Lyapunov Guidance Vector Field Research Articles

Fast Lyapunov Vector Field Guidance for Standoff Target Tracking Based on Offline Search

To address the problem of the long convergence time of traditional Lyapunov Vector Field Guidance (LVFG), a fast convergent LVFG designed with guidance function is proposed. The necessary criteria of a fast convergent vector field are first illustrated through an analysis of the contraction and circulation components. Then, two types of guidance functions of the distance between aircraft and target are constructed to replace the original guidance parameter. Considering the saturation constraints from the aircraft performance, the optimal parameter in guidance functions based on offline search is estimated to achieve the best convergence. Meanwhile, standoff tracking for a moving target is solved with analysis of motion correction. Simulation results indicate the proposed vector field guidance converges faster to the standoff circle without traversing the orbit regardless of whether the aircraft’s initial position is inside or outside the circle, even for a moving target. In addition, selection of parameter in the guidance function is proved to make convergence speed approach the limit.

Persistent standoff tracking guidance using constrained particle filter for multiple UAVs

This paper presents a new standoff tracking framework of a moving ground target using UAVs with limited sensing capabilities and motion constraints. To maintain persistent track of the target even in case of target loss for a certain period, this study predicts the target existence area using the particle filter and produces control commands that ensure that all predicted particles can stay within the field-of-view of the UAV sensor at all times. To improve target position prediction and estimation accuracy, the road information is incorporated into the constrained particle filter where the road boundaries are modelled as inequality constraints. Both Lyapunov vector field guidance and nonlinear model predictive control-based methods are applied, and the characteristics of them are compared using numerical simulations.

Coordinated Standoff Target Tracking Guidance Method for UAVs

We need a coordinated control method to continuously track a moving target using a group of UAVs. In this paper, we study the predicted reference point guidance method, where the target is considered to move with a constant velocity in a very short time window and the trajectories of the UAVs are designed as several tiny arcs around the target. The control law of the UAV is divided into roll angle control and velocity control. Simulations are used to verify that the proposed control laws have smaller standoff distances and phase angle control errors than the Lyapunov vector field guidance and model-based predictive control, and the wind is considered in the simulation, too. Therefore, we show that our proposed method has higher steady state accuracy among existing techniques.

Cooperative path planning with applications to target tracking and obstacle avoidance for multi-UAVs

In this paper, we propose a hybrid approach based on the Lyapunov Guidance Vector Field (LGVF) and the Improved Interfered Fluid Dynamical System (IIFDS), to solve the problems of target tracking and obstacle avoidance in three-dimensional cooperative path planning for multiple unmanned aerial vehicles (UAVs). First, LGVF method is improved for UAV cooperative target tracking in 3D environment by introducing vertical component, with two guidance layers containing steering control and speed control. Second, IIFDS method is presented for UAVs to avoid obstacles or threats in complicated environment, where the local minimum problem is well resolved. Moreover, some cooperative strategies are added into the IIFDS framework to satisfy the constraints of obstacle avoidance and cluster maintenance. Finally, the missions of tracking target and avoiding obstacles can be performed simultaneously, by replacing the original sink fluid of IIFDS with the vector field of LGVF. Besides, the reactive parameters of IIFDS can be adjusted by the rolling optimization strategy to enhance the path quality. The experimental results validate the effectiveness of the hybrid method.

Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment

According to the tactical requirements of unmanned aerial vehicle (UAV) for tracking target and avoiding obstacle in complex dynamic environment, a three-dimensional (3D) real-time path planning method is proposed by combing the improved Lyapunov Guidance Vector Field (LGVF), the Interfered Fluid Dynamical System (IFDS) and the strategy of varying receding-horizon optimization from Model Predictive Control (MPC). First, in order to track the moving target in 3D environment, the LGVF method is improved by introducing flight height into the traditional Lyapunov function, and the generated velocity can guide UAV converge gradually to the limit cycle in horizontal plane and the optimal height in vertical plane. Then, the IFDS method imitating the phenomenon of fluid flow is utilized to plan the collision-free path. To achieve the mission of tracking moving target and avoid static or dynamic obstacle at the same time, the guidance vector field by LGVF is taken as the original fluid of IFDS. As the fluid system still remains stable under the influence of obstacles, the disturbed streamline from the interfered fluid can be regarded as the planned path. Third, as the quality of route is mainly influenced by the repulsive and tangential parameters of IFDS, the real-time suboptimal route can be planned by the varying receding-horizon optimization according to the predicted motion. The experimental results prove that the proposed hybrid method is applicable to various dynamic environments.

