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

Motion Coordination of Multiple Autonomous Vehicles in a Spatiotemporal Flowfield

The long-term goal of this research is to provide theoretically justified control strategies to operate autonomous vehicles in spatiotemporal flowfields. The specific objective of this dissertation is to use estimation and nonlinear control techniques to generate decentralized control algorithms that enable motion coordination for multiple autonomous vehicles while operating in a time-varying flowfield. A cooperating team of vehicles can benefit from sharing data and tasking responsibilities. Many existing control algorithms promote collaboration of autonomous vehicles. However, these algorithms often fail to account for the degradation of control performance caused by flowfields. This dissertation presents decentralized multivehicle coordination algorithms designed for operation in a spatially or temporally varying flowfield. Each vehicle is represented using a Newtonian particle traveling in a plane at constant speed relative to the flow and subject to a steering control. Initially, we assume the flowfield is known and describe algorithms that stabilize a circular formation in a time-varying spatially nonuniform flow of moderate intensity. These algorithms are extended by relaxing the assumption that the flow is known: the vehicles dynamically estimate the flow and use that estimate in the control. We propose a distributed estimation and control algorithm comprising a consensus filter to share information gleaned from noisy position measurements, and an information filter to reconstruct a spatially varying flowfield. The theoretical results are illustrated with numerical simulations of circular formation control and validated in outdoor unmanned aerial vehicle (UAV) flight tests

Safe Coordination of Autonomous Vehicles

As more and more autonomous vehicles enter our road, new mechanisms must be considered to ensure the safe coordination between the autonomous vehicles. Al- though many algorithms have been proposed to coordinate autonomous vehicles, few of them have considered the robustness of the solution against disturbances. Therefore, in this master’s thesis, a vehicle coordination algorithm that uses vehicle to vehicle (V2V) communication is design in order to achieve collision free trajectories, while rejecting disturbances. Specifically, a robust tube-based model predictive control (MPC) scheme is proposed in order to control the autonomous vehicle. This controller uses series of zonotopic reachable sets (also known as tube) to compute a set of state and input constraints, which ensure the robust feasibility of the problem. To reduce the computational burden of the MPC opti- mization problem, the vehicle model is reformulated into a pseudo-linear model by transforming its non-linear equations into the linear parameter varying (LPV) form. The disturbance rejection is performed by a H∞-optimal corrective con- troller. Finally, the collision avoidance is achieved by a V2V coordination algo- rithm, in which the lateral bounds of a collision free path are computed. To validate the proposed control scheme, a series of simulations have been performed to test the disturbance rejection of the corrective controller, as well as the vehicle coordination capabilities. The results from these tests show that the proposed controller is effective in coordinating a multiple overtaking maneuver, while rejecting the disturbances

Ripples: a tool for supervision and control of remote assets

We describe the Ripples cloud-based software for coordination and control of multiple remote assets. Ripples can ingest and disseminate data coming from multiple sources such as physical models, drifting sensors, marine traffic (AIS) and unmanned vehicles deployed in remote areas. On top of data dissemination and awareness, Ripples can also be used for planning the autonomous assets using satellite communications, maintaining the operators in the loop.Peer Reviewe

The design and implementation of a multi-agent architecture to increase coordination efficiency in multi-AUV operations

This research addresses the problem of coordinating multiple autonomous underwater vehicle (AUV) operations. An intelligent mission executive has been created that uses multi-agent technology to control and coordinate multiple AUVs in communication deficient environments. By incorporating real time vehicle prediction, blackboardbased hierarchical mission plans and mission optimisation in conjunction with a simple broadcast communication system this system aims to handle the limitations inherent in underwater operations and intelligently control multiple vehicles. In this research efficiency is evaluated and then compared to the current state of the art in multiple AUV control. The research is then validated in real AUV coordination trials. Results will show that compared to the state of the art the control system developed and implemented in this research coordinates multiple vehicles more efficiently and is able to function in a range of poor communication environments. These findings are supported by in water validation trials with heterogeneous AUVs. This thesis will first present an in depth state of the art of the related research topics including multi-agent systems, collaborative robotics and autonomous underwater vehicles. The development and functionality of this research will then be explained followed by a detailed description of the experiments. Results are then presented both for the simulated and real world trials followed by a discussion of the findings

