Vector Field Guidance Research Articles

Guiding vector field and self-structuring learning network-based formation control of underactuated surface vessels in static obstacle environments with flexible performances

To address the problem of underactuated surface vessel (USV) formation control in static obstacle environments with model uncertainties and time-varying external disturbances, a model-free formation control strategy is proposed in this paper. First, based on the guiding vector field (GVF), a composite GVF is developed to guide USV formation to the desired position and to avoid multiple static obstacles. Second, a flexible constraint strategy is introduced, and the constraint boundary conditions are appropriately relaxed to avoid singularities in the obstacle environment. Then, based on the Mexican hat wavelet function, the self-structuring fuzzy Mexican hat wavelet cerebellar model articulation controller (SCMAC), and a self-structuring fuzzy Mexican hat wavelet brain emotional learning controller (SBELC), are proposed to achieve model-free control. In addition, the self-structuring algorithm is embedded into SCMAC and SBELC to achieve autonomous optimization of the controller structure and to reduce the computational effort of the control system. The salient features in the proposed control strategy are as follows. First, the proposed model-free formation control strategy does not have to rely on accurate model information. Second, collisions are effectively avoided, and good control performance is guaranteed even under the influence of disturbances and static obstacles. Third, the proposed self-structuring algorithm achieves automatic construction of the controller structure. Finally, the signals in the control system are proven to be bounded, and the simulation results verify the feasibility and superiority of the proposed model-free control strategy.

A Novel Method of 3D Lyapunov Guidance Vector Field to Avoid Intercepting Satellite Based on Reinforcement Learning

This paper proposes a new 3D Lyapunov guidance vector field(3D-LGV) avoidance strategy based on reinforcement learning for the satellite evasion and interception problem. Combining it with the interfered fluid dynamical system (IFDS) enables the satellite to evade and smoothly enter orbit according to the state of the intercepting satellite in real time. 3D-LGV provides an initial flow field approaching an elliptical orbit, while IFDS provides a perturbed flow field based on the intercepting satellite position. The combined potential field of the initial flow field and the disturbed flow field is the planned velocity direction of the satellite. As a decision-making layer, the proximal policy optimization (PPO) dynamically adjusts the perturbed flow field in the IFDS to increase the avoidance success rate in different scenarios. The experimental results show that, compared with the particle swarm optimization with rolling horizon control algorithm, the algorithm proposed in this paper has a shorter decision time and a higher avoidance success rate. At the same time, Monte Carlo simulation shows that the evasion success rate of the proposed algorithm reaches 98%.

Path-following control of an underactuated stratospheric airship with unknown dynamics and external disturbances using time-delay control

In this paper, the path-following problem for an underactuated stratospheric airship with unknown dynamics and external disturbances has been studied. Vector field guidance is employed to fix the underactuated issue and generate desired heading. Time-delay control (TDC) is utilised to track desired states and estimate the model uncertain and disturbances using simple delayed states. To compensate time-delay estimation (TDE) errors, terminal sliding-mode is utilised and combined with TDC. The stability of the proposed controller is provided by using Lyapunov function. A numerical simulation is demonstrated to prove the performance of the proposed control algorithm.

A Hierarchical Heuristic Architecture for Unmanned Aerial Vehicle Coverage Search with Optical Camera in Curve-Shape Area

This paper focuses on the problem of dynamic target search in a curve-shaped area by an unmanned aerial vehicle (UAV) with an optical camera. Our objective is to generate an optimal path for UAVs to obtain the maximum detection reward by a camera in the shortest possible time, while satisfying the constraints of maneuverability and obstacle avoidance. First, based on prior qualitative information, the original target probability map for the curve-shaped area is modeled by Parzen windows with 1-dimensional Gaussian kernels, and then several high-value curve segments are extracted by density-based spatial clustering of applications with noise (DBSCAN). Then, given an example that a target floats down river at a speed conforming to beta distribution, the downstream boundary of each curve segment in the future time is expanded and predicted by the mean speed. The rolling self-organizing map (RSOM) neural network is utilized to determine the coverage sequence of curve segments dynamically. On this basis, the whole path of UAVs is a successive combination of the coverage paths and the transferring paths, which are planned by the Dubins method with modified guidance vector field (MGVF) for obstacle avoidance and communication connectivity. Finally, the good performance of our method is verified on a real river map through simulation. Compared with the full sweeping method, our method can improve the efficiency by approximately 31.5%. The feasibility is also verified through a real experiment, where our method can improve the efficiency by approximately 16.3%.

