自主智能系统(英文)最新文献

With the development of autonomous car, a vehicle is capable to sense its environment more precisely. That allows improved drving behavior decision strategy to be used for more safety and effectiveness in complex scenarios. In this paper, a decision making framework based on hierarchical state machine is proposed with a top-down structure of three-layer finite state machine decision system. The upper layer classifies the driving scenario based on relative position of the vehicle and its surrounding vehicles. The middle layer judges the optimal driving behavior according to the improved energy efficiency function targeted at multiple criteria including driving efficiency, safety and the grid-based lane vacancy rate. The lower layer constructs the state transition matrix combined with the calculation results of the previous layer to predict the optimal pass way in the region. The simulation results show that the proposed driving strategy can integrate multiple criteria to evaluate the energy efficiency value of vehicle behavior in real time, and realize the selection of optimal vehicle driving strategy. With popularity of automatic vehicles in future, the driving strategy can be used as a reference to provide assistance for human drive or even the real-time decision-making of autonomous driving.

We study in this paper a semi-global leader-following output consensus problem for multiple heterogeneous linear systems in the presence of actuator position and rate saturation over a directed topology. For each follower, via the low gain feedback design technique and output regulation theory, both a state feedback consensus protocol and an output feedback consensus protocol are constructed. In the output feedback case, different distributed observers are designed for the informed followers and uninformed followers to estimate the state of the leader and the follower itself. We show that the semi-global leader-following output consensus of heterogeneous linear systems can be achieved by the two consensus protocols if each follower is reachable from the leader in the directed communication topology.

This paper investigates the robust flocking problem for second-order nonlinear systems with a leader and external disturbances. In contrast with most of second-order systems in the literature, the intrinsic dynamics here are nonlinear and non-identical that depend not only on the velocity but also on the position, which is more realistic. Moreover, the interaction topology is undirected and switching. Provided that the leader’s velocity may be constant or time-varying, two distributed flocking control laws have been proposed for two cases to make the differences of the velocities between all followers and the leader approach to zero asymptotically. The proposed distributed flocking control laws are both model-independent which results in the effectiveness of the controllers to cope with the different intrinsic dynamics of the followers and the leader under some assumptions on boundedness of several states. An example is given to illustrate the validity of the theoretical results.

Over the past several years, we have been studying the problem of optimally rotating a rigid sphere about its center, where the rotation is actuated by a triplet of external torques acting on the body. The control objective is to repeatedly direct a suitable radial vector, called the gaze vector, towards a stationary point target in IR³. The orientation of the sphere is constrained to lie in a suitable submanifold of SO(3). Historically, the constrained rotational movements were studied by physiologists in the nineteenth century, interested in eye and head movements. In this paper we revisit the gaze control problem, where two visual sensors, are tasked to simultaneously stare at a point target in the visual space. The target position changes discretely and the problem we consider is how to reorient the gaze directions of the sensors, along the optimal pathway of the human eyes, to the new location of the target. This is done by first solving an optimal control problem on the human binocular system. Next, we use these optimal control and show that a pan-tilt system can be controlled to follow the gaze trajectory of the human eye requiring a nonlinear static feedback of the pan and tilt angles and their derivatives. Our problem formulation uses a new Riemannian geometric description of the orientation space. The paper also introduces a new, pyramid based interpolation method, to implement the optimal controller.

This paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

The visual guidance of goal-directed movements requires transformations of incoming visual information that are different from those required for visual perception. For us to grasp an object successfully, our brain must use just-in-time computations of the object’s real-world size and shape, and its orientation and disposition with respect to our hand. These requirements have led to the emergence of dedicated visuomotor modules in the posterior parietal cortex of the human brain (the dorsal visual stream) that are functionally distinct from networks in the occipito-temporal cortex (the ventral visual stream) that mediate our conscious perception of the world. Although the identification and selection of goal objects and an appropriate course of action depends on the perceptual machinery of the ventral stream and associated cognitive modules, the execution of the subsequent goal-directed action is mediated by dedicated online control systems in the dorsal stream and associated motor areas. The dorsal stream allows an observer to reach out and grasp objects with exquisite ease, but by itself, deals only with objects that are visible at the moment the action is being programmed. The ventral stream, however, allows an observer to escape the present and bring to bear information from the past – including information about the function of objects, their intrinsic properties, and their location with reference to other objects in the world. Ultimately then, both streams contribute to the production of goal-directed actions. The principles underlying this division of labour between the dorsal and ventral streams are relevant to the design and implementation of autonomous robotic systems.

In this paper, a distributed stochastic approximation algorithm is proposed to track the dynamic root of a sum of time-varying regression functions over a network. Each agent updates its estimate by using the local observation, the dynamic information of the global root, and information received from its neighbors. Compared with similar works in optimization area, we allow the observation to be noise-corrupted, and the noise condition is much weaker. Furthermore, instead of the upper bound of the estimate error, we present the asymptotic convergence result of the algorithm. The consensus and convergence of the estimates are established. Finally, the algorithm is applied to a distributed target tracking problem and the numerical example is presented to demonstrate the performance of the algorithm.

In generalized Nash equilibrium (GNE) seeking problems over physical networks such as power grids, the enforcement of network constraints and time-varying environment may bring high computational costs. Developing online algorithms is recognized as a promising method to cope with this challenge, where the task of computing system states is replaced by directly using measured values from the physical network. In this paper, we propose an online distributed algorithm via measurement feedback to track the GNE in a time-varying networked resource sharing market. Regarding that some system states are not measurable and measurement noise always exists, a dynamic state estimator is incorporated based on a Kalman filter, rendering a closed-loop dynamics of measurement-feedback driven online algorithm. We prove that, with a fixed step size, this online algorithm converges to a neighborhood of the GNE in expectation. Numerical simulations validate the theoretical results.