Information-Theoretic Control of Multiple Sensor Platforms

This thesis is concerned with the development of a consistent, information-theoretic basis for understanding of coordination and cooperation decentralised multi-sensor multi-platform systems. Autonomous systems composed of multiple sensors and multiple platforms potentially have significant importance in applications such as defence, search and rescue mining or intelligent manufacturing. However, the effective use of multiple autonomous systems requires that an understanding be developed of the mechanisms of coordination and cooperation between component systems in pursuit of a common goal. A fundamental, quantitative, understanding of coordination and cooperation between decentralised autonomous systems is the main goal of this thesis. This thesis focuses on the problem of coordination and cooperation for teams of autonomous systems engaged in information gathering and data fusion tasks. While this is a subset of the general cooperative autonomous systems problem, it still encompasses a range of possible applications in picture compilation, navigation, searching and map building problems. The great advantage of restricting the domain of interest in this way is that an underlying mathematical model for coordination and cooperation can be based on the use of information-theoretic models of platform and sensor abilities. The information theoretic approach builds on the established principles and architecture previously developed for decentralised data fusion systems. In the decentralised control problem addressed in this thesis, each platform and sensor system is considered to be a distinct decision maker with an individual information-theoretic utility measure capturing both local objectives and the inter-dependencies among the decisions made by other members of the team. Together these information-theoretic utilities constitute the team objective. The key contributions of this thesis lie in the quantification and study of cooperative control between sensors and platforms using information as a common utility measure. In particular, * The problem of information gathering is formulated as an optimal control problem by identifying formal measures of information with utility or pay-off. * An information-theoretic utility model of coupling and coordination between decentralised decision makers is elucidated. This is used to describe how the information gathering strategies of a team of autonomous systems are coupled. * Static and dynamic information structures for team members are defined. It is shown that the use of static information structures can lead to efficient, although sub-optimal, decentralised control strategies for the team. * Significant examples in decentralised control of a team of sensors are developed. These include the multi-vehicle multi-target bearings-only tracking problem, and the area coverage or exploration problem for multiple vehicles. These examples demonstrate the range of non-trivial problems to which the theory in this thesis can be employed

Information-Theoretic Control of Multiple Sensor Platforms

This thesis is concerned with the development of a consistent, information-theoretic basis for understanding of coordination and cooperation decentralised multi-sensor multi-platform systems. Autonomous systems composed of multiple sensors and multiple platforms potentially have significant importance in applications such as defence, search and rescue mining or intelligent manufacturing. However, the effective use of multiple autonomous systems requires that an understanding be developed of the mechanisms of coordination and cooperation between component systems in pursuit of a common goal. A fundamental, quantitative, understanding of coordination and cooperation between decentralised autonomous systems is the main goal of this thesis. This thesis focuses on the problem of coordination and cooperation for teams of autonomous systems engaged in information gathering and data fusion tasks. While this is a subset of the general cooperative autonomous systems problem, it still encompasses a range of possible applications in picture compilation, navigation, searching and map building problems. The great advantage of restricting the domain of interest in this way is that an underlying mathematical model for coordination and cooperation can be based on the use of information-theoretic models of platform and sensor abilities. The information theoretic approach builds on the established principles and architecture previously developed for decentralised data fusion systems. In the decentralised control problem addressed in this thesis, each platform and sensor system is considered to be a distinct decision maker with an individual information-theoretic utility measure capturing both local objectives and the inter-dependencies among the decisions made by other members of the team. Together these information-theoretic utilities constitute the team objective. The key contributions of this thesis lie in the quantification and study of cooperative control between sensors and platforms using information as a common utility measure. In particular, * The problem of information gathering is formulated as an optimal control problem by identifying formal measures of information with utility or pay-off. * An information-theoretic utility model of coupling and coordination between decentralised decision makers is elucidated. This is used to describe how the information gathering strategies of a team of autonomous systems are coupled. * Static and dynamic information structures for team members are defined. It is shown that the use of static information structures can lead to efficient, although sub-optimal, decentralised control strategies for the team. * Significant examples in decentralised control of a team of sensors are developed. These include the multi-vehicle multi-target bearings-only tracking problem, and the area coverage or exploration problem for multiple vehicles. These examples demonstrate the range of non-trivial problems to which the theory in this thesis can be employed

Cooperation Attitude Control as a Part of Spacecraft Formation Flying

Cooperative and coordination control for Autonomous Multi-Agent Systems (AMAS) are gaining more popularity and interest in many areas of aerospace engineering, such as air-traffic control, swarming satellites, launch/reentry-vehicle systems, and Formation Flying (FF). There are many advantages of cooperative control of autonomous FF of multiple small aerospace vehicles to replace a single large vehicle, such as increasing feasibility, reducing cost, probability of success, and significantly widening the operating area. For example, a group of cooperative Earth Observation radar satellites can enhance the overall resolution by observing backscattered signals from different angles compared to one giant costly satellite observing from one angle. Aerospace FF applications include distributed antennas, atmospheric sampling, and synthetic aperture radars. Besides, it is appealing to have robust and optimal control for space manufacturing and servicing. The Nano/microsatellites market is expected to grow as more companies develop smaller, cheaper launch vehicles. This paper demonstrates a model-based design for decentralized cooperation control as part of spacecraft formation flying using a single-integrator dynamic for deep space exploration missions

Collaborative signal and information processing for target detection with heterogeneous sensor networks

In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield