A Haptic Shared-Control Architecture for Guided Multi-Target Robotic Grasping

Abstract

Although robotic telemanipulation has always been a key technology for the nuclear industry, little advancement has been seen over the last decades. Despite complex remote handling requirements, simple mechanically-linked master-slave manipulators still dominate the field. Nonetheless, there is a pressing need for more effective robotic solutions able to significantly speed up the decommissioning of legacy radioactive waste. This paper describes a novel haptic shared-control approach for assisting a human operator in the sort and segregation of different objects in a cluttered and unknown environment. A 3D scan of the scene is used to generate a set of potential grasp candidates on the objects at hand. These grasp candidates are then used to generate guiding haptic cues, which assist the operator in approaching and grasping the objects. The haptic feedback is designed to be smooth and continuous as the user switches from a grasp candidate to the next one, or from one object to another one, avoiding any discontinuity or abrupt changes. To validate our approach, we carried out two human-subject studies, enrolling 15 participants. We registered an average improvement of 20.8%, 20.1%, 32.5% in terms of completion time, linear trajectory, and perceived effectiveness, respectively, between the proposed approach and standard teleoperation.

Haptic Shared-Control Methods for Robotic Cutting under Nonholonomic Constraints

“…and τ l,s is a lateral control force enforcing the nonholonomic motion constraints on the slave robot, defined similarly to τ l,m in (5), τ l,s = −K l,s d l,s (t)n l,s (t) − B l,s ṫ s · n l,s (t) n l,s (t), (9) where S l,s (t) : (n l,s (t), t l,s (t)) is the plane we want to constrain the slave in, d l,s (t) = (t s − t l,s (t))n l,s (t) is the distance between the current slave pose and the plane S l,s (t), K l,s ∈ R is a (high) stiffness parameter, and B l,s ∈ R is the corresponding damping term. In our application, K l,s = 1000 N/m and B l,s = 63 Ns/m.…”

“…How to implement such division of roles between the human and the autonomous component highly depends on the task and robotic system [5], [6]. Nevertheless, different shared-control architectures have been proposed for different applications such as robotic grasping, cutting, and precise positioning [7]- [9].…”

Haptic-Guided Teleoperation of a 7-DoF Collaborative Robot Arm With an Identical Twin Master

“…As a result, most related studies focus on a single aspect of the task when generating force feedback. An exception is by Abi-Farraj et al, who combined haptic cues to provide trajectory guidance to reach good grasp configurations in addition to imposing kinematic constraints [28]. Even though this study is interesting to see a combination of different force cues, the kinematic constraints were conveyed not through kinaesthetic guidance but through vibrations.…”

Caring About the Human Operator: Haptic Shared Control for Enhanced User Comfort in Robotic Telemanipulation

“…α and β are weights to be tuned depending on the importance to be given to each cost function (α + β = 1). Finally, D is a diagonal damping matrix to improve the bilateral stability of the system [7], [32]. We chose a damping value of 2 Ns/m in translation and 0.07 Nms/rad in rotation.…”

“…Additional feedback techniques for providing this guidance information are discussed in Sec. V. Finally, once the user is within a threshold distance from the target position, we scale each force proportionally to the distance to the goal, to avoid strong forces and oscillations as the user gets closer to the goal (as done in [32]).…”