3 research outputs found
Increasing robot autonomy via motion planning and an augmented reality interface
Recently, there has been a growing interest in robotic systems that are able to share workspaces and collabo- rate with humans. Such collaborative scenarios require efficient mechanisms to communicate human requests to a robot, as well as to transmit robot interpretations and intents to humans. Recent advances in augmented reality (AR) technologies have provided an alternative for such communication. Nonetheless, most of the existing work in human-robot interaction with AR devices is still limited to robot motion programming or teleoperation. In this paper, we present an alternative approach to command and collaborate with robots. Our approach uses an AR interface that allows a user to specify high-level requests to a robot, to preview, approve or modify the computed robot motions. The proposed approach exploits the robot’s decision- making capabilities instead of requiring low-level motion spec- ifications provided by the user. The latter is achieved by using a motion planner that can deal with high-level goals corresponding to regions in the robot configuration space. We present a proof of concept to validate our approach in different test scenarios, and we present a discussion of its applicability in collaborative environments
An overview of XR technologies usage for industrial robot programming
Industrial robot programming can be a challenging
task, especially in today's age, where robots are more widespread
outside the large manufacturing companies, but rather in small
and medium enterprises where users are not necessarily fully
qualified individuals. The Extended Reality technologies may be
the ongoing answer to improved robot programming experience.
Current solutions for robot programming using Extended Reality
technologies are explored in this overview. In this paper, a
summarized description of certain solutions is given, focusing on
how are the XR technologies utilized in developing the robot
programming systems. Categorization by devices and motion
planners used is also given
Mutual-cognition for proactive human-robot collaboration: A mixed reality-enabled visual reasoning-based method
Human-Robot Collaboration (HRC) is key to achieving the flexible automation required by the mass personalization trend, especially towards human-centric intelligent manufacturing. Nevertheless, existing HRC systems suffer from poor task understanding and poor ergonomic satisfaction, which impede empathetic teamwork skills in task execution. To overcome the bottleneck, a Mixed Reality (MR) and visual reasoning-based method is proposed in this research, providing mutual-cognitive task assignment for human and robotic agents’ operations. Firstly, an MR-enabled mutual-cognitive HRC architecture is proposed, with the characteristic of monitoring Digital Twins states, reasoning co-working strategies, and providing cognitive services. Secondly, a visual reasoning approach is introduced, which learns scene interpretation from the visual perception of each agent’s actions and environmental changes to make task planning strategies satisfying human–robot operation needs. Lastly, a safe, ergonomic, and proactive robot motion planning algorithm is proposed to let a robot execute generated co-working strategies, while a human operator is supported with intuitive task operation guidance in the MR environment, achieving empathetic collaboration. Through a demonstration of a disassembly task of aging Electric Vehicle Batteries, the experimental result facilitates cognitive intelligence in Proactive HRC for flexible automation