Comparing Online Robot Joint Space Trajectory Optimization for Task Space Applications

Krämer, Maximilian; Velasco-Guillen, Rodrigo J.; Beckerle, Philipp; Bertram, Torsten

doi:10.1109/aim46487.2021.9517608

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…This makes it the fastest method compared to the other approaches. However, the introduction of a limited planning horizon leads to a local planning problem that departs from the original problem and promotes vulnerability to local minima [3], [12], [13]. Local minima appear in the form of suboptimal global trajectories or even standstills, since a local solution (locally optimal or not) is not necessarily optimal in the sense of the global problem.…”

Section: A Related Workmentioning

confidence: 99%

Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance

Krämer

Bertram

2022

IEEE Access

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Trajectory optimization is a promising method for planning trajectories of robotic manipulators. With the increasing success of collaborative robots in dynamic environments, the demand for online planning methods grows and offers new opportunities as well as challenges for trajectory optimization. Special requirements in terms of real-time capabilities are one of the greatest difficulties. Optimizing a short planning horizon instead of an entire trajectory is one approach to reduce computation time, which nonetheless separates the optimality of local and global solutions. This contribution introduces, on the one hand, Extended Initialization as a new approach that reduces the risk of local minima and aims at improving the quality of the global trajectory. On the other hand, the particularly critical cases in which local solutions lead to standstills are mitigated by globally guiding local solutions. The evaluation performs four experiments with comparisons to established methods like STOMP or PRM* and demonstrates the effectiveness of both approaches.

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Section: A Related Workmentioning

confidence: 99%

Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance

Krämer

Bertram

2022

IEEE Access

Self Cite

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An MPC-Based Framework for Dynamic Trajectory Re-Planning in Uncertain Environments

Lei

Lü

Laurenzi

et al. 2022

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)

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Modular reconfigurable manipulators enable quick adaptation and versatility to address different application environments and tailor to the specific requirements of the tasks. Task performance significantly depends on the manipulator's mounted pose and morphology design, therefore posing the need of methodologies for selecting suitable modular robot configurations and mounted pose that can address the specific task requirements and required performance. Morphological changes in modular robots can be derived through a discrete optimization process involving the selective addition or removal of modules. In contrast, the adjustment of the mounted pose operates within a continuous space, allowing for smooth and precise alterations in both orientation and position. This work introduces a computational framework that simultaneously optimizes modular manipulators' mounted pose and morphology. The core of the work is that we design a mapping function that implicitly captures the morphological state of manipulators in the continuous space. This transformation function unifies the optimization of mounted pose and morphology within a continuous space. Furthermore, our optimization framework incorporates a array of performance metrics, such as minimum joint effort and maximum manipulability, and considerations for trajectory execution error and physical and safety constraints. To highlight our method's benefits, we compare it with previous methods that framed such problem as a combinatorial optimization problem and demonstrate its practicality in selecting the modular robot configuration for executing a drilling task with the CONCERT modular robotic platform.

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Comparing Online Robot Joint Space Trajectory Optimization for Task Space Applications

Cited by 2 publications

References 17 publications

Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance

Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance

An MPC-Based Framework for Dynamic Trajectory Re-Planning in Uncertain Environments

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