Richard Garcia

Multi-UAV Simulator Utilizing X-Plane

This paper describes the development of a simulator for multiple Unmanned Aerial Vehicles (UAVs) utilizing the commercially available simulator X-Plane and Matlab. Coordinated control of unmanned systems is currently being researched for a wide range of applications, including search and rescue, convoy protection, and building clearing to name a few. Although coordination and control of Unmanned Ground Vehicles (UGVs) has been a heavily researched area, the extension towards controlling multiple UAVs has seen minimal attention. This lack of development is due to numerous issues including the difficulty in realistically modeling and simulating multiple UAVs. This work attempts to overcome these limitations by creating an environment that can simultaneously simulate multiple air vehicles as well as provide state data and control input for the individual vehicles using a heavily developed and commercially available flight simulator (X-Plane). This framework will allow researchers to study multi-UAV control algorithms using realistic unmanned and manned aircraft models in real-world modeled environments. Validation of the system’s ability is shown through the demonstration of formation control algorithms implemented on four UAV helicopters with formation and navigation controllers built in Matlab/Simulink.

The Implementation of an Autonomous Helicopter Testbed

Miniature Unmanned Aerial Vehicles (UAVs) are currently being researched for a wide range of tasks, including search and rescue, surveillance, reconnaissance, traffic monitoring, fire detection, pipe and electrical line inspection, and border patrol to name only a few of the application domains. Although small/miniature UAVs, including both Vertical Takeoff and Landing (VTOL) vehicles and small helicopters, have shown great potential in both civilian and military domains, including research and development, integration, prototyping, and field testing, these unmanned systems/vehicles are limited to only a handful of laboratories. This lack of development is due to both the extensive time and cost required to design, integrate and test a fully operational prototype as well as the shortcomings of published materials to fully describe how to design and build a “complete” and “operational” prototype system. This work attempts to overcome existing barriers and limitations by detailing the technical aspects of a small UAV helicopter designed specifically as a testbed vehicle. This design aims to provide a general framework that will not only allow researchers the ability to supplement the system with new technologies but will also allow researchers to add innovation to the vehicle itself.

Unmanned Vehicle Controller Design, Evaluation and Implementation: From MATLAB to Printed Circuit Board

A detailed step-by-step approach is presented to optimize, standardize, and automate the process of unmanned vehicle controller design, evaluation, validation and verification, followed by actual hardware controller implementation on the vehicle. The proposed approach follows the standard practice to utilize MATLAB/SIMULINK and related toolboxes as the design framework. Controller design in MATLAB/SIMULINK is followed by automatic conversion from MATLAB to code generation and optimization for particular types of processors using Real-Time Workshop, and C to Assembly language conversion to produce assembly code for a target microcontroller. Considering Unmanned Aerial Vehicles, fixed or rotary wing ones, X-Plane is used to verify, validate and optimize controllers before actual testing on an unmanned vehicle and actual implementation on a chip and printed circuit board. Sample designs demonstrate the applicability of the proposed method.

Utilizing compliance to manipulate doors with unmodeled constraints

Increasingly, robots are being applied to challenges in human environments such as soldier and disability assistance, household chores, and bomb disposal. To maximize a robots capabilities within these dynamic and uncertain environments, robots must be able to manipulate objects with unknown constraints, including opening and closing doors, cabinets, and drawers. Practicality suggests that these tasks be done at or near human speed. A simple and low cost method is proposed to achieve these ends - utilizing joint compliance to resolve forces non-tangent to the path of travel. In this paper, joint compliance is achieved by means of a clutch mechanism located in line with the manipulator joint motors. When an object is to be moved, the motors are disengaged from the joints using the clutch, thus allowing the joints to move freely with the object while force is applied by the mobility platform. This enables the robot to move an object within its constraints without the need for a precise forcing vector, minimizing sensing needs as well as computation time. Other implementations of the technique are also possible, including use of inverse dynamics, back-drivable motors, and/or actively controlled slip clutches for gravity and friction compensation. The effectiveness and robustness of this approach are demonstrated through kinematic analysis, dynamic simulation, and physical experimentation on three differently sized doors and a drawer.

