A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
Browse
4 results
Search Results
Item Cooperative Multi-Agent Sensing, Planning, and Control for Connected Autonomous Vehicles(2023) Suriyarachchi, Chethana Nilesh; Baras, John; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Modern transportation networks are witnessing ever-increasing levels of vehicle automation as subsystems originally controlled by the human driver are taken over by automated control systems. Connected autonomous vehicles (CAVs) represent the latest technological breakthrough in this domain, which promises increased safety, improved efficiency, and better accessibility. Recently, more and more academic and industrial research interest has been focused on guaranteeing the safe operation of a single CAV by harnessing its advanced sensing and computation capability. However, CAVs also have another very important capability whose potential remains largely untapped: communication. Communication among CAVs (V2V - Vehicle to Vehicle) and with infrastructure (V2I - Vehicle to Infrastructure) provide CAVs the ability to share progressively gathered information, policies, and rules, and pursue controls related to both local and global objectives (in space and time). Thus, CAVs can collaboratively achieve levels of safety and efficiency that cannot be achieved by a single vehicle working alone. With this new focus on cooperative CAV control, we propose methods and associated algorithmic implementations, through which CAVs can contribute to solving some of the persistent problems which have been plaguing road and highway infrastructure for decades. We provide problems such as highway merge junction bottlenecks, highway traffic shock waves, and signalized or unsignalized intersection management with vastly superior solutions by harnessing the communication capabilities of CAVs. In both the domains of fully autonomous traffic and mixed traffic (CAVs coexisting with human driven vehicles), communication-based CAV control allows for safer operation with improved global throughput of traffic flow. In a world facing increasing shortages of traditional fuel sources, the ability of cooperative CAV control to reduce overall fuel consumption levels is also quite notable. The algorithms proposed in this dissertation, which use heuristic, optimization, and learning-based control, can be broadly categorized as cooperative multi-agent control in which CAV sensing and/or actuation data are communicated. Sharing of this data allows CAVs to gather information about downstream traffic conditions, and overcome problems such as hidden obstacles due to sensor occlusion and dangerous conditions ahead or out of a single vehicle’s “line of sight”. This increased collection of information available to the control center (either ego vehicle or infrastructure) can then be used to compute better controls leading to safer, more efficient operation, and the ability to prevent the occurrence of certain unwanted traffic conditions. This potential capability of CAVs is explored using both centralized (often V2I-based) and decentralized (often V2V-based) control strategies. Moreover, as ensuring the safe operation of the overall system is a primary concern, in addition to achieving the desired performance, safety is a fundamental constraint built into all these methods. Our overall goal is to provide practically implementable algorithms that achieve high levels of performance and are capable of real-time operation while demonstrating robustness to variations in the environment. As such, we pay special attention to realistic driving environments including the impact of features such as multiple lanes, mixed traffic, heterogeneous traffic (cars, buses, trucks, emergency vehicles, etc.), and limitations in both communication and computation resources. We investigate the properties of communication networks used, as well as the ability of the control systems to adapt to imperfections in actuation, sensing, and communication.Item Motion Coordination of Multiple Autonomous Vehicles in a Spatiotemporal Flowfield(2012) Peterson, Cammy; Paley, Derek A; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)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.Item Three-Dimensional Motion Coordination in a Time-Varying Flowfield(2009) Hernandez-Doran, Sonia; Paley, Derek A; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Decentralized algorithms that stabilize three-dimensional formations of unmanned vehicles in a time-varying flowfield have applications in environmental monitoring in the atmosphere and ocean. This thesis provides Lyapunov-based algorithms to control a system of self-propelled particles traveling in three dimensions at a constant speed relative to a spatiotemporal flowfield. A particle's inertial velocity is the sum of its velocity relative to the flowfield plus the velocity of the flowfield. Multiple particles can be steered to form parallel, helical, and circular formations. A special case of the three-dimensional model is also studied, in which the particles travel on the surface of a sphere. In this case, we provide Lyapunov-based algorithms that stabilize circular formations in a time-varying flowfield on a rotating sphere. Because we are interested in using unmanned-vehicle formations for environmental monitoring, we simulate our results using numerical simulations of time-varying flowfields that resemble tornadoes and hurricanes.Item GEOMETRIC COOPERATIVE CONTROL OF FORMATIONS(2004-11-11) Zhang, Fumin; Krishnaprasad, P.S.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Robots in a team are modeled as particles which obey simple, second order dynamics. The whole team can be viewed as a deformable body with changing shape and orientation. Jacobi shape theory is applied to model such a formation. We derive the controlled system equations using the Lagrange-D'Alembert principle. Control forces on each robot are combined and reorganized as controls for the center, for rotation and for shape changes. From a shape-theoretic point of view, general feedback control laws are designed to achieve desired formations. The system equations on shape space provide possibilities for achieving formations without communication links between team members equipped with sufficient sensing ability. We allow each robot freedom to establish a coordinate system in which shape dynamics of the whole formation is computed. Without knowing such coordinate systems of other robots, each robot is able to perform cooperative control. This is made possible by a class of gauge covariant control laws. We argue that freedom of choosing gauge frame helps to improve controller performance. When all robots are required to have common constant speed, the control forces have to be of gyroscopic nature. Previous works of Justh and Krishnaprasad has inspired us to study the obstacle avoidance and navigation problem from a point of view of formation shape control. We achieve gyroscopic control laws to achieve boundary following behavior when the particle encounters an obstacle. The "steady state" trajectory of the particle forms a Bertrand pair with the boundary curve of the obstacle. This steady state behavior correspond to a relative equilibrium for a non-autonomous system on special Euclidean groups. Our control law achieves asymptotic convergence of the non-autonomous system dynamics. The boundary following behavior is a building block for robot navigation in a cluttered environment. Based on the configuration of the obstacles and the target, we may construct virtual boundary curves by analyzing sensory data. Such virtual boundary curves lead the robot to the target without collision. We have also studied the problem of establishing a formation of satellites with periodic shape changes near an elliptic earth orbit. We propose a control law that would set up a given formation near a given orbit. This law also allows a satellite formation to achieve orbit transfer.