Human operators of construction and farming equipment exhibit strong situational awareness which enables them to execute tasks while robustly handling unexpected uncertainties in the environment. Automatic control, on the other hand, is generally capable of executing repetitive and well-defined tasks more efficiently and precisely. Collaboratively achieving the tasks by combining the benefits of critical situational awareness and decision making capabilities of human operators and the efficiency and accuracy of automatic control is expected to provide improved performance of these tasks. Development of methods in learning, prediction and human-machine shared control to improve such collaborative task execution and application to hydraulic excavators is the focus of this work. In this paper, we propose a task learning method based on an operator primitives based segmentation (OPbS) and Bayesian non-parametric clustering with temporal ordering (BNPC/TO). Then, we introduce a method for predicting the operator's intent in a dynamical environment by proposing an empirical stochastic transition matrix (ESTM) and a dynamic angle difference exponential (DADE). We then provide a design for blended shared control with conflict-awareness (BSC/CA) extended from the dynamic angle difference. Finally, we evaluate the approach on a scaled hydraulic excavator test-platform for a typical earth-moving task with novice learning operators and a skilled demonstration operator.

Original languageEnglish (US)
Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538613955
StatePublished - Jul 2 2018
Event57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
Duration: Dec 17 2018Dec 19 2018

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference57th IEEE Conference on Decision and Control, CDC 2018
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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