TY - GEN
T1 - Blended Shared Control with Subgoal Adjustment
AU - Jin, Zongyao
AU - Pagilla, Prabhakar
AU - Maske, Harshal
AU - Chowdhary, Girish
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Combining the benefits of robust situational awareness of human operators with the efficiency and precision of automatic control has been an important topic of human-machine shared control. The emphasis is on keeping human operators in the loop while automatic control providing assistance to improve task performance. Given a task with specific subgoals, execution of a task using blended shared control involves predicting the operator's intent of subgoal transitions and deciding the blending weights for inputs from the human operator and automatic control. In this paper we address the problem of subgoal adjustment in blended shared control which is typically initiated by the operator's intent and necessary to sustain the shared control performance for changing subgoal conditions. First, we provide a method to predict operator's intent of visiting a subgoal. Based on intent prediction, we propose a method for subgoal adjustment where the adjustment is encoded by a hyperrectangle. The volume of the hyper-rectangle is obtained by using a hyperbolic slope transition function which is based on the distance between subgoals. The adjustment actions within the hyper-rectangle are facilitated by a skill-weighted action integral that takes into consideration the skill level of the operator. The approach is tested on a scaled hydraulic excavator platform with multiple novice operators and a skilled operator. Experimental results are presented and discussed.
AB - Combining the benefits of robust situational awareness of human operators with the efficiency and precision of automatic control has been an important topic of human-machine shared control. The emphasis is on keeping human operators in the loop while automatic control providing assistance to improve task performance. Given a task with specific subgoals, execution of a task using blended shared control involves predicting the operator's intent of subgoal transitions and deciding the blending weights for inputs from the human operator and automatic control. In this paper we address the problem of subgoal adjustment in blended shared control which is typically initiated by the operator's intent and necessary to sustain the shared control performance for changing subgoal conditions. First, we provide a method to predict operator's intent of visiting a subgoal. Based on intent prediction, we propose a method for subgoal adjustment where the adjustment is encoded by a hyperrectangle. The volume of the hyper-rectangle is obtained by using a hyperbolic slope transition function which is based on the distance between subgoals. The adjustment actions within the hyper-rectangle are facilitated by a skill-weighted action integral that takes into consideration the skill level of the operator. The approach is tested on a scaled hydraulic excavator platform with multiple novice operators and a skilled operator. Experimental results are presented and discussed.
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U2 - 10.1109/SMC.2018.00465
DO - 10.1109/SMC.2018.00465
M3 - Conference contribution
AN - SCOPUS:85062232629
T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
SP - 2724
EP - 2729
BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Y2 - 7 October 2018 through 10 October 2018
ER -