Operational safety prediction of unmanned systems based on trajectory planning
ID:13 Submission ID:2 View Protection:ATTENDEE Updated Time:2024-10-23 10:39:11 Hits:38 Oral Presentation

Start Time:2024-11-01 16:40 (Asia/Shanghai)

Duration:20min

Session:[P3] Parallel Session 3 » [P3-1] Parallel Session 3(November 1 PM)

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Abstract
When unmanned systems operate autonomously, it is important to predict and evaluate the operational safety of the evaluation system. The evaluation of operational safety depends on the method of predicting the operating state of unmanned systems. However, short time prediction methods based on physical drive or partial data drive can only ensure the security of the operating state of the unmanned system during operation, but cannot guarantee that the operation will not exceed the predetermined safety area from a global perspective. In order to solve this problem, this paper proposes a long time prediction method for operational safety based on path planning. A* algorithm is used for path planning, and the operation safety probability expression of unmanned system is obtained through repeated planning, and then the operation safety of unmanned system is quantitatively evaluated. The effectiveness of the proposed method is verified by experiments. In addition, this global prediction method can be deployed by the airborne computing platform to realize the planning and confirmation of the safe operation path of the unmanned system, so as to ensure the autonomous and safe operation of the unmanned system.
Keywords
Unmanned systems,Operational safety prediction,A* algorithm
Speaker
LiZhuo
Master Degree Studen Harbin Institute of Technology

Submission Author
LiZhuo Harbin Institute of Technology
WangYingqi Harbin Institute of Technology
ZhangWeiqi Harbin Institute of Technology
宋宇晨 Harbin Institute of Technology
刘大同 哈尔滨工业大学
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Important Dates

15th August 2024   31st August 2024- Manuscript Submission

15th September 2024 - Acceptance Notification

1st October 2024 - Camera Ready Submission

1st October 2024  – Early Bird Registration

 

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