Isaac Kaminer

Trajectory Tracking for Autonomous Vehicles: An Integrated Approach to Guidance and Control

Abstract. This paper addresses the problem of in- tegrated design of guidance and control systems for au- tonomous vehicles (AVs). In fact, it introduces a new methodology for integrated design of guidance and control for such vehicles. The methodology proposed leads to an efficient procedure for the design of controllers for AVs to accurately track reference trajectories defined in an iner- tia! reference frame. The paper illustrates the application of this procedure on the design of a tracking controller for the Unmanned Air Vehicle Bluebird. The design phase is summarized, and the performance of the resulting con- troller is assessed in simulation using dynamic models of the vehicle and its sensor suite.

Brief paper: A velocity algorithm for the implementation of gain-scheduled controllers

A new method is proposed to implement gain-scheduled controllers for nonlinear plants. Given a family of linear feedback controllers designed for linearizations of a nonlinear plant about constant operating points, a nonlinear gain-scheduled controller is derived that preserves the input-output properties of the linear closed loop systems locally, about each equilibrium point. The key procedures in the proposed method are to provide integral action at the inputs to the plant and differentiate some of the measured outputs before they are fed back to the scheduled controller. For a fairly general class of systems, the nonlinear gain-scheduled controllers are easy to obtain, and their structure is similar to that of the original linear controllers.

Vision-based tracking and motion estimation for moving targets using small UAVs

This paper addresses the development of a vision-based target tracking system for a small unmanned air vehicle. The algorithm performs autonomous tracking of a moving target, while simultaneously estimating GPS coordinates of the target. A low cost off the shelf system is utilized, with a modified radio controlled aircraft airframe, gas engine and servos. Tracking is enabled using a low-cost, miniature pan-tilt gimbal. The control algorithm provides rapid and sustained target acquisition and tracking capability. A target position estimator was designed and shown to provide reasonable targeting accuracy. The impact of target loss events on the control and estimation algorithms is analyzed in detail

Path following for unmanned aerial vehicles using L1 adaptive augmentation of commercial autopilots

The paper presents a three-dimensional path-following control algorithm that expands the capabilities of conventional autopilots, which are normally designed to provide only guidance loops for waypoint navigation. Implementation of this algorithm broadens the range of possible applications of small unmanned aerial vehicles. The solution proposed takes explicit advantage of the fact that normally these vehicles are equipped with autopilots stabilizing the vehicles and providing angular-rate tracking capabilities. Therefore, the overall closed-loop system exhibits naturally an inner-outer (dynamics-kinematics) control loop structure. The outer-loop path-following control law developed relies on a nonlinear control strategy derived at the kinematic level, while the inner-loop consisting of the autopilot together with an L1 adaptive augmentation loop is designed to meet strict performance requirements in the presence of unmanned aerial vehicle modeling uncertainty and environmental disturbances. A rigorous proof of stability and performance of the path-following closed-loop system, including the dynamics of the unmanned aerial vehicle with its autopilot, is given. The paper bridges the gap between theory and practice and includes results of extensive flight tests performed in Camp Roberts, California, which demonstrate the benefits of the framework adopted for the control system design.

Coordinated Path Following for Time-Critical Missions of Multiple UAVs via L1 Adaptive Output Feedback Controllers

This paper develops a complete framework for coordinated control of multiple unmanned air vehicles (UAVs) that are tasked to execute collision-free maneuvers under strict spatial and temporal constraints in restricted airspace. The framework proposed includes strategies for deconicted real-time path generation, nonlinear path following, and multiple vehicle coordination. Path following relies on the augmentation of existing autopilots with L1 adaptive output feedback control laws to obtain inner-outer loop control structures with guaranteed performance. Multiple vehicle coordination is achieved by enforcing temporal constraints on the speed proles of the vehicles along their paths in response to information exchanged over a communication network. Again, L1 adaptive control is used to yield an inner-outer loop structure for vehicle coordination. A rigorous proof of stability and performance bounds of the combined path following and coordination strategies is given. Flight test results obtained at Camp Roberts, CA in 2007 demonstrate the benets of using L1 adaptive control for path following of a single vehicle. Hardware-in-the-loop simulations for two vehicles are discussed and provide a proof of concept for time-critical coordination of multiple vehicles over communication networks with xed topologies.

