2D Linear Time-Variant Controller for Human's Intention Detection for Reach-to-Grasp Trajectorie
Designing robotic assistance devices for manipulation tasks is challenging. This work is concerned with improving accuracy and usability of semi-autonomous robots, such as human operated manipulators or exoskeletons. The key insight is to develop a system that takes into account context- and user-awareness to take better decisions in how to assist the user. The context-awareness is implemented by enabling the system to automatically generate a set of candidate grasps and reach-to-grasp trajectories in novel, cluttered scenes. The user-awareness is implemented as a linear time-variant feedback controller to facilitate the motion towards the most promising grasp. Our approach is demonstrated in a simple 2D example in which participants are asked to grasp a specific object in a clutter scene. Our approach also reduce the cognitive burden of the user by providing only control on $x-$ and $y-$axis, while orientation of the end-effector and the pose of its fingers are inferred by the system. The experimental results show the benefits of our approach in terms of accuracy and execution time with respect to a pure manual control.
#iLQR #lqr #timevariant #grasp #robot #robotics #reachtograsp #planning #ai #artificialintelligence #feedback #controller #humanintentiondetection #sharedcontroller #universityofbirmingham #irlab #uob