Combining Safe Collaborative and High-Accuracy Operations in Industrial Robots

Otto, Andreas; Hou, Shuxiao; Ahrens, Antje; Frieß, Uwe; Todtermuschke, Marcel; Bdiwi, Mohamad

doi:10.1007/978-3-662-62962-8_52

Cited by 2 publications

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“…In the past several decades, the sampling-based heuristic search method has been widely adopted in the field of motion planning in high-dimensional configuration space with great success. Rapidly-exploring Random Trees (RRT) ( LaValle, 1998 ), optimal Rapidly-exploring Random Trees (RRT*) ( Karaman and Frazzoli, 2011 ), Fast Marching Tree (FMT) ( Janson et al, 2015 ) and their extensions ( Kuffner and LaValle, 2000 ; Karaman and Frazzoli, 2011 ; Bdiwi et al, 2018 ; Otto et al, 2021 ) explore the configuration space incrementally by connecting feasible samples to a search tree. As the complexity of environments and the DOFs (degrees of freedom) of robot increase, samples are often infeasible.…”

Section: Related Workmentioning

confidence: 99%

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A data-driven approach for motion planning of industrial robots controlled by high-level motion commands

et al. 2023

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Most motion planners generate trajectories as low-level control inputs, such as joint torque or interpolation of joint angles, which cannot be deployed directly in most industrial robot control systems. Some industrial robot systems provide interfaces to execute planned trajectories by an additional control loop with low-level control inputs. However, there is a geometric and temporal deviation between the executed and the planned motions due to the inaccurate estimation of the inaccessible robot dynamic behavior and controller parameters in the planning phase. This deviation can lead to collisions or dangerous situations, especially in heavy-duty industrial robot applications where high-speed and long-distance motions are widely used. When deploying the planned robot motion, the actual robot motion needs to be iteratively checked and adjusted to avoid collisions caused by the deviation between the planned and the executed motions. This process takes a lot of time and engineering effort. Therefore, the state-of-the-art methods no longer meet the needs of today’s agile manufacturing for robotic systems that should rapidly plan and deploy new robot motions for different tasks. We present a data-driven motion planning approach using a neural network structure to simultaneously learn high-level motion commands and robot dynamics from acquired realistic collision-free trajectories. The trained neural network can generate trajectory in the form of high-level commands, such as Point-to-Point and Linear motion commands, which can be executed directly by the robot control system. The result carried out in various experimental scenarios has shown that the geometric and temporal deviation between the executed and the planned motions by the proposed approach has been significantly reduced, even if without access to the “black box” parameters of the robot. Furthermore, the proposed approach can generate new collision-free trajectories up to 10 times faster than benchmark motion planners.

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Section: Related Workmentioning

confidence: 99%

“…An improved RRT* approach ( Otto et al, 2021 ) is used to plan a trajectory as expert data. Unlike the basic RRT, the improved RRT* approach post-processes the planned robot motions using PTP and Linear interpolation and generates containing high-level motion commands.…”

Section: Experiments Design and Implementationmentioning

confidence: 99%