article · Annals of Emerging Technologies in Computing
Optimising the trajectory of a robotic arm is a critical design challenge that relies on solving inverse kinematics problems while targeting specific performance criteria. Previous techniques often yielded only marginal improvements. This research investigates trajectory control aimed at minimising reachability time across an operation cycle. Two methods were developed and assessed using forward and inverse kinematics: a rule-based optimization technique and a genetic algorithm. Both approaches were tested and compared on a KUKA KR 4 R600 six-degree-of-freedom robotic arm. The evaluation showed that the genetic algorithm significantly outperforms the rule-based approach, producing movement solutions that are approximately three times faster for identical paths. This difference arises because the rule-based method explores the entire search space, which requires substantial time, whereas the genetic algorithm operates more selectively.
In automated environments, the speed and efficiency of robotic movement directly dictate operational productivity. By reducing the time it takes for a robotic arm to reach its destination, industrial and research systems can complete tasks more quickly. Finding computational methods that generate rapid trajectories without exhaustive calculations is vital for boosting overall automated efficiency.
This research applies to automated manufacturing and industrial robotics where cycle times must be minimised. Tested specifically on an industrial KUKA KR 4 R600 arm, the work represents applied, tested research that could interest robotics software developers and manufacturing automation engineers seeking faster path-planning tools.
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The design of the robotic arm's trajectory is based on inverse kinematics problem solving, with additional refinements of certain criteria. One common design issue is the trajectory optimization of the robotic arm. Due to the difficulty of the work in the past, many of the suggested ways only resulted in a marginal improvement. This paper introduces two approaches to solve the problem of achieving robotic arm trajectory control while maintaining the minimum reachability time. These two approaches are based on rule-based optimization and a genetic algorithm. The way we addressed the problem here is based on the robot’s forward and inverse kinematics and takes into account the minimization of operating time throughout the operation cycle. The proposed techniques were validated, and all recommended criteria were compared on the trajectory optimization of the KUKA KR 4 R600 six-degree-of-freedom robot. As a conclusion, the genetic based algorithm behaves better than the rule-based one in terms of achieving a minimal trip time. We found that solutions generated by the Genetic based algorithm are approximately 3 times faster than the other solutions generated by the rule-based algorithm to the same paths. We argue that as the rule-based algorithm produces its solutions after discovering all the problem’s searching space which is time consuming, and it is not the case as per the genetic based algorithm.
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DOI: 10.33166/aetic.2024.01.003
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