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论文范文
1. Introduction In recent years, unmanned air vehicles have been widely used in military and civil fields. State-of-the-art navigation schemes for unmanned aerial vehicles (UAVs) typically rely on the high-quality global position system (GPS). However, the GPS signal is not always available (indoor places, for example), so the overreliance on GPS is becoming a prominent insufficiency of UAVs [1]. As a result, the increasing interest in navigation for a GPS-denied environment has heightened the need for novel GPS-free integrated navigation schemes. Inspired by flying insects (e.g., honeybees) that have the ability to fly with great agility, scientists hope that micro air vehicles (MAVs) could fly like the insects without the aid of GPS signals [2]. Experiments show that one of the navigation information sources is measuring the moving velocity of the image in the world appearing in the eye when honeybees fly to their destination [3]. Researchers think that what the honeybees are mainly relying on is the apparent motion of the objects in their field of view, namely, optical flow, which contains egomotion information relative to the environment of the flying insects [4]. On the other hand, the requirement for more self-reliant (autonomous) navigation systems and the need for MAV with a greater understanding of their environment are becoming more and more urgent. It is obvious that insects like honeybees, even with their small brains and limited intelligence, have the accurately autonomous navigation ability described above. Accordingly, bioinspired optical flow sensors are developed with the advantage of smaller size, lighter weight, low power requirement, higher frequency, and lower cost compared to other equipments such as lidar, radar, and magnetometer [5]. Chahl [6] has also pointed out that the largest development opportunities may exist in small and micro UAV domains as a result of the novelty of aerospace engineering on a small scale. Therefore, autonomous navigation or pose estimation using optical flow sensors is valuable for MAVs. This has motivated many researches into optical-flow-based MAV navigation systems and algorithms. Ruffier and Franceschini [7] have developed an optical flow regulation loop-based MAV which is able to take off, cruise, and land automatically. The MAV can also react appropriately to wind disturbances. This MAV can keep the downward optical flow at a constant value. Furthermore, the two-degree-of-freedom (DOF) tethered MAV was shown to land safely on a platform set into motion along two directions, vertical and horizontal, without ground height or groundspeed information [8]. The results show that their MAV works very well. However, the MAV has only two DOF, which is a little far from the 6 DOF practical MAV. As stated earlier, the optical flow contains the egomotion information of honeybees or MAVs. Consequently, it is a reasonable navigation frame that combines the optical flow and the inertial navigation system (INS) measurement information in order to improve the navigation precision. Sabo et al. [9] performed an approach for bioinspired navigation using optical flow. Hallway navigation results show that the roll, pitch, and thrust can be tuned and controlled as expected. The deficiency is that the optical flow was calculated off-board using a computer vision algorithm, rather than taking advantage of optical flow sensors with small computation and short response time. 
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