Path planning of wheel loader type robot for scooping and loading operation by genetic algorithm

Takei, Toshinobu; Ichikawa, Kentaro; Okawa, Kazuya; Sarata, Shigeru; Tsubouchi, Takashi; Torige, Akira

doi:10.1109/iccas.2013.6704123

Cited by 17 publications

(5 citation statements)

References 6 publications

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“…Figure 5 shows the results 4 . Figure 5a shows the error between predicted next states and the actual next states on a test set for each of the three models that predict state transitions 5 . The X-axis shows how the models perform over multi-step predictions (i.e., using their own output as input for the next timestep), and the Y-axis shows the average error in millimeters, with the dashed lines indicating the standard deviation for each model.…”

Section: Resultsmentioning

confidence: 99%

“…For example, there has been a significant amount of work on legged locomotion over granular media [1,2,3]. There has also been work on automated operation of construction equipment for scooping [4,5,6,7]. Additionally, much of the work related to robotic pouring has utilized granular media rather than liquids [8,9,10,11,12].…”

Section: Related Workmentioning

confidence: 99%

“…We empirically determined that the networks performed better with pre-training than without 4. Please refer to the associated video for additional results: https://youtu.be/YUb5hi-OK705 For this portion of the analysis we don't compare with the value-net because there is no next-state prediction to compare with, although empirically the error for it did seem to converge to a reasonable value after training.…”

mentioning

confidence: 99%

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Learning Robotic Manipulation of Granular Media

Schenck,

Tompson,

Fox

et al. 2017

Preprint

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In this paper, we examine the problem of robotic manipulation of granular media. We evaluate multiple predictive models used to infer the dynamics of scooping and dumping actions. These models are evaluated on a task that involves manipulating the media in order to deform it into a desired shape. Our best performing model is based on a highly-tailored convolutional network architecture with domain-specific optimizations, which we show accurately models the physical interaction of the robotic scoop with the underlying media. We empirically demonstrate that explicitly predicting physical mechanics results in a policy that out-performs both a hand-crafted dynamics baseline, and a "value-network", which must otherwise implicitly predict the same mechanics in order to produce accurate value estimates.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Learning Robotic Manipulation of Granular Media

Schenck,

Tompson,

Fox

et al. 2017

Preprint

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“…The methods search through the variables and parameters for a set amount of time and once finished, the semioptimal path is retrieved, where it is optimally defined as the length of the path. A similar optimization algorithm is based on a genetic algorithm, where the path is assumed to include a straight line, a clothoid curve, and a circle line [39]. The optimization criterion is the path length, and the genetic algorithm runs for 1000 iterations before returning the semi-optimal path.…”

Section: Navigationmentioning

confidence: 99%

Automating the Short-Loading Cycle: Survey and Integration Framework

Borngrund,

Bodin,

Andreasson

et al. 2024

Applied Sciences

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The short-loading cycle is a construction task where a wheel loader scoops material from a nearby pile in order to move that material to the tipping body of a dump truck. The short-loading cycle is a vital task performed in high quantities and is often part of a more extensive never-ending process to move material for further refinement. This, together with the highly repetitive nature of the short-loading cycle, makes it a suitable candidate for automation. However, the short-loading cycle is a complex task where the mechanics of the wheel loader together with the interaction between the wheel loader and the environment needs to be considered. This must be achieved while maintaining some productivity goal and, concurrently, minimizing the used energy. The main objective of this work is to analyze the short-loading cycle, assess the current state of research in this field, and discuss the steps required to progress towards a minimal viable product consisting of individual automation solutions that can perform the short-loading cycle well enough to be used by early adopters. This is achieved through a comprehensive literature study and consequent analysis of the review results. From this analysis, the requirements of an MVP are defined and some gaps which are currently hindering the realization of the MVP are presented.

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“…Takei T. et al [37] applied the optimization method using a genetic algorithm to the path planning for wheel loaders on scraping and loading operations. Rozo L. et al [38] developed a parametric hidden Markov model based on force feedback to teach robots of pouring skills.…”

Section: Machine Learning For Handling Bulk Materialsmentioning

confidence: 99%