A Multi-Vehicle Control Framework With Application to Automated Valet Parking

We introduce a distributed control method for coordinating multiple vehicles in the framework of an automated valet parking (AVP) system. The control functionality is distributed between an infrastructure server, called parking area management (PAM) system, and local autonomous vehicle control units. Via a vehicle-to-infrastructure (V2I) communication interface, model predictive control (MPC) decisions of the vehicles are shared with the coordination unit in the PAM. This unit in turn computes a coupling feedback which is shared with the vehicles. The control system is integrated in an automated test-system to cope with the high test requirements and short development cycles of highly automated systems. Evaluations conducted with the test-system show the functionality of the proposed distributed control method for multi-vehicle coordination. Results indicate safe coordination, and an efficiency increase compared to an uncoordinated method in an AVP simulation environment

Stabilization of Vehicle Formations-A Case Study

This work treats a specific multi-vehicle control case, namely six vehicles on the Multi-Vehicle Wireless Testbed (MTWT), wich is an arena at Caltech, California, built for doing experiments on small vehicles. A model is set up and a controller is found through simulations that stabilizes the system. Stability is proven through a Nyquist-like criterion

Multi-vehicle Control in a Strong Flowfield with Application to Hurricane Sampling

A major obstacle to path-planning and formation-control algorithms in multi-vehicle systems are strong flows in which the ambient flow speed is greater than the vehicle speed relative to the flow. This challenge is espe-cially pertinent in the application of unmanned aircraft used for collecting targeted observations in a hurricane. The presence of such a flowfield may inhibit a vehicle from making forward progress relative to a ground-fixed frame, thus limiting the directions in which it can travel. Using a self-propelled particle model in which each particle moves at constant speed relative to the flow, this paper presents results for motion coordination in a strong, known flowfield. We present the particle model with respect to inertial and rotating reference frames and provide for each case a set of con-ditions on the flowfield that ensure trajectory feasibility. Results from the Lyapunov-based design of decentralized control algorithms are presented for circular, folium, and spirograph trajectories, which are selected for their potential use as hurricane sampling trajectories. The theoretical results are illustrated using numerical simulations in an idealized hurricane model. Nomenclature N Number of particles in the system k Particle index k = 1,..., N rk Position of k th particle with respect to inertial frame r̃k Position of k th particle with respect to rotating fram

A Decomposition Approach to Multi-Vehicle Cooperative Control

We present methods that generate cooperative strategies for multi-vehicle control problems using a decomposition approach. By introducing a set of tasks to be completed by the team of vehicles and a task execution method for each vehicle, we decomposed the problem into a combinatorial component and a continuous component. The continuous component of the problem is captured by task execution, and the combinatorial component is captured by task assignment. In this paper, we present a solver for task assignment that generates near-optimal assignments quickly and can be used in real-time applications. To motivate our methods, we apply them to an adversarial game between two teams of vehicles. One team is governed by simple rules and the other by our algorithms. In our study of this game we found phase transitions, showing that the task assignment problem is most difficult to solve when the capabilities of the adversaries are comparable. Finally, we implement our algorithms in a multi-level architecture with a variable replanning rate at each level to provide feedback on a dynamically changing and uncertain environment.Comment: 36 pages, 19 figures, for associated web page see http://control.mae.cornell.edu/earl/decom

Distributed allocation of mobile sensing swarms in gyre flows

We address the synthesis of distributed control policies to enable a swarm of homogeneous mobile sensors to maintain a desired spatial distribution in a geophysical flow environment, or workspace. In this article, we assume the mobile sensors (or robots) have a "map" of the environment denoting the locations of the Lagrangian coherent structures or LCS boundaries. Based on this information, we design agent-level hybrid control policies that leverage the surrounding fluid dynamics and inherent environmental noise to enable the team to maintain a desired distribution in the workspace. We establish the stability properties of the ensemble dynamics of the distributed control policies. Since realistic quasi-geostrophic ocean models predict double-gyre flow solutions, we use a wind-driven multi-gyre flow model to verify the feasibility of the proposed distributed control strategy and compare the proposed control strategy with a baseline deterministic allocation strategy. Lastly, we validate the control strategy using actual flow data obtained by our coherent structure experimental testbed.Comment: 10 pages, 14 Figures, added reference

Semi-autonomous Intersection Collision Avoidance through Job-shop Scheduling

In this paper, we design a supervisor to prevent vehicle collisions at intersections. An intersection is modeled as an area containing multiple conflict points where vehicle paths cross in the future. At every time step, the supervisor determines whether there will be more than one vehicle in the vicinity of a conflict point at the same time. If there is, then an impending collision is detected, and the supervisor overrides the drivers to avoid collision. A major challenge in the design of a supervisor as opposed to an autonomous vehicle controller is to verify whether future collisions will occur based on the current drivers choices. This verification problem is particularly hard due to the large number of vehicles often involved in intersection collision, to the multitude of conflict points, and to the vehicles dynamics. In order to solve the verification problem, we translate the problem to a job-shop scheduling problem that yields equivalent answers. The job-shop scheduling problem can, in turn, be transformed into a mixed-integer linear program when the vehicle dynamics are first-order dynamics, and can thus be solved by using a commercial solver.Comment: Submitted to Hybrid Systems: Computation and Control (HSCC) 201

Cooperative control for multi-vehicle swarms

The cooperative control of large-scale multi-agent systems has gained a significant interest in recent years from the robotics and control communities for multi-vehicle control. One motivator for the growing interest is the application of spatially and temporally distributed multiple unmanned aerial vehicle (UAV) systems for distributed sensing and collaborative operations. In this research, the multi-vehicle control problem is addressed using a decentralised control system. The work aims to provide a decentralised control framework that synthesises the self-organised and coordinated behaviour of natural swarming systems into cooperative UAV systems. The control system design framework is generalised for application into various other multi-agent systems including cellular robotics, ad-hoc communication networks, and modular smart-structures. The approach involves identifying su itable relationships that describe the behaviour of the UAVs within the swarm and the interactions of these behaviours to produce purposeful high-level actions for system operators. A major focus concerning the research involves the development of suitable analytical tools that decomposes the general swarm behaviours to the local vehicle level. The control problem is approached using two-levels of abstraction; the supervisory level, and the local vehicle level. Geometric control techniques based on differential geometry are used at the supervisory level to reduce the control problem to a small set of permutation and size invariant abstract descriptors. The abstract descriptors provide an open-loop optimal state and control trajectory for the collective swarm and are used to describe the intentions of the vehicles. Decentralised optimal control is implemented at the local vehicle level to synthesise self-organised and cooperative behaviour. A deliberative control scheme is implemented at the local vehicle le vel that demonstrates autonomous, cooperative and optimal behaviour whilst the preserving precision and reliability at the local vehicle level

Coordinated collision avoidance for multi-vehicle systems based on collision time

Abstract Vehicles have irregular shapes and inter‐vehicle coordination is not a trivial task. Based on the distributed‐system framework, this paper studies multi‐vehicle control and coordinated obstacle avoidance for multiple autonomous vehicles with irregular shapes. The goal is to reach target points without collisions. The proposed approaches are based on collision time, which is calculated using vehicles' irregular shapes. The approaches have two parts. The first part enables a number of vehicles to reach the target points. The second part enables collision avoidance, which includes inter‐vehicle collisions and vehicle‐to‐obstacle collisions. Speed regulation approach is proposed to change the speeds, and frequency‐modulation approach is proposed to update control commands at varying steps, and a combined approach is also proposed. Simulation examples are set to verify the effectiveness of the proposed approaches