Forward-Looking Echoic Flow for Guidance of an Unmanned Aerial System

Echoic flow is a formula derived from natural phenomena that has the potential to control vehicles with great efficiency using range information. Initially studied in bats, echoic flow allows animals to use sonar as a navigation tool. Downward-facing echoic flow used in the vertical landing of an Unmanned Aerial System (UAS) has been studied in past research. Forward-looking echoic flow on a UAS could allow for new approaches to braking and following techniques in the horizontal plane of motion towards both fixed and moving targets. The goal of this project was to demonstrate forward-looking echoic flow guidance towards a fixed target using a quadcopter and to gather data showing the accuracy and precision of the process. In initial forward-looking tests, a modified Parrot AR Drone with an added ultrasonic sensor and Raspberry Pi were used as the UAS. Preliminary findings showed erratic and often inaccurate range finding measurements. These measurements were attributed in part to the inability of the UAS to aim directly at the small target. A software filter was designed to minimize the impact of erroneous measurements. Further tests conducted using a flat wall as the approach target still yielded trials that did not follow the ideal echoic flow approach accurately. In an attempt to improve the performance of trials, the equation used to convert velocities to motor thrust values was recalibrated. Though trial results did improve due to this modification, imprecise quadcopter movement control prevented the achievement of a smooth echoic flow approach. Finally, simulations of forward-looking trials were performed to test the impact of measurement and velocity error on the performance of echoic flow approaches. The values of measurement error that resulted in acceptable echoic flow performance were found to be lower than the expected values for the UAS in this study. Further forward-looking echoic flow research is recommended using a more accurate and robust rangefinder. A UAS capable of more precise horizontal plane movement is also recommended.No embargoAcademic Major: Electrical and Computer Engineerin

Kalman Filter for Noise Reduction in Aerial Vehicles using Echoic Flow

Echolocation is a natural phenomenon observed in bats that allows them to navigate complex, dim environments with enough precision to capture insects in midair. Echolocation is driven by the underlying process of echoic flow, which can be broken down into a ratio of the distance from a target to the velocity towards it. This ratio produces a parameter τ representing the time to collision, and controlling it allows for highly efficient and consistent movement. When a quadcopter uses echoic flow to descend to a target, measurements from the ultrasonic range sensor exhibit noise. Furthermore, the use of first order derivatives to calculate the echoic flow parameters results in an even greater magnitude of noise. The implementation of an optimal Kalman filter to smooth measurements allows for more accurate and precise tracking, ultimately recreating the high efficiency and consistency of echolocation tracking techniques found in nature. Kalman filter parameters were tested in realistic simulations of the quadcopter's descent. These tests determined an optimal Kalman filter for the system. The Kalman filter's effect on an accurate echoic flow descent was then tested against that of other filtering methods. Of the filtering methods tested, Kalman filtering best allowed the quadcopter to control its echoic flow descent in a precise and consistent manner. In this presentation, the test methodology and results of the various tests are presented.No embargoAcademic Major: Electrical and Computer Engineerin

An examination of Echoic Flow based autonomous guidance using the Lego Mindstorms NXT robot

Distinguished Undergraduate Research Proposal from The College of EngineeringIt is well known that some animals, such as bats, can find their routes home autonomously and are able to avoid crashing into each other while traveling in a group. They do this using echolocation that enables them to function at dusk and in the dark. My research project is to investigate how these complicated behaviors can be replicated using robot vehicles that have echolocation sensors. Echoic flow fields are computed and simple rules to govern subsequent behavior are implemented. Echoic flow is defined to be the ratio of a sensed parameter such as range or intensity to a change in that parameter per unit time. The ratio of these two quantities gives the time over which two bodies will come into contact (collide). This “time to collision” can be used to provide feedback to the robot to either avoid collisions or to control the form of a collision. Previous theoretical research has shown that echoic flow can be used to control the behaviors of objects in relative motion. Experimental work has shown that the Lego NXT robot and its ultrasonic echolocation sensors can enable obstacle avoidance. My project develops extends this earlier experimental research to determine if echoic flow can be used to control two robots so that one leads and has to avoid obstacles as it autonomously navigates a course while the other follows the first robot maintaining a constant time to collision.No embargoAcademic Major: Electrical and Computer Engineerin

