[1]XU Yaosong,WANG Chuanwei.FOA and improved D-S evidence theory for quadcopter obstacle identification[J].CAAI Transactions on Intelligent Systems,2020,15(3):499-506.[doi:10.11992/tis.201809011]
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FOA and improved D-S evidence theory for quadcopter obstacle identification
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 3
Page number:
499-506
Column:
学术论文—智能系统
Public date:
2020-05-05
- Title:
- FOA and improved D-S evidence theory for quadcopter obstacle identification
- Keywords:
- quadcopter; obstacle avoidance; ultrasonic sensor; infrared distance sensor; lidar sensor; multisensor information fusion; FOA; D-S evidence theory
- CLC:
- TP14
- DOI:
- 10.11992/tis.201809011
- Abstract:
- Aiming at the problem that the quadrilateral aircraft has poor recognition effect and low precision, we studied the method of quadcopter obstacle recognition using a multisensor based on an ultrasonic sensor, infrared ranging sensor, and lidar sensor. The original data evidence weight of the sensor was optimized using the fruit-fly optimization algorithm (FOA) to obtain the optimal weight. According to the optimal weight of each sensor, an improved D-S evidence theory algorithm was used to fuse the data of multiple sensors to improve the obstacle recognition accuracy of the quadcopter. By comparing the single sensor and other data fusion algorithms, the research results show that under the same condition, the proposed method has a higher recognition accuracy for obstacles and faster response to obstacles.
- References:
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1900-01-01