Toward safe close-proximity human-robot interaction with standard industrial robots

Lasota, Przemyslaw A.; Rossano, Gregory; Shah, Julie A.

doi:10.1109/coase.2014.6899348

Cited by 168 publications

(84 citation statements)

References 13 publications

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“…The safety in cooperative manufacturing extends from preventing the possibilities of any potential physical collision which might happen between the robot and the workcell components, to the psychological protection of the worker due to his cooperation with the robot. The research in [8] presents a real-time safety system which is capable of achieving the cooperative manufacturing with a standard IR -ABB-IRB-120. The system does not need to modify the standard robot hardware by adding any external sensors.…”

Section: Related Work and Challenges In Cooperative Manufacturingmentioning

confidence: 99%

An Ontology-Based Approach to Enable Knowledge Representation and Reasoning in Worker–Cobot Agile Manufacturing

Sadik

Urban

2017

Future Internet

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Abstract:There is no doubt that the rapid development in robotics technology has dramatically changed the interaction model between the Industrial Robot (IR) and the worker. As the current robotic technology has afforded very reliable means to guarantee the physical safety of the worker during a close proximity interaction with the IR. Therefore, new forms of cooperation between the robot and the worker can now be achieved. Collaborative/Cooperative robotics is the new branch of industrial robotics which empowers the idea of cooperative manufacturing. Cooperative manufacturing significantly depends on the existence of a collaborative/cooperative robot (cobot). A cobot is usually a Light-Weight Robot (LWR) which is capable of operating safely with the human co-worker in a shared work environment. This is in contrast with the conventional IR which can only operate in isolation from the worker workspace, due to the fact that the conventional IR can manipulate very heavy objects, which makes it so dangerous to operate in direct contact with the worker. There is a slight difference between the definition of collaboration and cooperation in robotics. In cooperative robotics, both the worker and the robot are performing tasks over the same product in the same shared workspace but not simultaneously. Collaborative robotics has a similar definition, except that the worker and the robot are performing a simultaneous task. Gathering the worker and the cobot in the same manufacturing workcell can provide an easy and cheap method to flexibly customize the production. Moreover, to adapt with the production demands in the real time of production, without the need to stop or to modify the production operations. There are many challenges and problems that can be addressed in the cooperative manufacturing field. However, one of the most important challenges in this field is the representation of the cooperative manufacturing environment and components. Thus, in order to accomplish the cooperative manufacturing concept, a proper approach is required to describe the shared environment between the worker and the cobot. The cooperative manufacturing shared environment includes the cobot, the co-worker, and other production components such as the product itself. Furthermore, the whole cooperative manufacturing system components need to communicate and share their knowledge, to reason and process the shared information, which eventually gives the control solution the capability of obtaining collective manufacturing decisions. Putting into consideration that the control solution should also provide a natural language which is human readable and in the same time can be understood by the machine (i.e., the cobot). Accordingly, a distributed control solution which combines an ontology-based Multi-Agent System (MAS) and a Business Rule Management System (BRMS) is proposed, in order to solve the mentioned challenges in the cooperative manufacturing, which are: manufacturing knowledge representation, sharing, and reasoning.

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Section: Related Work and Challenges In Cooperative Manufacturingmentioning

confidence: 99%

An Ontology-Based Approach to Enable Knowledge Representation and Reasoning in Worker–Cobot Agile Manufacturing

Sadik

Urban

2017

Future Internet

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“…Likewise in Croft, 2006, Kulić andCroft, 2007], the relative position, velocity and effective inertia is calculated at the point of minimum distance between human and robot. Relative position and velocity is also used as a safety metric in an industrial setting in [Zanchettin et al, 2016], while in [Lasota et al, 2014], the robot's velocity is decreased in response to an operator's approach. Finally, an interesting approach is proposed in [Lacevic and Rocco, 2010] and [Ragaglia et al, 2014] where a danger field based uniquely on the robot's state is generated.…”

Section: Introductionmentioning

confidence: 99%

An industrial security system for human-robot coexistence

Long

Chevallereau

Chablat

et al. 2017

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Purpose -The installation of industrial robots requires security barriers, a costly, time consuming exercise. Collaborative robots may offer a solution, however these systems only comply with safety standards if operating at reduced speeds. This paper describes the development and implementation of a novel security system that allows human robot co-existence while permitting the robot to execute much of its task at nominal speed. Design/methodology/approach -The security system is defined by three modes: a nominal mode, a coexistence mode and a gravity compensation mode. Mode transition is triggered by three lasers, two of which are mechanically linked to the robot. These scanners create a dynamic envelope around the robot and allow the detection of operator presence or environmental changes. To avoid velocity discontinuities between transitions we propose a novel time scaling method. Findings -The paper describes the system's mechanical, software and control architecture. The system is demonstrated experimentally on a collaborative robot and is compared with the performance of a state of art security system. Both a qualitative and quantitative analysis of the new system is carried out. Pratical Implications -The mode transition method is easily implemented, requires little computing power and leaves the trajectories unchanged. As velocity discontinuities are avoided, motor wear is reduced. The execution time is substantially less than a commercial alternative. These advantages can lead to economic benefits in high volume manufacturing environments. Originality/value -This paper proposes a novel system that is based on industrial material but that can generate dynamic safety zones for a collaborative robot.

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“…The conventional approach towards ensuring safe human robot interaction is to prevent collisions, which is usually achieved by setting mechanical barriers, using special actuators, or internal force and torque sensors [1]. This cannot be implemented for humanoid robots as it leads to limited workspace, which is not acceptable in operating environments shared with humans where physical contact is inevitable.…”

Section: Introductionmentioning

confidence: 99%

“…The RNN neuron is modeled by a dierential equation (1), where x i is the membrane potential, τ i a time constant, C a parameter that modulates the chaoticity level of the RNN, r j and u j , the output and input of the j th neuron respectively, and z j the signal generated by the read-out units. A nonlinearity (2) is added by a sigmoidal activation function that has also the function to limit the output of the neurons between [−1 1].…”

mentioning

confidence: 99%