Online Robot Navigation Using Discrete Waypoints via Time-Varying Guidance Vector Fields

Online Robot Navigation Using Discrete Waypoints via Time-Varying Guidance Vector Fields

Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation

Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation

Cooperative Circumnavigation with Robust Vector Field Guidance for Multiple UAVs in Unknown Wind Environments

Cooperative Circumnavigation with Robust Vector Field Guidance for Multiple UAVs in Unknown Wind Environments

Target encircling control possessing decreased sampling load for quadrotors with experimental verification

This article addresses a target encirclement control with reduced sampling burden for quadrotors using merely bearing measurements without demanding position and velocity of uncooperative target. At the translational level, a target-localization estimator holding the persistent exciting condition is devised assuring an ultimately uniformly bounded estimation via bearing angles only, and a target surrounding guidance law is developed to generate the quadrotor reference velocity, where an orientation vector field capable of steering quadrotors to the circular orbit around the uncooperative target with a pre-specified radius, is constructed based on the relative geometry between estimated target position and quadrotor, eliminating the dependence on relative position and target velocity inherent in existing Lyapunov guidance vector field. At the rotational level, an extended state observer (ESO) is employed to recover system uncertainties, then a robust event-triggered attitude controller is derived following the disturbance estimates and an inverse transformation. The salient merit is that target circumnavigation can be achieved for quadrotors with decreased transmission cost despite of uncertainties, being lack of relative distance measurements and target prior information. Error signals in target encircling system cascaded by guidance and control loops are proved to be bounded using input-to-state stability theorem. Simulations and experiments are provided to validate the property of developed algorithm.

Distributed swarm system with hybrid-flocking control for small fixed-wing UAVs: Algorithms and flight experiments

This paper presents a distributed swarm system for small fixed-wing unmanned aerial vehicles (UAVs). In particular, to perform various missions with multiple UAVs that are densely gathered and collision free, a hybrid-flocking control algorithm is synthesized by using three types of control protocols: vector field guidance (for path following/loitering), augmented Cucker-Smale (ACS) model (for collective flocking behavior), and potential field (for collision avoidance). In particular, to address the issue of conflicts between different control protocols, the adaptive ACS model is proposed and the optimization problem is formulated to determine the suitable mixing weights of control protocols. We also design the transition of multiple operation modes and communication architecture for the swarm system. The system is evaluated using the proposed hybrid-flocking control algorithm by proof-of-concept real flight experiments using 18 small fixed-wing UAVs as well as extensive numerical simulations. Flight experiments are successfully performed for multiple consecutive tasks including the individual task, circular path loitering and elliptical path loitering while avoiding collisions among UAVs.

Data-Driven Hazard Avoidance Landing of Parafoil: A Deep Reinforcement Learning Approach

This paper examines a couple of realizations of autonomous landing hazard avoidance technology of parafoil: a reinforcement-learning-based approach and a rule-based approach, advocating the former. Furthermore, comparative advantages and behavioral analogies between the two approaches are presented. In the data-driven approach, a decision process observing only a series of nadir-pointing images is designed without explicit augmentation of vehicle dynamics for the homogeneity of observation data. An agent then learns the hazard avoidance steering law in an end-to-end fashion. On the contrary, the rule-based approach is facilitated via explicit notions of guidance-control hierarchy, vehicle dynamic states, and metric details of ground obstacles. The soft actor–critic method is applied to learn a policy that maps the down-looking images to parafoil brakes, whereas a vector field guidance law is employed in the rule-based approach, considering each hazard as a repulsive source. This paper then presents empirical equivalences in designing both approaches and their distinctions. Numerical experiments in multiple test cases validate the reinforcement learning method and present comparisons between the approaches regarding their resultant trajectories. The interesting behaviors of the resultant policy of the data-driven approach are emphasized.

Adaptive Neural Control of Flight-Style AUV for Subsea Cable Tracking Under Electromagnetic Localization Guidance

This article focuses on an industrial system for the intelligent inspection of subsea cables based on a flight-style autonomous underwater vehicle (AUV) that tracks the route of subsea cable guided by electromagnetic localization signals. The intelligent inspection system consists of electromagnetic localization and autonomous tracking control modules. The electromagnetic localization algorithm for subsea cable is geometrically derived based on the electromagnetic sequences measured with two triaxial sensors that are symmetrically mounted on the forewings of a flight-style AUV. The argument results on the singularity, sensitivity, and consistency of the localization algorithm provide guidelines for designing the cable-tracking AUV prototypes and planning the near-bottom cable-tracking tasks. Subsequently, the cable-tracking control schemes for decoupled AUV heading and diving subsystems are developed by using the localization results. Particularly, the vector field guidance based on the localization results is designed to overcome the challenges, including underactuation and the lack of cable-tracking error dynamics. To minimize the electromagnetic noise interference and to relax the dependence on the accurate AUV hydrodynamics, the kinematic virtual control signals are filtered with low-pass filters, and neural networks are applied to approximate dynamics. Finally, an integrated simulation study is presented to demonstrate the effectiveness and robustness of the proposed electromagnetic localization-guided autonomous cable-tracking system.

