape-based reconfigurable tiling robots.(2) We build the reinforcement learning (RL) reward function based on the Travel Salesman Problem, which depends on the platform’s real actions within any defined workspace.(3) CTPP is proposed to be tested on a real robot platform and proves energy and travel time effectiveness
EXPERIMENTAL RESULTS after presenting the result and analysis of RL training, simulated workspaces and real environment setups are used to validate the outperformance of the proposed tiling-based complete coverage path planning framework for the hRombo robot in terms of saving navigation energy travel time
Summary
Autonomous systems have been developing for both home and industrial appliances as their consumer demand witnesses a huge increase during recent years. Lakshmanan et al [36] discussed using Q-Learning to arrange modern tilling robotics In all cases, these works focus on the overage-oriented demarcation of guidelines from the source to destination and do not directly propose a complete coverage situation of reconfigurable tiling robots. There are threefold as the contributions of this article: (1) We proposed a complete tileset coverage CTPP approach developed for rhombus shape-based reconfigurable tiling robots.(2) We build the RL reward function based on the Travel Salesman Problem, which depends on the platform’s real actions within any defined workspace.(3) CTPP is proposed to be tested on a real robot platform and proves energy and travel time effectiveness. The final section, along with potential future work, is investigated in the Final Section VII
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