Path Generation, Path Following and Coordinated Control for TimeCritical Missions of Multiple UAVs

The paper proposes a solution to the problem of coordinated control of multiple unmanned air vehicle (UAV) to ensure collision-free maneuvers under strict spatial and temporal constraints. First, a set of feasible trajectories are generated for all UAVs using a new direct method of optimal control that takes into account rules for collision avoidance. A by-product of this step yields, for each vehicle, a spatial path to be followed together with a nominal desired speed profile. Each vehicle is then made to execute a pure path following maneuver in three-dimensional space by resorting to a novel 3D algorithm. Finally, the speed profile for each vehicle is adjusted to enforce the temporal constraints that must be met in order to coordinate the fleet of vehicles. Simulations illustrate the potential of the methodology developed

On Vision-Based Target Tracking and Range Estimation for Small UAVs

*† ‡ § Development of a vision-based target tracking algorithm for an unmanned air vehicle is described. The algorithm provides an autonomous target tracking capability, while simultaneously estimating GPS coordinates of the target. A low cost, primarily COTS system is utilized, with a modified RC aircraft airframe, gas engine and servos. Tracking is enabled using a low-cost, miniature pan-tilt gimbal, driven by COTS servos and electronics. The control algorithm provides rapid and sustained target acquisition and tracking capability. A target position estimator was designed and shown to provide reasonable targeting accuracy. Impact of target loss events on the control and estimation algorithms is analyzed in detail.

Unmanned aircraft navigation for shipboard landing using infrared vision

This paper addresses the problem of determining the relative position and orientation of an unmanned air vehicle with respect to a ship using three visible points of known separation. The, images of the points are obtained from an onboard infrared camera. The paper develops a numerical solution to this problem. Both simulation and flight test results are presented.

Vision-Based Tracking and Motion Estimation for Moving Targets Using Unmanned Air Vehicles

This paper addresses the development of a vision-based target tracking system for a small unmanned air vehicle. The algorithm performs autonomous tracking of a moving target, while simultaneously estimating geographic coordinates, speed, and heading of the target Tight real-time integration of unmanned air vehicles video and telemetry data streams with georeferenced database allows for reliable target identification, increased precision, and shortened time of target motion estimation. A low-cost off-the-shelf system is used, with a modified radiocontrolled aircraft airframe, gas engine, and servos. Tracking is enabled using a low-cost, miniature pan-tilt gimbal. The control algorithm provides rapid target acquisition and tracking capability. A target motion estimator was designed and shown in multiple flight tests to provide reasonable targeting accuracy. The impact of tracking loss events on the control and estimation algorithms is analyzed in detail.

Coordinated path following control of multiple wheeled robots using linearization techniques

The paper addresses the problem of steering a fleet of wheeled robots along a set of given spatial paths, while keeping a desired inter-vehicle formation pattern. This problem arises for example when multiple vehicles are required to scan a given area in cooperation. In a possible mission scenario, one of the vehicles acts a leader and follows a path accurately, while the other vehicles follow paths that are naturally determined by the formation pattern imposed. The paper solves this and other related problems using a simple algorithm that builds on linearization techniques and gain scheduling control theory. Using this set-up, path following (in space) and inter-vehicle coordination (in time) are almost decoupled. Path following for each vehicle amounts to reducing a conveniently defined generalized error vector to zero. Vehicle coordination is achieved by adjusting the speed of each of the vehicles along its path, according to information on the position of all or some of the other vehicles. No other information is exchanged among the robots. The set-up adopted allows for a simple analysis of the resulting coordinated path following control system. The paper describes the structure of the coordination system proposed and addresses challenging problems of robustness with respect to certain types of vehicle failures.