Evolving a Multiagent Controller for Micro Aerial Vehicles

Micro Aerial Vehicles (MAVs) are notoriously difficult to control as they are light, susceptible to minor fluctuations in the environment, and obey highly non-linear dynamics. Indeed, traditional control methods, particularly those relying on difficult to obtain models of the interaction between an MAV and its environment have been unable to provide adequate control beyond simple maneuvers. In this paper, we address the problem of controlling an MAV (which has segmented control surfaces) by evolving a neurocontroller and fine-tuning it using multiagent coordination techniques. This approach is based on a control strategy that learns to map MAV states (position, velocity) to MAV actions (e.g., actuator position) to achieve good performance (e.g., flight time) by maximizing an objective function. The main difficulty with this approach is defining the objective functions at the MAV level that allow good performance. In addition, to provide added robustness, we investigate a multiagent approach to control where each control surface aims to optimize a local objective. Our results show that this approach not only provides good MAV control, but provides robustness to (i) wind gusts by a factor of six; (ii) turbulence by a factor of four; and (iii) hardware failures by a factor of eight over a traditional control method.This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by IEEE-Institute of Electrical and Electronics Engineers and can be found at: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5326. ©2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Keywords: Neuro-Evolution, Evolutionary Control, Multiagent Control, Micro Aerial Vehicle

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

Design and Implementation of a Control System for a Quadrotor MAV

The quadrotor is a 200 g MAV with rapid-prototyped rotors that are driven by four brushless electric motors, capable of a collective thrust of around 400 g using an 11 V battery. The vehicle is compact with its largest dimension at 188 mm. Without any feedback control, the quadrotor is unstable. For flight stability, the vehicle incorporates a linear quadratic regulator to augment its dynamics for hover. The quadrotor's nonlinear dynamics are linearized about hover in order to be used in controller formulation. Feedback comes both directly from sensors and a Luenberger observer that computes the rotor velocities. A Simulink simulation uses hardware and software properties to serve as an environment for controller gain tuning prior to flight testing. The results from the simulation generate stabilizing control gains for the on-board attitude controller and for an off-board PC autopilot that uses the Vicon computer vision system for position feedback. Through the combined effort of the on-board and off-board controllers, the quadrotor successfully demonstrates stable hover in both nominal and disturbed conditions

An Innovative Approach for Data Collection and Handling to Enable Advancements in Micro Air Vehicle Persistent Surveillance

The success of unmanned aerial vehicles (UAV) in the Iraq and Afghanistan conflicts has led to increased interest in further digitalization of the United States armed forces. Although unmanned systems have been a tool of the military for several decades, only recently have advances in the field of Micro-Electro-Mechanical Systems (MEMS) technology made it possible to develop systems capable of being transported by an individual soldier. These miniature unmanned systems, more commonly referred to as micro air vehicles (MAV), are envisioned by the Department of Defense as being an integral part of maintaining America?s military superiority. As researchers continue to make advances in the miniaturization of flight hardware, a new problem with regard to MAV field operations is beginning to present itself. To date, little work has been done to determine an effective means of collecting, analyzing, and handling information that can satisfy the goal of using MAVs as tools for persistent surveillance. Current systems, which focus on the transmission of analog video streams, have been very successful on larger UAVs such as the RQ-11 Raven but have proven to be very demanding of the operator. By implementing a new and innovative data processing methodology, currently existing hardware can be adapted to effectively present critical information with minimal user input. Research currently being performed at Texas A&M University in the areas of attitude determination and image processing has yielded a new application of photographic projection. By replacing analog video with spatially aware high-resolution images, the present MAV handheld ground control stations (GCS) can be enhanced to reduce the number of functional manpower positions required during operation. Photographs captured by an MAV can be displayed above pre-existing satellite imagery to give an operator a lasting reference to the location of objects in his vicinity. This newly generated model also increases the functionality of micro air vehicles by allowing for target tracking and energy efficient perch and stare capabilities, both essential elements of persistent surveillance