Wind disturbance compensated path-following control for fixed-wing UAVs in arbitrarily strong winds

Wind is the primary challenge for low-speed fixed-wing unmanned aerial vehicles to follow a predefined flight path. To cope with various wind conditions, this paper proposes a wind disturbance compensated path following control strategy where the wind disturbance estimate is incorporated with the nominal guiding vector field to provide the desired airspeed direction for the inner-loop. Since the control input vector for the outer-loop kinematic subsystem needs to satisfy a magnitude constraint, a scaling mechanism is introduced to tune the proportions of the compensation and nominal components. Moreover, an optimization problem is formulated to pursue a maximum wind compensation in strong winds, which can be solved analytically to yield two scaling factors. A cascaded inner-loop tracking controller is also designed to fulfill the outer-loop wind disturbance compensated guiding vector field. High-fidelity simulation results under sensor noises and realistic winds demonstrate that the proposed path following algorithm is less sensitive to sensor noises, achieves promising accuracy in normal winds, and mitigates the deviation from a desired path in wild winds.

Cooperative Standoff Target Tracking using Multiple Fixed-Wing UAVs with Input Constraints in Unknown Wind

This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate moving target tracking in the presence of unknown background wind, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on a Lyapunov guidance vector field is proposed. The convergence analysis shows that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through a rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on the heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller meets the requirements of the airspeed constraint. Furthermore, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit, which is defined by the Lyapunov guidance vector field, to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results indicate that the temporal-phase-based speed controller can achieve a fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits a fast response speed and high estimation accuracy.

Integration of path planning and following control for the stratospheric airship with forecasted wind field data

The absence of real-time airspeed sensors, which was more often ignored in previous studies, and low dynamic characteristics render stratospheric airship control challenging. This study creatively overcomes the aforementioned problems in an integrated path planning and following control scheme using forecasted wind field data. Herein, an efficient and practicable path planning algorithm is designed. Further, a smooth vector field guidance law is proposed for solving the problem of complex path following. Subsequently, an event-triggered neural network-based adaptive tracking controller is designed, considering the wind forecast error influence. Finally, these three parts are organically integrated to achieve autonomous flight. The stability of the closed-loop system and the exclusion of Zeno behavior are rigorously proved. The simulation results reveal that the convergence rate is 63.8% improved, essentially exhibiting better optimization, the tracking errors are eliminated within 80 s, and 99.4% control input updating times are saved.

Standoff Tracking Using DNN-Based MPC With Implementation on FPGA

This work studies the standoff tracking problem to drive an unmanned aerial vehicle (UAV) to slide on a desired circle over a moving target at a constant height. We propose a novel Lyapunov guidance vector (LGV) field with tunable convergence rates for the UAV’s trajectory planning and a deep neural network (DNN)-based model predictive control (MPC) scheme to track the reference trajectory. Then, we show how to collect samples for training the DNN offline and design an integral module (IM) to refine the tracking performance of our DNN-based MPC. Moreover, the hardware-in-the-loop (HIL) simulation with a field-programmable gate array (FPGA) at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$200$</tex-math> </inline-formula> MHz demonstrates that our method is a valid alternative to embedded implementations of MPC for addressing complex systems and applications which is impossible for directly solving the MPC optimization problems.

Highly Robust Adaptive Sliding Mode Trajectory Tracking Control of Autonomous Vehicles

Autonomous driving technology has not yet been widely adopted, in part due to the challenge of achieving high-accuracy trajectory tracking in complex and hazardous driving scenarios. To this end, we proposed an adaptive sliding mode controller optimized by an improved particle swarm optimization (PSO) algorithm. Based on the improved PSO, we also proposed an enhanced grey wolf optimization (GWO) algorithm to optimize the controller. Taking the expected trajectory and vehicle speed as inputs, the proposed control scheme calculates the tracking error based on an expanded vector field guidance law and obtains the control values, including the vehicle's orientation angle and velocity on the basis of sliding mode control (SMC). To improve PSO, we proposed a three-stage update function for the inertial weight and a dynamic update law for the learning rates to avoid the local optimum dilemma. For the improvement in GWO, we were inspired by PSO and added speed and memory mechanisms to the GWO algorithm. Using the improved optimization algorithm, the control performance was successfully optimized. Moreover, Lyapunov's approach is adopted to prove the stability of the proposed control schemes. Finally, the simulation shows that the proposed control scheme is able to provide more precise response, faster convergence, and better robustness in comparison with the other widely used controllers.

