The level of autonomy is the most important feature by which the modern robotic systems development is directed. Furthermore, if the robots are supposed to work together in order to solve a complex task, their workspaces are shared. In this case, the robots present dynamic obstacle to each other. This paper presents a solution of the problem of motion coordination of two robots with overlapping workspaces based on co-evolutionary algorithm for simultaneous motion planning of the two robots. A method for exact calculation of the solution coding chromosome length based on physical limitations of the robots is proposed. The algorithm is evaluated in a simulation environment developed in Matlab. Implementation to the real industrial FANUC Lr Mate 200iC robots is performed. The simulation and implementation show high potential in terms of convergence robustness and time.
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.
This paper presents a cooperative coevolutionary approach to path planning for two robotic arms sharing common workspace. Each arm is considered an agent, required to find transition strategy from given initial to final configuration in the work space. Since the robots share workspace, they present dynamic obstacle to each other. To solve the problem of path planning in optimized fashion, we formulated it to multi-objective optimization domain and implemented co-evolutionary algorithm to simultaneously optimize four conflicting objectives. End-effector trajectory length, end-effector velocity distribution, total rotate angle and number of collisions are the objectives to be optimized. Simulation results for two 2-R type robots are presented.
In this paper, an integration of Honey bees mating algorithm (HBMA) and adaptive resonance theory neural network (ART1) for efficient path planning of a mobile robot in a static environment is presented. The robot must find shortest route from given origin to the target position. Moreover, it should be able to memorize the environment and, if it faces known world, execute already learned trajectory found by HBMA solver, or solve the world and memorize the trajectory for the given environment. This is done using Adaptive Resonance Theory based neural network. This way simulated robot is able to navigate through environment and to continuously increase its knowledge.
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-offreedom (DOF) industrial robots whose workspaces overlap. The planning is based on a novel hall of famePareto-based co-evolutionary algorithm. The modification of the algorithm is directed towards speeding-up coevolution, 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.
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