Learning based methods applied to the MAV control problem

This thesis addresses Micro Aerial Vehicle (MAV) control by leveraging learning based techniques to improve robustness of the control system. Applying classical control methods to MAVs is a difficult process due to the complexity of the control laws with fast and highly non-linear dynamics. These methods are mostly based on models that are diffcult to obtain for dynamic and stochastic environments. Due to their size, MAVs are affected by wind gusts and perturbations that push the limits of model based controllers where the linear approximation no longer holds. Instead, we focus on a control strategy that learns to map MAV states (e.g., heading, altitude, velocity) to MAV actions (e.g., actuator positions) to achieve good performance (e.g., flight time, minimal altitude and heading error) by maximizing an objective function. The main difficulty with this approach is defining the objective function and tuning the learning parameters to achieve the desired results. These learning based techniques have been used with great success in many domains with similar dynamics and are shown to improve MAV robustness with respect to wind gusts, perturbations, and actuator failure. Our results show significant improvements in response times to minor altitude and heading corrections over a traditional PID controller. In addition, we show that the MAV response to maintaining altitude in the presence of wind gusts improves by a factor of five. Similarly, we show that the MAV response to maintaining heading in the presence of turbulence improves by factors of three. Finally, we show significant improvements in the case of control surface actuator failure when using a multiagent system. The multiagent control system performs up to 8 times better than the PID controller when tracking a target heading

Design, Development and Flight Testing of a Tube-launched, Rotary-wing, Micro Air Vehicle

This thesis describes the development and flight testing of a compact, re-configurable, rotary-wing micro air vehicle concept capable of sustained hover and could potentially be launched from a 40mm grenade launcher when scaled down. By launching these energy-constrained platforms to a target area, the mission range could be significantly improved. The vehicle design features coaxial rotors with foldable blades, and a thrust-vectoring mechanism for pitch and roll control. Yaw control was accomplished with a specialized counter-rotating motor system composed of two independently controlled motors. Passive unfolding of the coaxial rotor blades in flight utilizing centrifugal force was demonstrated. A cascaded feedback control strategy was implemented on a 1.7 gram custom-designed autopilot. Systematic wind tunnel tests were conducted with the vehicle on a single degree-of-freedom stand, which proved the ability of the controller to reject wind gusts up to 6 m/s and stabilize the vehicle during the powered axial descent phase. Free flight testing verified that the vehicle could hover and fly forward in winds up to 5 m/s. In-flight drop tests were conducted by throttling down the vehicle from a high altitude to attain high decent speeds followed by recovery using the rotor thrust to aggressively brake the descent and achieve a stable hover. Finally, the 366 gram vehicle was launched vertically from a pneumatic cannon followed by a stable projectile phase utilizing the fins, passive rotor unfolding, and final transition to a stable hover from arbitrarily large attitude angles demonstrating the robustness of the controller

Autonomous 3D mapping and surveillance of mines with MAVs

A dissertation Submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, for the degree of Master of Science. 12 July 2017.The mapping of mines, both operational and abandoned, is a long, di cult and occasionally dangerous task especially in the latter case. Recent developments in active and passive consumer grade sensors, as well as quadcopter drones present the opportunity to automate these challenging tasks providing cost and safety bene ts. The goal of this research is to develop an autonomous vision-based mapping system that employs quadrotor drones to explore and map sections of mine tunnels. The system is equipped with inexpensive, structured light, depth cameras in place of traditional laser scanners, making the quadrotor setup more viable to produce in bulk. A modi ed version of Microsoft's Kinect Fusion algorithm is used to construct 3D point clouds in real-time as the agents traverse the scene. Finally, the generated and merged point clouds from the system are compared with those produced by current Lidar scanners.LG201

