Formations Of Autonomous Vehicles Research Articles

Enhancing Connected Autonomous Vehicle Formations: Discrete–Offline–Online Three-Layer Architecture for Platoon Reconfiguration

Enhancing Connected Autonomous Vehicle Formations: Discrete–Offline–Online Three-Layer Architecture for Platoon Reconfiguration

Cooperative positioning method of underwater autonomous vehicle formation based on extended Kalman filter

Autonomous Underwater Vehicles (UAVs) have shown significant potential application value in marine environmental surveillance, development, and utilization of resources. However, due to the complex underwater acoustic channel environment and the relative motion between AUVs, this may lead to heavy-tailed non-Gaussian process noise and measurement noise, leading to increased error and the Extended Kalman Filter (EKF) may fail. In prior studies, we propose a multi-AUV formation hierarchical target location algorithm based on the EKF, which can realize multi-AUV self-localization and stationary target localization. The AUV formation consists of one high-precision piloting AUV and several low-precision following AUVs. The following AUVs are divided into two levels, the reference AUVs and the AUVs to be tested. According to the designed positioning period, the reference AUV receives the position parameters from a high-precision piloting AUV and transmits its own position parameters to the AUV to be measured. Then use the EKF to complete the cooperative position correction of the AUV cluster. In this paper, we will study the influence of heavy-tailed noise on this method and study the EKF based on Huber estimation to improve the antijamming capability.

Cooperative Formation of Autonomous Vehicles in Mixed Traffic Flow: Beyond Platooning

Cooperative formation and control of autonomous vehicles (AVs) promise increased efficiency and safety on public roads. In mixed traffic flow consisting of AVs and human-driven vehicles (HDVs), the prevailing platooning of multiple AVs is not the only choice for cooperative formation. In this paper, we investigate how different formations of AVs impact traffic performance from a set-function optimization perspective. We first reveal a stability invariance property and a diminishing improvement property of noncooperative formation when AVs adopt typical Adaptive Cruise Control (ACC) strategies. Then, we focus on the case of cooperative formation where the AV controllers are cooperatively designed %redesign the control strategies of AVs in different formations and investigate the optimal formation of multiple AVs using set-function optimization. Two predominant optimal formations, i.e., uniform distribution and platoon formation, emerge from extensive numerical experiments. Interestingly, platooning might have the least potential to improve traffic performance when HDVs have poor string stability behavior. These results suggest more opportunities for cooperative formation of AVs, beyond platooning, in practical mixed traffic flow.

A Swarm-Based Distributed Model Predictive Control Scheme for Autonomous Vehicle Formations in Uncertain Environments.

In this article, a novel distributed model predictive control architecture is proposed for the coordination and control of multivehicle formations moving within uncertain environments. As one of its main merits, multiagent swarm modeling and leader-follower configurations are jointly exploited within an ad hoc model predictive control framework to reduce as much as possible the use of onboard sensors that is essential in long-range missions. Moreover, the feasibility and asymptotic closed-loop stability of the resulting scheme are formally proved. Finally, a laboratory experiment is used to show the effectiveness of the proposed algorithm with particular attention to the follower capability of operating in a so-called blind fashion.

Optimal Formation of Autonomous Vehicles in Mixed Traffic Flow

Platooning of multiple autonomous vehicles has attracted significant attention in both academia and industry. Despite its great potential, platooning is not the only choice for the formation of autonomous vehicles in mixed traffic flow, where autonomous vehicles and human-driven vehicles (HDVs) coexist. In this paper, we investigate the optimal formation of autonomous vehicles that can achieve an optimal system-wide performance in mixed traffic flow. Specifically, we consider the optimal $\mathcal{H}_2$ performance of the entire traffic flow, reflecting the potential of autonomous vehicles in mitigating traffic perturbations. Then, we formulate the optimal formation problem as a set function optimization problem. Numerical results reveal two predominant optimal formations: uniform distribution and platoon formation, depending on traffic parameters. In addition, we show that 1) the prevailing platoon formation is not always the optimal choice; 2) platoon formation might be the worst choice when HDVs have a poor string stability behavior. These results suggest more opportunities for the formation of autonomous vehicles, beyond platooning, in mixed traffic flow.

Discrete-time distributed Kalman filter design for formations of autonomous vehicles

This paper addresses the problem of distributed state estimation in a multi-vehicle framework. Each vehicle aims to estimate its own state relying on locally available measurements and limited communication with other vehicles in the vicinity. The dynamics of the problem are formulated as a discrete-time Kalman filtering problem with a sparsity constraint on the gain, and two different algorithms for computation of steady-state observer gains for arbitrary fixed measurement topologies are introduced. Their application to the practical problem of distributed localization in a formation of Autonomous Underwater Vehicles (AUVs) is detailed, supported by simulation results.

