Co-evolutionary algorithm for motion planning of two industrial robots with overlapping workspaces regular paper

A high level of autonomy is a prerequisite for achieving robotic presence in a broad spectrum of work environments. If there is more than one robot in a given environment and the workspaces of robots are shared, then the robots present a dynamic obstacle to each other, which is a potentially dangerous situation. This paper deals with the problem of motion planning for two six-degrees-of-freedom (DOF) industrial robots whose workspaces overlap. The planning is based on a novel hall of fame - Pareto-based co-evolutionary algorithm. The modification of the algorithm is directed towards speeding-up co-evolution, to achieve real-time implementation in an industrial robotic system composed of two FANUC LrMate 200iC robots. The results of the simulation and implementation show the great potential of the method in terms of convergence, robustness and time

Spoznajni model upravljanja grupom industrijskih robota

U odnosu na klasične pristupe u kojima se robotski sustavi programiraju za ograničeni broj djelovanja, sustavi koji koriste koncepte sveprisutnog računarstva mogu autonomno predviđati ponašanje u specifičnim situacijama. U ovoj disertaciji je razvijen spoznajni model upravljanja sustavom umreženih robota zasnovan na kontekstualnoj spoznaji okoline koristeći koncepte sveprisutnog računarstva. Ti su koncepti ostvareni razvojem odgovarajuće ontologije povezane s mehanizmom odlučivanja. Razvojem ontologije za sustav robota definiran je deskriptivni model znanja karakterističan za industrijsku primjenu kod poslova robotskog sklapanja. Korišteni mehanizmi odlučivanja temeljeni su na deskriptivnoj logici ostvarenoj unutar ontologije te Bayesovoj mreži, omogućujući pritom dovoljnu razinu apstrakcije potrebnu za donošenje jednoznačnih odluka primjerenih trenutnom kontekstu

Context-aware system applied in industrial assembly environment

The objective of this paper is to present an ongoing development of a context-aware system used within industrial environments. The core of the system is so-called Cognitive Model for Robot Group Control. This model is based on well-known concepts of Ubiquitous Computing, and is used to control robot behaviours in specially designed industrial environments. By using sensors integrated within the environment, the system is able to track and analyse changes, and update its informational buffer appropriately. Based on freshly collected information, the Model is able to provide a transformation of high-level contextual information to lower-level information that is much more suitable and understandable for technical systems. The Model uses semantically defined knowledge to define domain of interest, and Bayesian Network reasoning to deal with the uncertain events and ambiguity scenarios that characterize our naturally unstructured world

Context-Driven Method in Realization of Optimized Human-Robot Interaction

Perceptual uncertainty and environmental volatility are among the most enduring challenges in robotic research today. Contemporary robotic systems are usually designed to work in specific and controlled domains where a total number of variables is defined. Traditional solutions therefore often result in over-constrained interaction spaces or rigid system architectures where any unexpected change can result in system failure. The focus of this work is set on achieving a constant adaptation of the system to changes through interaction. A computational mechanism based on the entropy reduction method is integrated along with the three-component control model. This model is seen as a context-to-data interpreter used to provide context-aware reasoning to the technical system. The mechanism is using a decrease in interaction uncertainties when proofs are provided to the system. In this way, the robot can choose the right interaction strategy that resolves reasoning ambiguities most efficiently