Teaching a robot to use electric tools with regrasp planning. Issue 1 (20th February 2019)

• Record Type: Journal Article
• Authors: Raessa, Mohamed; Sánchez, Daniel; Wan, Weiwei; Petit, Damien; Harada, Kensuke
• Is Part Of: CAAI transactions on intelligence technology. Volume 4:Issue 1(2019)
• Page Start: 54
• Page End: 63
• Publication Date: 2019-02-20
• Subjects: mobile robots -- learning (artificial intelligence) -- motion control -- path planning -- cameras -- robot vision -- manipulators -- dexterous manipulators; electric tools -- regrasp planning -- straightforward method -- teaching robots -- conventional methods -- third‐party systems -- complicated vision system -- map human grasps -- tool poses -- human motion -- human user -- robot motion -- dual‐arm robot; C3120C Spatial variables control -- C3390M Manipulators -- C5260B Computer vision and image processing techniques; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2018.1062
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : This study presents a straightforward method to teach robots to use tools. Teaching robots is crucial in quickly deploying and reconfiguring robots in next‐generation factories. Conventional methods require third‐party systems like wearable devices or complicated vision system to capture, analyse, and map human grasps, motion, and tool poses to robots. These systems assume lots of experience from their users. Unlike the conventional methods, this study does not involve learning human motion and skills. Instead, it only learns the object goal poses from the human user whilst employs regrasp planning to generate robot motion. The method is most suitable for a robot to learn the usage of electric tools that can be operated by simply switching on and off. The proposed method is validated using a dual‐arm robot with hand‐mounted cameras and several tools. Experimental results show that the proposed method is robust, feasible, and simple to teach robots. It can find a collision‐free and kino‐dynamic feasible grasp sequences and motion trajectories when the goal pose is reachable. The method allows the robot to automatically choose placements or handover considering the surrounding environment as intermediate states to change the pose of the tool and use tools following human demonstrations.