Collaborative Robotics and Adaptive Machines

Dear Colleagues,

Robots in traditional industrial applications are usually separated from humans. This is done partly for safety for humans and partly due to the perception that the capabilities of robots and the abilities of humans are largely mutually exclusive. However, this perception has significantly changed in recent years with the prevailing view that the capabilities of robots and the abilities of humans are largely complementary if exploited properly. This is the notion behind ‘collaborative robotics’ where robots and human co-workers share the same workspace and perform together on the same tasks with the same goals augmenting their mutual strengths and counteracting their inherent limitations. While collaboration between robots and humans is the central theme of collaborative robotics, collaboration between robots or other artificial agents is also promising. Currently, there is no doubt about the importance of collaborations in robotics. However, the enhancement of effectiveness, safety, and performance of collaborative robots is still a demanding issue. Especially, the collaborators require perceiving the situations and adjusting with it to smoothly accomplish the collaborative tasks. It is assumed that different approaches to make the collaborations more adaptive to prevailing situations and uncertainties should receive priority in research to make the collaborations more effective. Thus, the objective of this Special Issue is to accumulate various innovative strategies that make the collaborative robots and other similar machines more effective, safe, efficient, and adaptive with respect to prevailing task situations and uncertainties.

The Special Issue will cover topics in the context of recent advances and future trends in collaborative robotics and adaptive machines including, but are not limited to the following:

• Collaboration modeling and collaboration dynamics
• Intelligent control strategies especially adaptive and predictive controls
• Artificial intelligence, machine learning, and vision-based algorithms
• Subtasks allocation and scheduling optimization
• Autonomy, team fluency, mixed initiatives, and mutual trust
• Bio-inspiration in human-robot or robot-robot collaboration
• Transparency, engagement, communication, safety, and efficiency
• Mechanisms and interface design, and passive compliance
• Security in collaborative robotics
• Task and motion planning in constrained spaces
• Collaboration or symbiosis in the forms of cyber-physical system (CPS) and internet of robotic things (IoRT)
• Ambient, embedded and cloud-based intelligence, and adaptive robotic ecology
• Collaborations in/with heterogeneous realities (e.g., real robot–virtual human collaboration)
• Performance assessment methods and metrics and benchmarking
• Education in/with collaborative robotics

Dr. S. M. Mizanoor Rahman
Guest Editor

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