Physical Human–Robot Cooperation Based on Robust Motion Intention Estimation

Abstract

SUMMARY Cooperative transportation by human and robotic coworkers constitutes a challenging research field that could lead to promising technological achievements. Toward this direction, the present work demonstrates that, under a leader–follower architecture, where the human determines the object’s desired trajectory, complex cooperative object manipulation with minimal human effort may be achieved. More specifically, the robot estimates the object’s desired motion via a prescribed performance estimation law that drives the estimation error to an arbitrarily small residual set. Subsequently, the motion intention estimation is utilized in the object dynamics to determine the interaction force between the human and the object. Human effort reduction is then achieved via an impedance control scheme that employs the aforementioned estimations. The feedback relies exclusively on the robot’s force/torque, position as well as velocity measurements at its end effector, without incorporating any other information on the task. Moreover, an adaptive control scheme is adopted to relax the need for exact knowledge of the object dynamics. Finally, an extension for multiple robotic coworkers is studied and verified via simulation, while extensive experimental results for the single robot case clarify the proposed method and corroborate its efficiency.

A Human-Aware Method to Plan Complex Cooperative and Autonomous Tasks using Behavior Trees

“…The chosen weights are: = 0.51, = 0.49, = 0.6 and = 0.35. Since the human is present in the workcell, = 0.51 and = 0.49 are chosen to satisfy the conditions in (6), where the two weights are similar to not privilege a specific agent. Moreover, in our scenario, we would like to favor worker ergonomics instead of the execution time.…”

“…The latter category has received slightly more attention in industry due to an inherently greater postural stability, while dealing with heavy manipulation tasks. The examples include logistics [6]- [8] and manufacturing [9] scenarios. The mobility of such systems allows to exploit their loco-manipulation capabilities to ensure safe human-robot collaboration, through intuitive interfaces that allow the human operators to interact with the robot [10], [11].…”

Hierarchical Intention Tracking for Robust Human-Robot Collaboration in Industrial Assembly Tasks

“…Collaborative robot solutions are being actively developed in many industrial tasks, including concurrent part sorting and desired tool delivery, precise positioning, and cooperative transportation [1]- [4]. Teaming up humans and robots boosts the production efficiency by combining cognition and dexterity from humans with the repeatability and load carrying capacity from robots [5].…”

Adaptive task‐space cooperative tracking control for manipulators with a desired trajectory estimator and a velocity observer

“…In a study by Alevizos et al, an object's desired trajectory determined by the human leader was estimated with the use of a prescribed performance estimator, and human effort was dramatically reduced under the estimation law. 15 In a study by Heshmati-Alamdari et al, a novel prescribed performance estimation law in combination with an impedance control law was proposed to estimate the object's desired trajectory. 16 In a study by Mavridis et al, the desired trajectory of a manipulator was estimated via a prescribed performance estimation law, and the problem of physical human-robot cooperation for object manipulation was solved by an impedance control scheme based on the aforementioned estimation.…”