Optic Flow Based Autonomous Tasks

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Introduction
Some Theory
Microsensors
Autonomous Tasks
Videos
Useful Links

Introduction

It is evident that flying insects, like honeybees, utilize optic flow sensors to maneuver through regions with dense obstacle fields. Optic flow refers to the apparent movement of texture in the visual field relative to the insect’s velocity. Without GPS or IMUs for navigation, insects perform tasks like collision avoidance, altitude control, takeoff and landing and can therefore serve as a model for micro air vehicle (MAV) flight patterns in such environments. For example, while in flight, objects which are in close proximity to the insect have higher optic flow magnitudes. The video below demonstrates this. The simulation starts out with the insect very close to the left wall of the tunnel. Thus, the left wall appears to be moving with a higher velocity than the right wall. Halfway down the tunnel, the insect moves over to the right side of the wall and the opposite is true - the right wall appears to be moving with a higher velocity. Flying insects, such as fruit flies and dragon flies, avoid imminent collisions by saccading (or turning) away from regions of high optic flow (see Figure 1). Applying such insect-inspired navigational methods to aerial robots is possible due to the development of optic flow microsensors weighing under 10 grams.

Simulation of insect's perspective while flying down tunnel

Figure 1: Dragon fly saccading away from regions of high optic flow in order to avoid a collision

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Theory & Background

Theoretically, optic flow is measured in rad/sec and is a function of the UAV’s forward velocity, V , angular velocity, w, distance D from an object, and the angle theta, between the MAV’s direction of travel and the object (see Figure 2). For a more detailed theoretical explanation, please click here

Figure 2: Optic flow parameters during MAV flight.

Figure 3 depicts optic flow as it might be seen by a MAV traveling a straight line above the ground. The focus of expansion (FOE) in the forward sensor view indicates the direction of travel. If the FOE is located inside a rapidly diverging region, then a collision is imminent. A rapidly expanding region to the right of the FOE (like the one seen in the Figure 3) corresponds to an obstacle approaching on the right side of the MAV. Thus, the MAV should turn left, or away from the region of high optic flow, to avoid the collision. Similarly, the MAV can estimate its height from the optic flow in the downward direction; faster optic flow indicates a low flight altitude. By equiping a MAV with sensors capable of measuring the optic flow in front of and below the aircraft, the above flight patterns can be embedded in a sensor suite for autonomous navigation.

Figure 3: Optic flow as seen by aerial robot flying above ground.

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Microsensors

Nueromorphic chips have been available for many years. However, to achieve the desired weight of 1-2 grams, mixed-mode and mixed-signal VLSI techniques are used to develop compact circuits that directly perform computations necessary to measure optic flow. Centeye has developed the one-dimensional Ladybug optic flow microsensor based on such tecniques and is shown in Figure 4. These sensors are inspired by the general optic flow model of animal visual systems (see Figure 5). A lens focuses an image of the environment onto a focal plane chip, which contains photoreceptor circuits and other circuits necessary to compute optic flow. Low level feature detectors respond to different spatial or temporal entities in the environment, such as edges, spots, or corners. The elementary motion detector (EMD) is the most basic structure or entity that senses visual motion, though its output may not be in a form easily used. Fusion circuitry fuses information from the EMDs to reduce errors, increase robustness, and produces a meaningful representation of the optic flow for specific applications.

Figure 4: Mixed-mode VLSI optic flow microsensor.

Figure 5: Overall sensor architecture.

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Autonomous Tasks

Near-Earth environments are complex and dynamic and thus, require autonomous or semi-autonomous vehicles to fly safely in. As such, an aerial robot must be highly maneuverable while capable of demonstrating tasks and control schemes such as path planning, localization, stabilization and takeoff and landing. Sensing the one-dimensional optic flow in the downward and forward directions allows MAVs to perform automated landings and collision avoidance, respectively.

The control system block diagram and flow chart are shown in Figures 9 and 10 respectively. When approaching a landing, an embedded microcontroller, will implement a function to gradually throttle down the motor while continuing to take readings throughout the landing process. The error, e(t), is computed between the desired optic flow, oi which was estimated beforehand, and the actual optic flow value, of (t). When the optic flow on the landing surface becomes larger than the desired optic flow, the error is negative and two conditions are possible. One, the forward velocity, V , could be significantly increasing which is not possible based on the motor function. Two, the altitude, D, can be decreasing at a faster rate than V . Here, the controller will send a signal to the elevator to decrease the vehicle’s descent rate based on the error magnitude and proportional constant, Ka. The other possibility is that the optic flow could start to dip below the desired level causing the error to be positive. The two possible cases that arise here are one, D is increasing but again this is not practical while in landing mode and two, V is decreasing faster than D. In this case, the controller will need to command the elevator to increase the descent rate. After a control sequence has been implemented to force the optic flow back to the desired value, the elevator resets to its neutral position. By implementing this control scheme, we were able to successfully demonstrate an autonomous landing (see videos below).