期刊
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
卷 29, 期 1, 页码 18-28出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCST.2019.2959536
关键词
Task analysis; Safety; Hydraulic systems; Bayes methods; Wheelchairs; Mobile robots; Blended shared control (BSC); collaborative robotics; human performance augmentation; human-centered automation; robotics in construction
资金
- NSF NRI [1527828]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1527828] Funding Source: National Science Foundation
This article focuses on developing efficient methods for the collaborative blending of control inputs from human operators and automatic control in hydraulic excavators. By utilizing task learning, intent prediction, and blended shared control techniques, significant safety and performance benefits can be achieved in dynamic and uncertain construction environments.
Human operators of construction equipment usually exhibit strong situational awareness, which enables them to execute tasks while handling unexpected uncertainties and adapting to environmental changes. Automatic control, on the other hand, is generally capable of executing repetitive and well-defined tasks more efficiently and precisely. In dynamic and uncertain environments, significant safety and performance benefits can be derived by collaboratively achieving the tasks via the blending of control inputs from the human operator and automatic control. The focus of this article is on developing efficient methods for such blending and its application to hydraulic excavators, which involves the developments in task learning, intent prediction, and human-machine shared control. We propose a new task learning method by segmenting the tasks with the operator primitive-based segmentation (OPbS) and clustering of subgoals via Bayesian nonparametric clustering with temporal ordering (BNPC/TO). We introduce a method for dynamically predicting the operator's intent in seeking a particular subgoal by proposing an empirical stochastic transition matrix (ESTM) and a dynamic angle difference exponential (DADE). We then propose a method for blended shared control with conflict awareness extended from dynamic angle difference. Finally, we apply our algorithms and evaluate the results on a scaled hydraulic excavator test platform for a typical earth-moving task with novice learning operators and a skilled demonstration operator.
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