Robotics and autonomous systems

Autonomous vehicle field of study has seen considerable researches within three decades. In the last decade particularly, interests in this field has undergone tremendous improvement. One of the main aspects in autonomous vehicle is the path tracking control, focusing on the vehicle control in lateral and longitudinal direction in order to follow a specified path or trajectory. In this paper, path tracking control is reviewed in terms of the basic vehicle model usually used; the control strategies usually employed in path tracking control, and the performance criteria used to evaluate the controller's performance. Vehicle model is categorised into several types depending on its linearity and the type of behaviour it simulates, while path tracking control is categorised depending on its approach. This paper provides critical review of each of these aspects in terms of its usage and disadvantages/advantages. Each aspect is summarised for better overall understanding. Based on the critical reviews, main challenges in the field of path tracking control is identified and future research direction is proposed. Several promising advancement is proposed with the main prospect is focused on adaptive geometric controller developed on a nonlinear vehicle model and tested with hardware-in-the-loop (HIL). It is hoped that this review can be treated as preliminary insight into the choice of controllers in path tracking control development for an autonomous ground vehicle.

Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges

This article proposes a memory-based event-triggering $H_{\infty }$ load frequency control (LFC) method for power systems through a bandwidth-constrained open network. To overcome the bandwidth constraint, a memory-based event-triggered scheme (METS) is first proposed to reduce the number of transmitted packets. Compared with the existing memoryless event-triggered schemes, the proposed METS has the advantage to utilize series of the latest released signals. To deal with the random deception attacks induced by open networks, a networked power system model is well established, which couples the effects of METS and random deception attacks in a unified framework. Then, a sufficient stabilization criterion is derived to obtain the memory $H_{\infty }$ LFC controller gains and event-triggered parameters simultaneously. Compared with existing memoryless LFC, the control performance is greatly improved since the latest released dynamic information is well utilized. Finally, an illustrative example is used to show the effectiveness of the proposed method.

Memory-Based Event-Triggering H ∞ Load Frequency Control for Power Systems Under Deception Attacks

Redundancy resolution is a critical problem in the control of robotic manipulators. Recurrent neural networks (RNNs), as inherently parallel processing models for time-sequence processing, are potentially applicable for the motion control of manipulators. However, the development of neural models for high-accuracy and real-time control is a challenging problem. This paper identifies two limitations of the existing RNN solutions for manipulator control, i.e., position error accumulation and the convex restriction on the projection set, and overcomes them by proposing two modified neural network models. Our method allows nonconvex sets for projection operations, and control error does not accumulate over time in the presence of noise. Unlike most works in which RNNs are used to process time sequences, the proposed approach is model-based and training-free, which makes it possible to achieve fast tracking of reference signals with superior robustness and accuracy. Theoretical analysis reveals the global stability of a system under the control of the proposed neural networks. Simulation results confirm the effectiveness of the proposed control method in both the position regulation and tracking control of redundant PUMA 560 manipulators.

Kinematic Control of Redundant Manipulators Using Neural Networks

For decreasing communication load and overcoming network constrains, such as the limited bandwidth and data loss in multi-agent networks, this paper integrates the two control strategies to investigate the bounded consensus problem of multi-agent systems (MASs) with external disturbance on the basis of an undirected graph, namely, the quantized control and the event-triggered control. In the existence of the external disturbance, two types of the high-gain control laws with the uniform quantized relative state measurements for the bounded consensus problem of MASs are first discussed, respectively. Then, in order to save the limited network resources in a multi-agent network, the event-triggered quantized communication protocols are designed based on the first case to obtain the bounded consensus in multi-agent systems. Moreover, it is shown that “Zeno behavior” phenomenon can be excluded under the two event-triggered quantized control mechanisms, and the boundness of the relative state error can be adjusted by selecting the different parameters. Finally, two examples are shown to validate the feasibility and efficiency of our theoretical analysis.

Event-Triggered Control for Consensus Problem in Multi-Agent Systems With Quantized Relative State Measurements and External Disturbance

This paper is concerned with the problem of distributed $H_{\infty }$ filtering for a cloud-aided active semi-vehicle suspension system. The information of semi-vehicle suspension system are transmitted to the cloud through wireless network with some unreliable characteristics, such as communication time-delay and limited bandwidth, which are solved by the delay-distribution dependent method and event-triggered scheme, respectively. In order to reduce the dimensions of results, the semi-vehicle suspension is decomposed into two interconnected subsystems. At the side of remote cloud, filters are designed for each subsystem with considering the unreliable characteristics of network and stochastic sensor faults, simultaneously. A codesign algorithm is provided to obtain the distributed $H_\infty$ filtering and the event-triggered scheme, simultaneously. Finally, the efficient performances of the codesigned algorithm are evaluated both in time and frequency domains under different road conditions.

