A Layered Control Approach to Human-Aware Task and Motion Planning for Human-Robot Collaboration

Abstract

Combining task and motion planning efficiently in human-robot collaboration (HRC) entails several challenges because of the uncertainty conveyed by the human behavior. Tasks plan execution should be continuously monitored and updated based on the actual behavior of the human and the robot to maintain productivity and safety. We propose control-based approach based on two layers, i.e., task planning and action planning. Each layer reasons at a different level of abstraction: task planning considers high-level operations without taking into account their motion properties; action planning optimizes the execution of high-level operations based on current human state and geometric reasoning. The result is a hierarchical framework where the bottom layer gives feedback to top layer about the feasibility of each task, and the top layer uses this feedback to (re)optimize the process plan. The method is applied to an industrial case study in which a robot and a human worker cooperate to assemble a mosaic.

Adapting Cobot Behavior to Human Task Ordering Variability for Assembly Tasks

“…Robotic architectures bridging sensory and action elements with task planning reasoning capabilities in the context of industrial human-robot collaboration were discussed mainly from the performance point of view, where the time of planning and scheduling is the main criterion for evaluation [4,8,10,24]. In contrast, human aspects such as acceptability, usability, and cognitive load were less discussed [2,5,21].…”

“…In contrast, human aspects such as acceptability, usability, and cognitive load were less discussed [2,5,21]. Furthermore, most works limit the freedom of action of humans in the execution of their task, for example by assigning specific tasks to humans [5,8,21], or limiting their choices in terms of assembly zones and assembled block colors [4]. To relax these constraints it is necessary to grant the human a higher degree of liberty to make the interaction with the cobot smooth, natural, and intuitive.…”

“…Accommodating robot's behaviors to human task variability is a challenging issue due to the uncertainty in human behavior [4] and the probability of conflicts between human and cobot actions [2]. To deal with this issue, we propose in this paper a robotic architecture bridging sensory and action Copyright © 2021by the authors.…”

Simplifying the A.I. Planning modeling for Human-Robot Collaboration

“…Furthermore, task assignment and synchronization between humans and robots should consider duration variability and good (or bad) synergies of simultaneous collaborative operations. In this regard, AI Planning and its integration with Motion Planning and Robot Control have shown promising results in terms of improved robot flexibility [13], [14] and efficiency of resulting collaborative processes [15], [16], [17]. In this work we rely on the timeline-based paradigm which represents a well established AI planning technology.…”

Simplify the robot programming through an action-and-skill manipulation framework

“…In [7], a layered approach was adopted, demanding the control and the execution of simple actions to a layer named action planner. Similarly, [8] introduced a framework that addresses manipulation tasks through three interconnected layers: plan and execute (high-layer), learn from the experience (mediumlayer), and deal with the motion planning (low-layer).…”