We propose a new method for the analysis and design of a robotic system that minimizes the energy consumption of a six-axis robot arm by controlling the velocity and acceleration of each arm of the robot to achieve the specified trajectory of the robot determined from a lean manufacturing method. A dynamic model of the PUMA 560 robot has been simulated on MATLAB, while the Robotics Toolbox and particle swarm optimization (PSO) are utilized to search for optimal paths and the optimal velocity and acceleration of the robot arms. The optimal velocity and acceleration are described as those giving minimum overall energy consumption constrained by a specified cycle time of the entire robotic system. Typically, the picking and placing of materials are carried out by humans, causing a variation in production rate, whereas our system using a robot arm ensures a stable production rate. Moreover, the optimal results obtained from PSO are adopted to train an artificial neural network (ANN) to extend the design system from discrete optimal values to a continuous and near-optimal value. In other words, the ANN is used to obtain an approximate optimal value between those obtained from PSO to make the system applicable to a real-world system. As shown by the simulation results, this method reduces the energy consumption of 12.3% from the initial energy and reduces the time for optimization by 99.8% compared with that for the PSO technique.
Robots have increasingly replaced humans for many jobs, including 24 h work, routine tasks, and dangerous jobs. However, the robot operating system has high power consumption in many processes. This has led to energy efficiency being the main focus. We have opted to build a robot with high strength, light weight, and low power consumption by reducing the weight of its components. Presently, we know that the structure of most robots in the world is made of metals, plastics, and composite materials. In this research, we designed the mechanical structure of robot arms with three different materials (cast iron, polyamide, and aluminum) using the finite element method to analyze and evaluate the possibilities of these materials. The dynamic load, power consumption, and mechanical characteristics were compared. It was found that polyamide could help lighten the weight by 40% and increase energy efficiency along with cost effectiveness by 41%. Although polyamide is particularly easy to find, cast iron is stronger than polyamide.
In this paper, the adaptive Monte Carlo localization (AMCL) error in terms of similar data detected by light detection and ranging (LiDAR) in different locations is investigated. This localization causes a robot to move to the incorrect location temporarily. We propose the fusion of landmark-based localization using an iBeacon device combined with the AMCL algorithm. This technique can solve the probabilistic localization problem of the conventional techniques applied in mobile robots by fusing the timed elastic band (TEB) and scan-matching algorithms, which reduces the error from 7 cm to less than 3 cm. The proposed technique is implemented on a clean-room-type mobile robot with 100 kg payload certificated by the SOP39 standard.
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