Multigrid CHOMP with Local Smoothing

He, Keliang; Martin, Elizabeth; Zucker, Matt

doi:10.1109/humanoids.2013.7029993

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…This method is different from CHOMP in that it is optimized by a specific optimization algorithm and does not require gradient information. In recent years, CHOMP and STOMP have also received great attention and further research [29][30][31]. In this article, we use the planning request adapter in Moveit to modify the trajectory created by the sampling algorithm in OMPL.…”

Section: Related Workmentioning

confidence: 99%

Benchmarking and optimization of robot motion planning with motion planning pipeline

Liu

2021

Int J Adv Manuf Technol

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Algorithms have been designed for robot motion planning with various adaptability to different problems. However, how to choose the most suitable planner in a scene has always been a problem worthy of research. This paper aims to find the most suitable motion planner for each query under three different scenes and six different queries. The work lies in optimization of sampling-based motion planning algorithms through motion planning pipeline and planning request adapter. The idea is to use the pre-processing of the planning request adapter, to run OMPL as a pre-processer for the optimized CHOMP or STOMP algorithm, and connect through the motion planning pipeline, to realize the optimization of the motion trajectory. The optimized trajectories are compared with original trajectories through benchmarking. The benchmarking determines the most suitable motion planning algorithm for different scenarios and different queries. Experimental results show that after optimization, the planning time of the algorithm is longer, but the efficiency is significantly improved. In the low-complexity scenes, STOMP optimizes the sampling algorithm very well, improves the trajectory quality greatly, and has a higher success rate. CHOMP also has a good optimization of the sampling algorithm, but it reduces the success rate of the original algorithm. However, in more complex scenes, optimization performance of the two optimization methods may not be as good as the original algorithm. In future work, we need to find better algorithms and better optimization algorithms to tackle with complex scenes.

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Section: Related Workmentioning

confidence: 99%

Benchmarking and optimization of robot motion planning with motion planning pipeline

Liu

2021

Int J Adv Manuf Technol

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“…Sampling-based algorithms (Kavraki et al, 1996; Kuffner and LaValle, 2000; LaValle, 2006) can effectively find feasible trajectories for high-dimensional systems, but the trajectories often exhibit jerky and redundant motion and therefore require post-processing to address optimality. Although optimal planners (Karaman and Frazzoli, 2010) have been proposed, they are computationally inefficient on high-dimensional problems with challenging constraints.…”

Section: Introductionmentioning

confidence: 99%

Continuous-time Gaussian process motion planning via probabilistic inference

Mukadam

Dong

Yan

et al. 2018

The International Journal of Robotics Research

166

196

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We introduce a novel formulation of motion planning, for continuous-time trajectories, as probabilistic inference. We first show how smooth continuous-time trajectories can be represented by a small number of states using sparse Gaussian process (GP) models. We next develop an efficient gradient-based optimization algorithm that exploits this sparsity and Gaussian process interpolation. We call this algorithm the Gaussian Process Motion Planner (GPMP). We then detail how motion planning problems can be formulated as probabilistic inference on a factor graph. This forms the basis for GPMP2, a very efficient algorithm that combines GP representations of trajectories with fast, structure-exploiting inference via numerical optimization. Finally, we extend GPMP2 to an incremental algorithm, iGPMP2, that can efficiently replan when conditions change. We benchmark our algorithms against several samplingbased and trajectory optimization-based motion planning algorithms on planning problems in multiple environments. Our evaluation reveals that GPMP2 is several times faster than previous algorithms while retaining robustness. We also benchmark iGPMP2 on replanning problems, and show that it can find successful solutions in a fraction of the time required by GPMP2 to replan from scratch.

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Learning Implicit Priors for Motion Optimization

Urain

Lambert

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Multigrid CHOMP with Local Smoothing

Cited by 5 publications

References 12 publications

Benchmarking and optimization of robot motion planning with motion planning pipeline

Benchmarking and optimization of robot motion planning with motion planning pipeline

Continuous-time Gaussian process motion planning via probabilistic inference

Learning Implicit Priors for Motion Optimization

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