Spatial image model for manipulation of shape variable objects and application to excavation

Homma, Keiko; Nakamura, Tomomi; Arai, Tamio; Adachi, H.

doi:10.1109/iros.1990.262470

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…Singh and Simmons [4] used a simple heuristic soil model proposed by Homma et. al [5]. Attempts at modeling these interactions using discrete-element method [6] are met with computational challenges that make these models infeasible for real-time applications.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators

Sandzimier

Asada

2020

IEEE Robot. Autom. Lett.

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We develop a data-driven, statistical control method for autonomous excavators. Interactions between soil and an excavator bucket are highly complex and nonlinear, making traditional physical modeling difficult to use for realtime control. Here, we propose a data-driven method, exploiting data obtained from laboratory tests. We use the data to construct a nonlinear, non-parametric statistical model for predicting the behavior of soil scooped by an excavator bucket. The prediction model is built for controlling the amount of soil collected with a bucket. An excavator collects soil by dragging the bucket along the soil surface and scooping the soil by rotating the bucket. It is important to switch from the drag phase to the scoop phase with the correct timing to ensure an appropriate amount of soil has accumulated in front of the bucket. We model the process as a heteroscedastic Gaussian process based on the observation that the variance of the collected soil mass depends on the scooping trajectory, i.e. the input, as well as the shape of the soil surface immediately prior to scooping. We develop an optimal control algorithm for switching from the drag phase to the scoop phase at an appropriate time and for generating a scoop trajectory to capture a desired amount of soil with high confidence. We implement the method on a robotic excavator. Experiments show promising results. Index Terms-Data-driven control, Gaussian process, robotics in construction, mining robotics, field robots, model learning for control, optimization and optimal control, probability and statistical methods

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Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators

Sandzimier

Asada

2020

IEEE Robot. Autom. Lett.

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“…In this project an automated excavating robot will be developed to help construct an underground dome of approximately 50 m diameter and 30 m height. The Mechanical Engineering Laboratory is engaged in the development of key technologies for the robot and is making basic studies on a) excavation manipulator, b) task planning for excavation, and c) walking vehicles [ 11- [3]. In our preliminary design the robot system should consist of a powerful manipulator on a walking vehicle with a dedicated task planner.…”

Section: Introductionmentioning

confidence: 99%

Integration of parallel arm and legged mechanism

Arai

Adachi

Homma

et al.

Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative R

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An excavation robot is required to build a large scale underground dome. The excavation robot is developed which is composed of a parallel manipulator and four leg locomotion machine. Although the parallel manipulator has a nice feature of producing large force required for the excavation, cooperative control should be considered to achieve effective excavation. This paper discusses integrated kinematics f o r r legs and parallel arm mechanism for analysis of the mechanism and its control. Based on the derived kinematics, evaluation criteria are introduced in order to design mechanism itself as well as motion controller. Numerical examples show how the proposed methods work effectively to analyze motion of the integrated mechanism.

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State of the Art in Automation of Earthmoving

Singh

1997

J. Aerosp. Eng.

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Spatial image model for manipulation of shape variable objects and application to excavation

Cited by 7 publications

References 4 publications

A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators

A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators

Integration of parallel arm and legged mechanism

State of the Art in Automation of Earthmoving

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