Swarm formation control utilizing ground and aerial unmanned systems

This work addresses the problem of coordinating a swarm of unmanned ground vehicles with an unmanned aerial vehicle (UAV). The UAV is utilized as a leader robot that is intelligently followed by a coordinated group of unmanned ground vehicles (UGVs). The UAV is a completely autonomous agent that is controlled using Sugueno fuzzy logic. The UGVs are organized into formation utilizing artificial potential fields generated from normal and sigmoid functions. These functions are built around the location of the UAV and ultimately construct the surface that swarm members travel on, which inherently controls the overall swarm geometry and the individual member spacing. Nonlinear limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables forcing the swarm to behave according to set constraints, formation and member spacing. The swarm function and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation as well as user defined constraints. Simulations demonstrate the precision of the approach with up to forty UGVs. Experimental results are presented using a fully autonomous swarm of three UGVs and a single UAV helicopter for coordination.

Design, Implementation and Testing of a Vision System for Small Unmanned Vertical Take off and Landing Vehicles with Strict Payload Limitations

Alternative designs of a simple, low cost and effective vision system for small, portable, unmanned aerial vertical take off and landing (VTOL) vehicles are presented. Design configurations follow the ‘on-board’ and ‘on-the-ground’ processing concept and they depend on very strict payload limitations and power supply restrictions. Hardware and software components for both designs are described; advantages and disadvantages of both alternatives are compared; computational complexity is calculated and trade offs are discussed. Implementations on a series of small unmanned VTOL vehicles as well as testing details are included and experimental results are presented.

Concepts and Validation of a Small-Scale Rotorcraft Proportional Integral Derivative (PID) Controller in a Unique Simulation Environment

At the current time, the U.S. Army Research Laboratory’s (ARL’s) Vehicle Technology Directorate is interested in expanding its Unmanned Vehicles Division to include rotary wing and micro-systems control. The intent is to research unmanned aircraft systems not only for reconnaissance missions but also for targeting and lethal attacks. This project documents ongoing work expanding ARL’s program in research and simulation of autonomous control systems. A proportional integral derivative control algorithm was modeled in Simulink (Simulink is a trademark of The MathWorks) and communicates to a flight simulator modeling a physical radio-controlled helicopter. Waypoint navigation and flight envelope testing were then systematically evaluated to the final goal of a feasible autopilot design. Conclusions are made on how to perhaps make this environment more dynamic in the future.

A multiplatform on-board processing system for miniature unmanned vehicles

This paper presents a low cost, high powered onboard processing system for miniature unmanned vehicles (UVs), especially unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). Such vehicles have shown great potential due to their compact size, high maneuverability and high payload-to-size ratio. The proposed on-board processing system differs from other existing designs by utilizing developed commercial off the shelf (COTS) hardware and a full, nongraphical, operating system (OS). The hardware allows for quick development and deployment of the on-board processing system as well as easy integration with new hardware. The full OS allows for simplified software development and testing as well as the use of commercial and open source software. The physical configuration of the system, necessary drivers, and field tests on both aerial and ground vehicles are presented

Unmanned aircraft systems as wingmen

This paper introduces a method to integrate Unmanned Aircraft Systems (UASs) into a highly functional manned/unmanned team through the design and implementation of 3D distributed formation/flight control algorithms with the goal to act as wingmen for a manned aircraft. The proposed algorithms are designed to increase UAS autonomy, dynamically modify formations, utilize standard operating formations to reduce pilot resistance to integration, and support splinter groups for surveillance and/or as safeguards between potential threats and manned vehicles. The proposed work coordinates UAS members by utilizing artificial potential functions whose values are based on the state of the unmanned and manned assets including the desired formation, obstacles, task assignments, and perceived intentions. The overall unmanned team geometry is controlled using weighted potential fields. Individual UASs utilize fuzzy logic controllers for stability and navigation as well as a fuzzy reasoning engine for predicting the intent of surrounding aircrafts. Approaches are demonstrated in simulation using the commercial simulator X-Plane and controllers designed in Matlab/Simulink. Experiments include staggered trail and right echelon formations as well as splinter group surveillance.

Control and Limitations of Navigating a Tail Rotor/Actuator Failed Unmanned Helicopter

According to Federal Aviation Administration (FAA) statistics on mechanical failures, tail rotor failure is the third highest cause of fatal accidents in helicopters. Tail rotor failure represents a serious hazard to personnel and mission objectives and can create high fiscal loss. This is especially true for unmanned helicopters, which cannot be equipped with the fail-safes standard on manned counterparts. This work provides an overview of how a helicopter can be controlled after a tail rotor failure and its applicability to both manned and unmanned vehicles. This work specifically details some of the limitations of this type of software failure control.