Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance
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. S...
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Format: | Article |
Language: | English |
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IEEE
2022-01-01
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Series: | IEEE Access |
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Online Access: | https://ieeexplore.ieee.org/document/9733921/ |
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author | Maximilian Kramer Torsten Bertram |
author_facet | Maximilian Kramer Torsten Bertram |
author_sort | Maximilian Kramer |
collection | DOAJ |
description | 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 Stochastic Trajectory Optimization for Motion Planning (STOMP) or Probabilistic Roadmap Method (PRM*) and demonstrates the effectiveness of both approaches. |
first_indexed | 2024-12-17T00:50:59Z |
format | Article |
id | doaj.art-3016077a28a74ea9875d59223d0270c7 |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-12-17T00:50:59Z |
publishDate | 2022-01-01 |
publisher | IEEE |
record_format | Article |
series | IEEE Access |
spelling | doaj.art-3016077a28a74ea9875d59223d0270c72022-12-21T22:09:46ZengIEEEIEEE Access2169-35362022-01-0110296332964510.1109/ACCESS.2022.31592339733921Improving Local Trajectory Optimization by Enhanced Initialization and Global GuidanceMaximilian Kramer0https://orcid.org/0000-0003-0179-0684Torsten Bertram1Institute of Control Theory and Systems Engineering, TU Dortmund University, Dortmund, GermanyInstitute of Control Theory and Systems Engineering, TU Dortmund University, Dortmund, GermanyTrajectory 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 Stochastic Trajectory Optimization for Motion Planning (STOMP) or Probabilistic Roadmap Method (PRM*) and demonstrates the effectiveness of both approaches.https://ieeexplore.ieee.org/document/9733921/Moving horizon planningonline trajectory optimizationlocal minima |
spellingShingle | Maximilian Kramer Torsten Bertram Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance IEEE Access Moving horizon planning online trajectory optimization local minima |
title | Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance |
title_full | Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance |
title_fullStr | Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance |
title_full_unstemmed | Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance |
title_short | Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance |
title_sort | improving local trajectory optimization by enhanced initialization and global guidance |
topic | Moving horizon planning online trajectory optimization local minima |
url | https://ieeexplore.ieee.org/document/9733921/ |
work_keys_str_mv | AT maximiliankramer improvinglocaltrajectoryoptimizationbyenhancedinitializationandglobalguidance AT torstenbertram improvinglocaltrajectoryoptimizationbyenhancedinitializationandglobalguidance |