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Abstract
This paper presents a complex trajectory evaluation framework with a high potential for use in many industrial applications. The framework focuses on the evaluation of robotic arm trajectories containing only robot states defined in joint space without any time parametrization (velocities or accelerations). The solution presented in this article consists of multiple criteria, mainly based on well-known trajectory metrics. These were slightly modified to allow their application to this type of trajectory. Our framework provides the methodology on how to accurately compare paths generated by randomized-based path planners, with respect to the numerous industrial optimization criteria. Therefore, the selection of the optimal path planner or its configuration for specific applications is much easier. The designed criteria were thoroughly experimentally evaluated using a real industrial robot. The results of these experiments confirmed the correlation between the predicted robot behavior and the behavior of the robot during the trajectory execution.
Highlights
Criteria for Trajectories of RoboticIn many modern applications of robotic arms, there is a need to compute collision-free trajectories with variable start and goal positions
This problem is described in the literature as a problem of dynamic path planning. Several solutions to this problem have been proposed and applied over the past decade. Many of these popular methods are based on randomized sampling, such as probabilistic roadmap methods (PRM) [1] or rapidly random trees (RRT) [2]
3, lines were compared with positions on trajectory executed by a real robot
Summary
In many modern applications of robotic arms, there is a need to compute collision-free trajectories with variable start and goal positions. An example of such application is the problem of bin-picking, where the main goal is to pick randomly placed objects from within the bin. Several solutions to this problem have been proposed and applied over the past decade Many of these popular methods are based on randomized sampling, such as probabilistic roadmap methods (PRM) [1] or rapidly random trees (RRT) [2]. Another approach to path planning is using optimization-based methods.
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