MODERN MANNED, UNMANNED AND TELEOPERATED EXCAVATOR SYSTEM

This paper presents a re-evaluation on the modern development and practical use of manned, unmanned and teleoperated construction vehicles in universities around the world, which focuses on the use of robotized excavators. Unmanned operation is becoming synonymous in the extreme environment operation. The operation is also becoming important in order to increase working efficiency and situational awareness. The review includes the theoretical, experimental and practical applications of such technology in the present days, particularly for excavators. Various innovation and control methods have been studied over the years by various entities, which provide the significant contribution by the scientific community to the progressing world.

자동화 굴착기를 위한 숙련자 굴착력 패턴 기반 굴착 작업 궤적 생성

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 이동준.In this thesis, we propose an excavation trajectory generation framework for autonomous excavators based on expert operator forcing pattern. The primary focus is to develop autonomous excavator system which is stable and guarantees a certain quantity of excavation in various surroundings. We nd the excavation trajectories based on the terrain features and the excavation forcing patterns from the excavation data of expert operators. The expert excavation trajectories are encoded with dynamic movement primitives (DMP) and learn through multilayer perceptron (MLP). The excavation trajectory is generated according to the terrain feature using the trained model. The excavator is modeled with 3-DoF rigid body system, and the excavation force on the bucket tip is estimated online by using the momentum-based disturbance observer(DOB). The estimated force is added to the DMP as a coupling term to modulate the excavation trajectory in real-time so that the estimated force can follow the expert excavation force pattern. Lastly, we verify the performance of the suggested framework through simulation and actual excavator test.본 논문에서는 자동화 굴착기를 위한 숙련자 굴착력 패턴 기반 굴착 작업 궤적 계획 프레임워크를 제시한다. 본 프레임워크는 자동화 굴착기의 다양한 작업 환경에서 숙련자와 유사하게 안정된 굴착 작업을 수행하며, 굴착량이 보장되는 작업을 하는 것이 목표이다. 우선 숙련된 굴착기 작업자들의 굴착 작업 데이터로부터 지형 특징에 기반한 작업 궤적과 굴착력 패턴을 찾아내었다. 숙련자의 굴착 궤적은 dynamic movement primitives(DMP)으로 encoding하여 neural network의 한 기법인 multi-layer perceptron(MLP)을 통해 학습하고, 학습된 모델을 기반으로 지형에 따른 굴착 궤적을 생성하였다. 굴착기를 다자유도 강체 시스템으로 모델링 하고, 실시간으로 버켓 끝단에 걸리는 굴착력을 momentum-based disturbance observer를 이용하여 추정하였다. 추정된 굴착력은 실시간으로 굴착 궤적을 재생성 하기위해 DMP에 coupling term으로 추가하였고, 이를 통해 추정되는 굴착력이 숙련자의 굴착 패턴을 따라갈 수 있도록 제어하였다. 마지막으로 제안한 프레임워크에 대해서는 시뮬레이션 실험과 실제 굴착기를 이용한 실험을 통해 정합성 검증을 진행하였다.1 Introduction 1 1.1 Motivation and Objectives 1 1.2 Related Work 2 1.3 Contribution 4 2 Preliminary 6 2.1 System Description 6 2.2 Excavator Dynamic Modeling 7 2.3 Force Estimation via Momentum Based Disturbance Observer 9 2.4 Dynamic Movement Primitives 10 3 Excavation Trajectory Generation 13 3.1 Analysis of Expert's Excavation Trajectory 13 3.2 Generate Nominal Excavation Trajectory by Imitating Expert Operator 19 3.3 Modulate Excavation Trajectory by Force Pattern of Expert Operator 22 4 Experiments 26 4.1 Excavation Simulation 26 4.1.1 Excavation on Flat and Slope Terrain 26 4.1.2 Excavation using Trajectory Generated by Incorrect Terrain Recognition 31 4.1.3 Excavation with Obstacle in the Ground 33 4.2 Excavation Test Result using Excavator 35 5 Conclusion and Future Work 40 5.1 Conclusion 40 5.2 Future Work 41Maste