Complex Behavior from a Simple Rule: Demonstration with Lego Mindstorms NXT Kit

2013 Denman Undergraduate Research Forum Winner. Second Place.This report describes the successful implementation of two robots, built using Lego Mindstorms, that demonstrate how complex animal behaviors can be replicated using a simple algorithm to replicate a two neuron nervous system. The process of cognition and decision making inside the mammalian brain occurs subtly but near instantaneously and in a way that makes it hard to replicate synthetically. However, the understanding of its behavior is valuable in multiple disciplines and it may be applied to future technologies. In order to explore some behavior that relates to sensing and cognition, the Lego Mindstorms NXT robot kit has been used. It is a sophisticated kit that includes a programmable embedded computer, known as ‘the Brick’. This brick controls the mechanical system made up from a set of modular Lego sensors and motors as well as Lego parts. The base set of equipment and the customized add-ons provide an open-ended platform that makes it possible to test a number of complex theories. The main objective of the proposed project is to demonstrate how a complex behavior can be simulated just based on some simple rule that represents the operation of neurons. After investigating the capability and limitation of critical sensor used for the robot and the motor specifications, the female cricket’s behavior of locating her mate in dark with sound signals only, has been mechanically mimicked on Lego using two sound sensors and two motors. Subsequently the echo location process of a bat using echoic flow theory has been studied for collision avoidance. Preliminary results have had successful and constant performance showing the potential of using Echoic Flow for steering control on vehicles. This approach offers scientific researchers with an alternative to test and experiment their hypothesis before applying it to large scale or real-life test subjects, especially in cognitive sensing or intelligent control.No embarg

Memory-augmented cognitive radar for obstacle avoidance using nearest steering vector search

Abstract This study describes a cognitive radar architecture with application to real‐time obstacle avoidance in mobile robotic platforms. The concept of a world memory map is introduced as a means of providing an enhanced perception of the environment around the robotic platform. This is combined with a specially designed obstacle avoidance algorithm, Nearest Steering Vector Searching, all capable of operating in real‐time. The study analytically derives the radar signal processing algorithm, starting from range‐angle maps, so that a collision free course to a set destination point can be robustly navigated. Finally, the performance of this cognitive approach is examined through a number of proof‐of‐concept experiments using a commercial off‐the‐shelf radar mounted on a mobile ground robotic platform

Biologically-inspired radar sensing

The natural world has an unquantifiable complexity and natural life exhibits remarkable techniques for responding to and interacting with the natural world. This thesis aims to find new approaches to radar systems by exploring the paradigm of biologically-inspired design to find effective ways of using the flexibility of modern radar systems. In particular, this thesis takes inspiration from the astonishing feats of human echolocators and the complex cognitive processes that underpin the human experience. Interdisciplinary research into human echolocator tongue clicks is presented before two biologically-inspired radar techniques are proposed, developed, and analyzed using simulations and experiments. The first radar technique uses the frequency-diversity of a radar system to localize targets in angle, and the second technique uses the degrees-of-freedom accessible to a mobile robotic platform to implement a cognitive radar architecture for obstacle avoidance and navigation

Proposed ontology for cognitive radar systems

Cognitive radar is a rapidly developing area of research with many opportunities for innovation. A significant obstacle to development in this discipline is the absence of a common understanding of what constitutes a cognitive radar. The proposition in this study is that radar systems should not be classed as cognitive, or not cognitive, but should be graded by the degree of cognition exhibited. The authors introduce a new taxonomy framework for cognitive radar against which research, experimental and production systems can be benchmarked, enabling clear communication regarding the level of cognition being discussed

Biologically Inspired Guidance for Autonomous Systems

Animals and humans can perform purposeful actions using only their senses. Birds can perch on branches; bats use echolocation to hunt prey and humans are able to control vehicles. It must therefore be possible for autonomous systems to replicate this autonomous behaviour if an understanding of how animals and humans perceive their environment and guide their movements is obtained. Tau theory offers a potential explanation as to how this is achieved in nature. Tau theory posits, that in combination with the so-called ‘motion guides’, animals and humans perform useful movements by closing action-gaps, i.e. gaps between the current state and a desired state. The theory suggests that the variabl