Codesign of Event-Triggered and Distributed $H_{\infty }$ Filtering for Active Semi-Vehicle Suspension Systems

This paper addresses the distributed adaptive event-triggered ${H}_{\infty}$ filtering problem for a class of sector-bounded nonlinear system over a filtering network with time-varying and switching topology. Both topology switching and adaptive event-triggered mechanisms (AETMs) between filters are simultaneously considered in the filtering network design. The communication topology evolves over time, which is assumed to be subject to a nonhomogeneous Markov chain. In consideration of the limited network bandwidth, AETMs have been used in the information transmission from the sensor to the filter as well as the information exchange among filters. The proposed AETM is characterized by introducing the dynamic threshold parameter, which provides benefits in data scheduling. Moreover, the gain of the correction term in the adaptive rule varies directly with the estimation error and inversely with the transmission error. The switching filtering network is modeled by a Markov jump nonlinear system. The stochastic Markov stability theory and linear matrix inequality techniques are exploited to establish the existence of the filtering network and further derive the filter parameters. A co-design algorithm for determining ${H}_{\infty}$ filters and the event parameters is developed. Finally, some simulation results on a continuous stirred tank reactor and a numerical example are presented to show the applicability of the obtained results.

Adaptive Event-Triggered Transmission Scheme and $H_{\infty}$ Filtering Co-Design Over a Filtering Network With Switching Topology

High-precision self-localization is one of the basic functions of intelligent vehicles Moreover, location information is necessary prior information for tasks such as behavioural decision-making and path planning Recently, the technology of map-based localization has been widely concerned due to its accuracy A prior map for localization stores one or more environmental features extracted by on-board sensors These features represent fundamental information about the ground environment such as curbs and lane markings, or the vertical environment such as building outlines In order to enhance the robustness and positioning accuracy, this paper uses two layers of features to express the environment: one is a bottom layer composed of ground and curb features, and the other is an upper layer composed of a 2D point cloud with vertical features Firstly, a novel collision-based detection method is proposed to extract the curb features, and a vertical-projection-based detection algorithm is used to detect vertical feature points from multilayer LIDAR Then, the fusion of a multi-frame feature point cloud is used to represent the rich and complete information on the environment of the intelligent vehicle Finally, the Monte Carlo localization algorithm is used to obtain an optimal estimate of the vehicle position The experimental results indicate that the proposed localization algorithm can realize localization accuracy

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Intelligent Vehicle Self-Localization Based on Double-Layer Features and Multilayer LIDAR

Architecture design is one of the most important problems for an intelligent system. In this paper, a practical framework of hardware and software is proposed to reveal the external configuration and internal mechanism of an autonomous vehicle—a typical intelligent system. The main contributions of this paper are as follows. First, we compare the advantages and disadvantages of three typical sensor plans and introduce a general autopilot for a vehicle. Second, we introduce a software architecture for an autonomous vehicle. The perception and planning performances are improved with the help of two inner loops of simultaneous localization and mapping. An algorithm to enlarge the detection range of the sensors is proposed by adding an inner loop to the perception system. A practical feedback to restrain mutations of two adjacent planning periods is also realized by the other inner loop. Third, a cross-platform virtual server (named project cocktail) for data transmission and exchange is presented in detail. Through comparisons with the robot operating system, the performance of project cocktail is proven to be considerably better in terms of transmission delay and throughput. Finally, a report on an autonomous driving test implemented using the proposed architecture is presented, which shows the effectiveness, flexibility, stability, and low-cost of the overall autonomous driving system.

Architecture Design and Implementation of an Autonomous Vehicle

This paper is concerned with the stability of the vehicular platoon consisting of a leader and multiple cooperative autonomous driving followers. The objective of the platoon control is to ensure all vehicles traveling at the same speed while maintaining a safety spacing. To achieve the objective, a novel control framework consisting of the distributed adaptive event-triggered observer and the car-following control protocol is proposed for the vehicular platoon control. The condition that only few following vehicles can access the information of the leader is considered. In order to design controllers while avoid using any global information, such as the system matrix and the state of the leader, a distributed event-triggered observer is proposed, such that each vehicle can observe the dynamics of the leader. Based on this observer, both collision avoidance and limited communication source of each vehicle are simultaneously considered in the design of the observer. It is shown that under the proposed control framework, the platoon can achieve asymptotical stable, meanwhile, the amount of transmission data and communication cost among vehicles can be reduced. Finally, numerical simulations are presented to show the applicability of the obtained results.

Zhuping Wang

Papers

Codesign of Event-Triggered and Distributed $H_{\infty }$ Filtering for Active Semi-Vehicle Suspension Systems

Adaptive Event-Triggered Transmission Scheme and $H_{\infty}$ Filtering Co-Design Over a Filtering Network With Switching Topology

Intelligent Vehicle Self-Localization Based on Double-Layer Features and Multilayer LIDAR

Architecture Design and Implementation of an Autonomous Vehicle

Distributed Adaptive Event-Triggered Control and Stability Analysis for Vehicular Platoon