Cooperative target search and tracking for multi-UAVs based on control barrier functions

In this paper, a multiple unmanned aerial vehicles (multi-UAVs) cooperative target search and tracking strategy with collision avoidance and flocking constraints are presented applying the control barrier functions (CBFs). First, the constraints of collision avoidance and flocking maintenance are designed based on CBF. Second, the information map and Lyapunov guidance vector field (LGVF) are used to design the corresponding nominal controllers in search mode and tracking mode, respectively. To minimize the difference between the actual controller and the nominal controller, the controller based on quadratic programming (QP) is proposed to achieve the target search and tracking on the premise of security. Finally, the simulation results verify the effectiveness of the proposed method.

Vision-based environment perception and autonomous obstacle avoidance for unmanned underwater vehicle

We propose two schemes to solve the problems of visual perception delay and limited obstacle avoidance capability in the autonomous navigation of unmanned underwater vehicles (UUV). In the aspect of visual perception, this paper presents a compressed algorithm based on the UNDERWATER-CUT model for underwater image enhancement and a two-stage compressed YOLOv5s model for object detection, simultaneously reducing the model volume, decreasing the run-time memory footprint, and lowering the number of computing operations, without compromising enhancement effect and recognition accuracy. The 2D coordinates of the obstacles acquired by the visual perception algorithms and the depth values by the depth camera are then merged into real-time navigation information. In terms of obstacle avoidance, this paper proposes the adapted modified guidance vector field (AMGVF) to achieve autonomous navigation. A high-fidelity underwater simulation platform based on AirSim is constructed to jointly validate the above algorithms. Finally, the experimental results on visual perception and obstacle avoidance show that the above methods apply to the UUV well in complex underwater environment.

Topological Analysis of Vector-Field Guided Path Following on Manifolds

A path-following control algorithm enables a system’s trajectories under its guidance to converge to and evolve along a given geometric desired path. There exist various such algorithms, but many of them can only guarantee local convergence to the desired path in its neighborhood. In contrast, the control algorithms using a well-designed guiding vector field can ensure almost global convergence of trajectories to the desired path; here, “almost” means that in some cases, a measure-zero set of trajectories converge to the singular set where the vector field becomes zero (with all other trajectories converging to the desired path). In this article, we first generalize the guiding vector field from the Euclidean space to a general smooth Riemannian manifold. This generalization can deal with path-following in some abstract configuration space (such as robot arm joint space). Then, we show several theoretical results from a topological viewpoint. Specifically, we are motivated by the observation that singular points of the guiding vector field exist in many examples where the desired path is homeomorphic to the unit circle, but it is unknown whether the existence of singular points always holds in general (i.e., is inherent in the topology of the desired path). In the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional Euclidean space, we provide an affirmative answer, and conclude that it is not possible to guarantee global convergence to desired paths that are homeomorphic to the unit circle. Furthermore, we show that there always exist nonpath-converging trajectories (i.e., trajectories that do not converge to the desired path) starting from the boundary of a ball containing the desired path in an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional Euclidean space where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n \geq 3$</tex-math></inline-formula> . Examples are provided to illustrate the theoretical results.

GVF-based guidance and super-twisting control of autonomous surface vehicle for target tracking in obstacle environments with experiments

This paper addresses the target tracking problem of an under-actuated autonomous surface vehicle (ASV) subject to internal uncertainties and exogenous forces in ocean environments with static and dynamic obstacles. A collision-free target tracking (CFTT) control method is proposed based on a guiding vector field (GVF) guidance and a super-twisting algorithm (STA) control. Specifically, a resultant GVF is developed to guide the ASV towards the target position and to avoid multiple obstacles. Based on the resultant GVF, a saturated guidance law is designed to calculate the surge velocity and angular velocity commands. Then, robust exact differentiator (RED)-based observers are developed to estimate the lumped disturbances within finite time. By using the estimated lumped disturbances, STA-based anti-disturbance kinetic control laws are designed to track the given velocity commands within finite time. The finite-time input-to-state stability of the closed-loop system is proven based on cascade theory. Emulational and experimental validations are conducted to substantiate the efficacy of the proposed CFTT control method.