Evaluation of control strategies for fixed-wing drones following slow-moving ground agents

There are many situations where fixed-wing drones may be required to track ground moving agents, such as humans or cars, which are typically slower than drones. Some control strategies have been proposed and validated in simulations using the average distance between the target and the drone as a performance metric. However, besides the distance metric, energy expenditure of the flight also plays an important role in assessing the overall performance of the flight. In this paper, we propose a new methodology that introduces a new metric (energy expenditure), we compare existing methods on a large set of target motion patterns and present a comparison between the simulation and field experiments on proposed target motion patterns. Using this new methodology we examine the performance of three control strategies: the Lyapunov Guidance Vector Field strategy, the Bearing-only strategy and the Oscillatory strategy. Among the three strategies considered, we demonstrate that the Lyapunov Guidance Vector Field strategy has the best performance for all target motion patterns. Field experiments with fixed-wing drones provide additional insights into the benefits and shortcomings of each strategy in practice.

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

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.

Nonlinear Model Predictive Coordinated Standoff Tracking of a Moving Ground Vehicle

This paper proposes a nonlinear model-predictive control framework for coordinated standoff tracking by a pair of unmanned aerial vehicles. The benefit of this approach is to get optimal performance compared with using a decoupled controller structure: heading control for standoff-distance keeping and speed control for phase keeping. The overall controller structure is fully decentralized as each unmanned aerial vehicle optimizes its controller based solely on the future propagation of the pair vehicle states and the target estimates received via communication. This paper uses an acceleration model for sophisticated and realistic target dynamics, which can consider a more reasonable system noise covariance matrix reflecting the target’s motion characteristics. To simplify optimization formulation and decrease computation burden, a new manipulation using the inner product of position vectors of the unmanned aerial vehicles with respect to the target position is proposed for antipodal tracking instead of using the relative phase-angle difference. To consider a more realistic situation, inequality constraints are considered for collision avoidance between unmanned aerial vehicles and control input saturations using penalty functions in the model-predictive control scheme. Simulations with a pair of unmanned aerial vehicles are done using realistic car trajectory data in an urban environment in the United Kingdom to verify the feasibility and benefit of the proposed approach with comparing to a Lyapunov vector field guidance.

Coordinated Standoff Tracking of Moving Targets: Control Laws and Information Architectures

In this paper, we present work on control of autonomous vehicle formations in the context of the coordinated stando tracking problem . The objective is to use a team of unmanned aircraft to fly a circular orbit around a moving target with prescribed inter-vehicle angular spacing using only local information. We use the recently introduced Lyapunov guidance vector field approach to achieve the desired circular trajectory. The contributions of this paper involve both single vehicle path planning and multiple vehicle coordination. For single vehicle path planning, we complete a proof of heading convergence using feedback, which has thus far not been fully addressed in the literature, and also oer a novel approach for heading convergence that does not require continuous feedback in the ideal case (no wind, stationary target), taking advantage of an analytical solution to the guidance field. Further, we use a variable airspeed controller to maintain the circular trajectory despite unknown wind and unknown constant velocity target motion. Adaptive estimates of the unknown wind and target motion are introduced to ensure stability to the circular trajectory. A novel feature of our results is rigorous satisfaction of vehicle specific kinematic constraints on heading rates and airspeed variations. For multiple vehicle coordination, we again use a variable airspeed controller to achieve the prescribed angular spacing. In an eort towards a unified framework for control of autonomous vehicle formations, we make a connection with some recent work that addresses information architecture in vehicle formations using graph theory. Specifically, we utilize two types of information architectures, symmetric and asymmetric, and implement decentralized control laws. The information architectures are scalable in the sense that the number of required communication/sensing links increases linearly with the number of vehicles. The control laws are decentralized in the sense that they use only local information.

Coordinated Standoff Tracking of Moving Targets Using Lyapunov Guidance Vector Fields

This paper presents a control structure for cooperative stand-off line-of-sight tracking of a moving target by a team of unmanned aircraft based on a Lyapunov guidance vector field that produces stable convergence to a circling limit cycle behavior. A guidance vector field is designed for a stationary target and then modified with a correction term that accounts for a moving target and constant background wind. Cooperative tracking by multiple unmanned aircraft is achieved through additional phasing, also with a Lyapunov stability analysis. Convoy protection, in which the unmanned aircraft must scout an area ahead of the moving target, is performed by extending the cooperative stand-offline-of-sight limit cycle attractor along the direction of travel. Simulation results demonstrate the behavior of the algorithms as well as the improvement that results from cooperation. Finally, simulations of a larger cooperative search, acquisition, and tracking scenario are described that illustrate the use of the cooperative standoff line-of-sight and convoy protection controllers in a realistic application.