Decentralized state observers for range‐based position and velocity estimation in acyclic formations with fixed topologies

SummaryThis paper addresses the problem of decentralized position and velocity estimation in formations of autonomous vehicles. A limited number of vehicles in the formation have access to absolute position measurements, while the rest must rely on range measurements to neighboring agents, local sensor data, and limited communication capabilities to estimate their own position and velocity. The contribution is threefold: (i) a method for designing local state observers for each agent in the formation that rely only on locally available information is presented; (ii) the stability of the continuous‐time linear time‐varying Kalman filter subject to exponentially decaying perturbations in some variables is studied; and (iii) the stability of the error dynamics of the resulting decentralized state observer is analyzed for acyclic formations with fixed topologies, and it is shown that the error converges exponentially fast to the origin for all initial conditions. Simulation results are presented and discussed to validate the proposed solution, as well as assessing its performance under the influence of measurement noise. Copyright © 2015 John Wiley & Sons, Ltd.

Stability Analysis and Variational Integrator for Real-Time Formation Based on Potential Field

This paper investigates a framework of real-time formation of autonomous vehicles by using potential field and variational integrator. Real-time formation requires vehicles to have coordinated motion and efficient computation. Interactions described by potential field can meet the former requirement which results in a nonlinear system. Stability analysis of such nonlinear system is difficult. Our methodology of stability analysis is discussed in error dynamic system. Transformation of coordinates from inertial frame to body frame can help the stability analysis focus on the structure instead of particular coordinates. Then, the Jacobian of reduced system can be calculated. It can be proved that the formation is stable at the equilibrium point of error dynamic system with the effect of damping force. For consideration of calculation, variational integrator is introduced. It is equivalent to solving algebraic equations. Forced Euler-Lagrange equation in discrete expression is used to construct a forced variational integrator for vehicles in potential field and obstacle environment. By applying forced variational integrator on computation of vehicles' motion, real-time formation of vehicles in obstacle environment can be implemented. Algorithm based on forced variational integrator is designed for a leader-follower formation.

Distributed Cohesive Motion Control of Flight Vehicle Formations

In this paper, we consider the problem of decentralized cohesive motion control of a formation of autonomous vehicles or robots moving in three dimensions, where the formation is required to move from its initial setting (defined by the positions of the agents in the formation) to a final desired setting and, during this motion, maintain its formation geometry defined by the initial distances between the agent pairs. We propose a distributed control scheme to solve this problem utilizing the notions of graph rigidity and persistence as well as techniques of virtual target tracking and smooth switching. The distributed control scheme is developed by modeling the agent kinematics as single-velocity integrator; nevertheless, extension to the cases with practical kinematic and dynamic models of fixed-wing autonomous aerial vehicles and quadrotors is discussed. In this context, we examine the maintenance of geometric formation of a swarm of autonomous flight vehicles. The developed coordination and control schemes are verified via a number of simulations.

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.

Rigid graph control architectures for autonomous formations

This article sets out the rudiments of a theory for analyzing and creating architectures appropriate to the control of formations of autonomous vehicles. The theory rests on ideas of rigid graph theory, some but not all of which are old. The theory, however, has some gaps in it, and their elimination would help in applications. Some of the gaps in the relevant graph theory are as follows. First, there is as yet no analogue for three-dimensional graphs of Laman's theorem, which provides a combinatorial criterion for rigidity in two-dimensional graphs. Second, for three-dimensional graphs there is no analogue of the two-dimensional Henneberg construction for growing or deconstructing minimally rigid graphs although there are conjectures. Third, global rigidity can easily be characterized for two-dimensional graphs, but not for three-dimensional graphs.

Stability of autonomous vehicle formations using an ISS small‐gain theorem for networks

AbstractWe consider a formation of vehicles moving on the two dimensional plane. The movement of each vehicle is described by a system of ordinary differential equations with inputs. The formation is maintained using autonomous controls that are designed to maintain fixed relative distances and orientations between vehicles. Moreover this formation should track a given trajectory on the plane. The vehicles can measure the relative distances and angles to their neighbors. These values are the inputs from one system to another. With the help of a general ISS small–gain theorem for networks we will show that the dynamics of such a formation is stable for the given controls. The notion of local input–to– state stability (local ISS) will be used for this purpose. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Decentralized Receding Horizon Control and Coordination of Autonomous Vehicle Formations

This paper describes the application of a novel methodology for high-level control and coordination of autonomous vehicle teams and its demonstration on high-fidelity models of the organic air vehicle developed at Honeywell Laboratories. The scheme employs decentralized receding horizon controllers that reside on each vehicle to achieve coordination among team members. An appropriate graph structure describes the underlying communication topology between the vehicles. On each vehicle, information about neighbors is used to predict their behavior and plan conflict-free trajectories that maintain coordination and achieve team objectives. When feasibility of the decentralized control is lost, collision avoidance is ensured by invoking emergency maneuvers that are computed via invariant set theory.