Experimental Investigation of a MAV-Scale Cyclocopter

The development of an efficient, maneuverable, and gust tolerant hovering concept with a multi-modal locomotion capability is key to the success of micro air vehicles (MAVs) operating in multiple mission scenarios. The current research investigated performance of two unconventional cycloidal-rotor-based (cyclocopter) configurations: (1) twin-cyclocopter and (2) all-terrain cyclocopter. The twin-cyclocopter configuration used two cycloidal rotors (cyclorotors) and a smaller horizontal edge-wise nose rotor to counteract the torque produced by the cyclorotors. The all-terrain cyclocopter relied on four cyclorotors oriented in an H-configuration. Objectives of this research include the following: (1) develop control strategies to enable level forward flight of a cyclocopter purely relying on thrust vectoring, (2) identify flight dynamics model in forward flight, (3) experimentally evaluate gust tolerance strategies, and (4) determine feasibility and performance of multi-modal locomotion of the cyclocopter configuration. The forward flight control strategy for the twin-cyclocopter used a unique combination of independent thrust vectoring and rotational speed control of the cyclorotors. Unlike conventional rotary-winged vehicles, the cyclocopter propelled in forward flight by thrust vectoring instead of pitching the entire fuselage. While the strategy enabled the vehicle to maintain a level attitude in forward flight, it was accompanied by significant yaw-roll controls coupling and gyroscopic coupling. To understand these couplings and characterize the bare airframe dynamics, a 6-DOF flight dynamics model of the cyclocopter was extracted using a time-domain system identification technique. Decoupling methods involved simultaneously mixing roll and yaw inputs in the controller. After implementing the controls mixing strategy in the closed-loop feedback system, the cyclocopter successfully achieved level forward flight up to 5 m/s. Thrust vectoring capability also proved critical for gust mitigation. Thrust vectoring input combined with flow feedback and position feedback improved gust tolerance up to 4 m/s for a twin-cyclocopter mounted on a 6-DOF test stand. Flow feedback relied on a dual-axis flowprobe attached to differential pressure sensors and position feedback was based on data recorded by the VICON motion capture system. The vehicle was also able to recover initial position for crosswind scenarios tested at various side-slip angles up to 30 degrees. Unlike existing multi-modal platforms, the all-terrain cyclocopter solely relied on its four cyclorotors as main source of propulsion, as well as wheels. Aerial and aquatic modes used aerodynamic forces generated by modulating cyclorotor rotational speeds and thrust vectors while terrestrial mode used motor torque. In aerial mode, cyclorotors operated at 1550 rpm and consumed 232 W to sustain hover. In terrestrial mode, forward translation at 2 m/s required 28 W, which was an 88% reduction in power consumption required to hover. In aquatic mode, cyclorotors operated at 348 rpm to achieve 1.3 m/s translation and consumed 19 W, a 92% reduction in power consumption. With only a modest weight addition of 200 grams for wheels and retractable landing gear, the versatile cyclocopter platform achieved sustained hover, efficient translation and rotational maneuvers on ground, and aquatic locomotion

Towards MAV Autonomous Flight: A Modeling and Control Approach

This thesis is about modeling and control of miniature rotary-wing flying vehicles, with a special emphasis on quadrotor and coaxial systems. Mathematical models for simulation and nonlinear control approaches are introduced and subsequently applied to commercial aircrafts: the DraganFlyer and the Hummingbird quadrotors, which have been hardware-modified in order to perform experimental autonomous flying. Furthermore, a first-ever approach for modeling commercial micro coaxial mechanism is presented using a flying-toy called the Micro-mosquito

A Study on the Control, Dynamics, and Hardware of Micro Aerial Biomimetic Flapping Wing Vehicles

Biological flight encapsulates 400 million years of evolutionary ingenuity and thus is the most efficient way to fly. If an engineering pursuit is not adhering to biomimetic inspiration, then it is probably not the most efficient design. An aircraft that is inspired by bird or other biological modes of flight is called an ornithopter and is the original design of the first airplanes. Flapping wings hold much engineering promise with the potential to produce lift and thrust simultaneously. In this research, modeling and simulation of a flapping wing vehicle is generated. The purpose of this research is to develop a control algorithm for a model describing flapping wing robotics. The modeling approach consists of initially considering the simplest possible model and subsequently building models of increasing complexity. This research finds that a proportional derivative feedback and feedforward controller applied to a nonlinear model is the most practical controller for a flapping system. Due to the complex aerodynamics of ornithopter flight, modeling and control are very difficult. Overall, this project aims to analyze and simulate different forms of biological flapping flight and robotic ornithopters, investigate different control methods, and also acquire understanding of the hardware of a flapping wing aerial vehicle