Path planning to expedite the complete transfer of distributed gravel piles with an automated wheel loader. (2nd December 2021)

• Record Type: Journal Article
• Authors: Kawabe, Tomohito; Takei, Toshinobu; Imanishi, Etsujiro
• Is Part Of: Advanced robotics. Volume 35:Number 23(2021)
• Page Start: 1418
• Page End: 1437
• Publication Date: 2021-12-02
• Subjects: Wheel loader -- gravel pile -- path planning -- deep reinforcement learning -- automation; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2021.2008488
• Languages: English
• ISSNs: 0169-1864

Abstract

Abstract : This study introduces expedite the complete transfer of distributed gravel piles with an automated wheel loader. The wheel loader scoops the gravel and unloads it onto the bed of a truck. The total mileage for the repeated scooping and unloading work is reduced. The complicated optimal task is divided into three simple, organized subjects and different appropriate algorithms are used for each subject. A method is proposed that interactively selects the appropriate scooping points, unloading points, and appropriate path for the varying shape of the gravel pile until all gravel piles have been scooped up. To expedite the complete transfer of distributed gravel piles, the deep reinforcement learning model, trained via simulation, can be used in practice by implementing as part of the proposed methods. This paper describes these issues and the proposed methods. The performance of the actual application is demonstrated in terms of the calculation time and the feasibility of the simulations. GRAPHICAL ABSTRACT: UF0001