In this paper, we propose hierarchical control architecture for a system that does border or perimeter patrol using unmanned air vehicles (AUV). By control architecture we mean a specific way of organizing the motion control and navigation functions performed by the UAV. It is convenient to organize the functions into hierarchical layers. This way, a complex design problem is partitioned into a number of more manageable subproblems that are addressed in separate layers. This paper discusses vehicle control requirements and maps them onto layered control architecture. The formalization of the hierarchy is accomplished in terms of the specific functions accomplished by each layer and of the interfaces between layers. The implementation of the layers is discussed and illustrative examples are provided.

Border patrol and surveillance missions using multiple unmanned air vehicles

Inexpensive fixed wing UAV are increasingly useful in remote sensing operations. They are a cheaper alternative to manned vehicles, and are ideally suited for dangerous or monotonous missions that would be inadvisable for a human pilot. Groups of UAV are of special interest for their abilities to coordinate simultaneous coverage of large areas, or cooperate to achieve goals such as mapping. Cooperation and coordination in UAV groups also allows increasingly large numbers of aircraft to be operated by a single user. Specific applications under consideration for groups of cooperating UAV are border patrol, search and rescue, surveillance, communications relaying, and mapping of hostile territory. The capabilities of small UAV continue to grow with advances in wireless communications and computing power. Accordingly, research topics in cooperative UAV control include efficient computer vision for real-time navigation and networked computing and communication strategies for distributed control, as well as traditional aircraft-related topics such as collision avoidance and formation flight. Emerging results in cooperative UAV control are presented via discussion of these topics, including particular requirements, challenges, and some promising strategies relating to each area. Case studies from a variety of programs highlight specific solutions and recent results, ranging from pure simulation to control of multiple UAV. This wide range of case studies serves as an overview of current problems of Interest, and does not present every relevant result.

An overview of emerging results in cooperative UAV control

This work provides flight-path geometry, guidance laws, and synchronous camera angles to observe a ground target from an unmanned aerial vehicle. The observation of the target is affected by wind, aircraft performance, and camera limits. Analytic expressions are derived for paths that result in constant line-of-sight orientation of the target relative to the aircraft body frame. Using minimal heuristics, a guidance law based on “good helmsman” behavior is developed and implemented, and stability of its integration with aircraft dynamics is assessed. An observer estimates wind data, which are used to orient path geometry about the target. Results are demonstrated in high-fidelity simulation. Nomenclature a = helmsman sensitivity parameter c(·) = cos(·) d = distance Fb = body-fixed frame Fs = Serret‐Frenet frame g = gravity constant r = radius s = arclength position along desired path s(·) = sin(·) t(·) = tan(·)

Unmanned Aerial Vehicle Path Following for Target Observation in Wind

A dynamic path-planning algorithm is proposed for routing unmanned air vehicles (UAVs) in order to track ground targets under path constraints, wind effects, and obstacle avoidance requirements. We first present the tangent vector field guidance (TVFG) and the Lyapunov vector field guidance (LVFG) algorithms. We demonstrate that the TVFG outperforms the LVFG as long as a tangent line is available between the UAV's turning circle and an objective circle, which is a desired orbit pattern over a target. Based on a hybrid version of the TVFG and LVFG, we then derive a theoretically shortest path algorithm with UAV operational constraints given a target position and the current UAV dynamic state. This algorithm has the efficiency of the TVFG when UAV is outside the standoff circle and the ability to follow the path via the LVFG when inside the standoff circle. In addition we adopt point-mass approximation of the target state probability density function (pdf) for target motion prediction by exploiting road network information and target dynamics as well as obstacle avoidance strategies. Overall, the proposed technical approach is practical and competitive, supported by solid theoretical analysis on several aspects of the algorithm performance. With extensive simulations we show that the tangent-plus-Lyapunov vector field guidance (T+LVFG) algorithm provides effective and robust tracking performance in various scenarios, including a target moving according to waypoints or a random kinematics model in an environment that may include obstacles and/or winds.

UAV Path Planning with Tangent-plus-Lyapunov Vector Field Guidance and Obstacle Avoidance

A method for guidance and control of an autonomous vehicle in problems of border patrolling and obstacle avoidance

In this paper, we present a strategy of path-planning for an unmanned aerial vehicle (UAV) to follow a ground vehicle. The ground vehicle may change its heading and vary its speed from a standstill up to the velocity of the UAV, while the UAV will maintain a fixed airspeed and will maneuver itself to track the ground vehicle. The algorithm also allows the UAV to track the ground vehicle with an offset vector (i.e. the user may wish the UAV to stay ahead of the ground vehicle or to its sides). Since the ground vehicle may operate in a range of velocities, the algorithm must plan the UAV's path with the appropriate schemes for the various ground vehicle speeds. The natural effect of wind injects a disturbance into the system, and so wind compensation techniques had to be developed. In order to maintain the focus of this project on path-planning strategies, the path-planning algorithm was implemented on top of a system that already controls the dynamics of the UAV. Simulation of aircraft and ground vehicles was performed with a hardware-in-the-loop simulation environment to test for mission feasibility. After attaining satisfactory simulation results, an experiment was conducted to confirm the path-planning strategy.

Strategies of Path-Planning for a UAV to Track a Ground Vehicle

This paper has determined the main failure modes of the electric drive through investigation and analysis of the structure and working conditions of the drive, and found out the corresponding components. Based on Physics of Failure (PoF), the function corresponding to the main failure modes of the electric drive is established, and based on this, a reliability analysis was carried out, which lays a certain foundation for improving the reliability of the industrial robot drive. 

Rosemary Huang

Papers

Strategies of Path-Planning for a UAV to Track a Ground Vehicle

Reliability analysis of industrial robot electric drive based on physics of failure