Solid waste selection is a common problem of the cities in emerging economies. In order to overcome this problem, a concept of smart selection of solid waste system has been developed, composed by a transportation system, a sensorial systems and a robotic system for selection of the solid waste. This paper focuses of the robotic system, the aim of this paper being the multi-objective optimization of a parallel robotic system in order to achieve maximal workspace and dexterity. Hence, the paper illustrates a small introduction in the concept of the new solid waste selection system and presents a recent literature review regarding the optimization of robots. Then, the structure and formal analysis of the robotic systems that has application for a smart selection of solid waste. The optimization is carried out using Genetics Algorithms and the objective function of the optimizations that takes into consideration the volume of the constant orientation workspace alongside with the average isotropy index within the workspace.
Parallel robots have many industrial applications due to their well-known advantages as high operational speeds, stiffness and accelerations. One the other hand, their workspace is reduced compared to the size of the elements of the robot. Frequently, the design of parallel robots implies a large amount of variables and nonlinear equations. This is why, a human designer generally applies optimisation algorithms in order to obtain specific properties of the robot. If the number of variables involved in the optimisation is too high, the required computational times may be extremely increased, aspect that for some applications is unacceptable. This is why, the aim of this paper is to analyse the performance comparison in terms of efficiency and computational times of an optimisation problem with several numbers of variables included in the optimisation. The variable define the geometrical characteristics of a parallel robot used for a solid waste selection system. Also, the optimisation problem is implemented using a heuristic algorithm, namely the Particle Swarm Optimization.
At present, in specific and complex industrial operations, robots have to respect certain requirements and criteria as high kinematic or dynamic performance, specific dimensions of the workspace, or limitation of the dimensions of the mobile elements of the robot. In order to respect these criteria, a proper design of the robots has to be achieved, which requires years of practice and a proper knowledge and experience of a human designer. In order to assist the human designer in the process of designing the robots, several methods (including optimization methods) have been developed. The scientific problem addressed in this paper is the development of an artificial intelligence method to estimate the size of the workspace and the kinematics of a robot using a feedforward neural network. The method is applied on a parallel robot composed of a base platform, a mobile platform and six kinematic rotational-universal-spherical open loops. The numerical results show that, with proper training and topology, a feedforward neural network is able to estimate properly values of the volume of the workspace and the values of the generalized coordinates based on the pose of the end effector.
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