Phone Thiha Kyaw

TL;DR: In this article, the authors proposed a novel approach to solve the coverage path planning problem in large complex environments based on the Travelling Salesman Problem (TSP) and Deep Reinforcement Learning (DRL) leveraging the grid-based maps.

TL;DR: A complete waypoint path planning (CWPP) to re-blast the self-synthesizing deep convolutional neural network (DCNN) based corrosion heatmap by initial-blasting is proposed for a novel robot platform named Hornbill with the adhesion mechanism by permanent magnetic, self-localization by sensor fusion to navigate smoothly on a vertical surface.

TL;DR: A Complete Tileset Energy-Aware Coverage Path Planning (CTPP) framework for a tiling self-reconfigurable robot named hRombo with four rhombus-shaped modules is proposed and 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.

TL;DR: In this paper, the authors proposed an optimal complete waypoint path planning (CWPP) framework to help the robot re-blast the benchmarked workspace, including objective functions of the shortest travel distance, the least upward moving direction to reduce water, energy spent while ensuring the visiting of the robot to all uncleaned waypoints defined by benchmarking output, is modeled as the classic Travel Salesman Problem.

TL;DR: A novel autonomous self-evaluating hull cleaning robot called Hornbill is presented that could successfully generate high accurate cleanliness heat maps in all considered scenarios, which simplifies the checkup process for paint inspectors.