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.
Highlights
Modularised manufacturing cells provide a promising way for the improvement of productivity in the manufacturing process
In order for robots to take the place of human workers (Fig. 1) in manufacturing cells, several challenges have to be overcome such as teaching the robots which workpieces and tools to use for a given process, how to pick up workpieces and operate tools, and when to take advantages of the surrounding fixtures, and so on
The results show that the developed system is robust and can automatically choose intermediate states and plan robot motion
Summary
Modularised manufacturing cells provide a promising way for the improvement of productivity in the manufacturing process. This research presents a method to handle one of the stated problems in replacing human workers in a manufacturing cell, namely teaching a dual-arm robot to use electric tools using regrasp planning and visual recognition. The advantage of this method is that it does not require any third-party assisting systems like wearable devices or complicated visual tracking technology to capture, analyse, and map human grasps, motion, and tool poses to robots. The robot learns the starting pose and goal poses of an electric tool from human demonstration and automatically generates manipulation motion using a regrasp planner [1]
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