TY - GEN
T1 - Haptic-Guided Multi-Strategy for Orbit Decision-making of Satellite Flight
AU - Zhang, Yingxin
AU - Tang, Xingjian
AU - Liu, Junhui
AU - Xiang, Junyi
AU - Dong, Hanlin
AU - Ma, Zhiqiang
AU - Huang, Panfeng
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - In non-cooperative satellite pursuit-evasion missions, maneuver decision-making involves complex tactical trade-offs and imposes a substantial cognitive burden on human operators. Although game-tree-based autonomous planners provide strong computational capability, they are less effective at incorporating time-varying human intent. Meanwhile, purely manual teleoperation is constrained by the high complexity of orbital dynamics and heavy operator cognitive load. To address these challenges, this paper proposes a human-in-the-loop maneuver decision framework that combines game-tree-based multi-strategy generation with haptic virtual-fixture guidance. A game-tree planner first produces a small set of candidate pursuit orbits with distinct tactical preferences, thereby separating continuous trajectory optimization from high-level strategy selection. These candidate orbits are then mapped to haptic guide rails, allowing the operator to explore and compare them through touch. A smooth hybrid force model with distance-based weighting and multi-rail fusion provides stable attraction and continuous guidance during rail engagement and switching. Simulation results show that the proposed method maintains smooth haptic transitions across candidate strategies and enables intuitive visual-haptic coupling during interactive exploration. The framework therefore supports operator situational awareness and facilitates strategy exploration and selection in dynamic engagement scenarios.
AB - In non-cooperative satellite pursuit-evasion missions, maneuver decision-making involves complex tactical trade-offs and imposes a substantial cognitive burden on human operators. Although game-tree-based autonomous planners provide strong computational capability, they are less effective at incorporating time-varying human intent. Meanwhile, purely manual teleoperation is constrained by the high complexity of orbital dynamics and heavy operator cognitive load. To address these challenges, this paper proposes a human-in-the-loop maneuver decision framework that combines game-tree-based multi-strategy generation with haptic virtual-fixture guidance. A game-tree planner first produces a small set of candidate pursuit orbits with distinct tactical preferences, thereby separating continuous trajectory optimization from high-level strategy selection. These candidate orbits are then mapped to haptic guide rails, allowing the operator to explore and compare them through touch. A smooth hybrid force model with distance-based weighting and multi-rail fusion provides stable attraction and continuous guidance during rail engagement and switching. Simulation results show that the proposed method maintains smooth haptic transitions across candidate strategies and enables intuitive visual-haptic coupling during interactive exploration. The framework therefore supports operator situational awareness and facilitates strategy exploration and selection in dynamic engagement scenarios.
KW - game tree search
KW - haptic virtual fixtures
KW - Human-in-the-Loop
KW - maneuver decision-making
KW - satellite pursuit-evasion
KW - shared control
UR - https://www.scopus.com/pages/publications/105044195425
U2 - 10.1109/ICAISISAS68969.2026.11567917
DO - 10.1109/ICAISISAS68969.2026.11567917
M3 - 会议稿件
AN - SCOPUS:105044195425
T3 - 2026 Joint International Conference on Automation-Intelligence-Safety, ICAIS 2026 and International Symposium on Autonomous Systems, ISAS 2026
BT - 2026 Joint International Conference on Automation-Intelligence-Safety, ICAIS 2026 and International Symposium on Autonomous Systems, ISAS 2026
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2026 Joint International Conference on Automation-Intelligence-Safety, ICAIS 2026 and International Symposium on Autonomous Systems, ISAS 2026
Y2 - 8 May 2026 through 10 May 2026
ER -