障碍物
固定翼
试验台
激光雷达
避障
全球导航卫星系统应用
计算机科学
弹道
软件
实时计算
模拟
航空航天工程
遥感
全球定位系统
工程类
翼
人工智能
机器人
电信
移动机器人
地理
天文
程序设计语言
考古
物理
作者
Konstantinos A. Tsintotas,Loukas Bampis,Anastasios Taitzoglou,Ioannis Kansizoglou,Pavlos Kaparos,C Bliamis,Kyros Yakinthos,Αντώνιος Γαστεράτος
标识
DOI:10.1109/tim.2022.3225020
摘要
Unmanned aerial vehicles (UAVs) are at the forefront of this century's technological shift, becoming ubiquitous in research and market areas. However, as a UAV navigates autonomously, there are unanticipated occasions, e.g., collisions with dynamic obstacles or loss of data provided by the global navigation satellite system (GNSS), where the aircraft has to change its mission plans. In particular, to be protected from possible accidents, the platform's geofence protection system should adjust the trajectory appropriately when obstacles are detected and select the proper ground surface when emergency landing is prompted. As these processes require fast reaction times, utilizing low latency sensors and algorithms is necessary. This article proposes a complete and low-complexity geofence protection system for recognizing moving objects and assessing the ground surface's suitability using onboard sensing and processing modules. The proposed system is implemented on a novel fixed-wing UAV, designated as MPU RX-4, which features an unconventional flying wing layout and vertical take-off and landing (VTOL) capabilities. Our system is based on a forward-facing laser imaging, detection, and ranging (LIDAR) sensor and three downward-facing laser rangefinders. We take advantage of the high-precision distance measurements and operational speed to identify moving obstacles using the LIDAR module, while the ground's slope and the existence of any obstacle therein are computed through the rangefinders. First, the article describes the UAV design procedure and its aerodynamic performance characteristics, which allowed us to evaluate our approach on a testbed aircraft. Then, the evaluation protocol shows that our system can perform robustly and under real-time constraints reaching an overall latency of only 165.5 ms, sufficient for reliable detection and avoidance of moving obstacles.
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