Autonomous Control Architecture Research Articles

Autonomous distributed braking and driving force control architecture based on broadcast control for vehicles with in-wheel motors on four wheels

In this paper, we investigate an architecture for autonomous distributed control of braking and driving force on each wheel of a vehicle equipped with in-wheel motors on four wheels. Specifically, in a scenario where the driving force of some wheels is reduced due to low-friction road or motor failure during going straight ahead, the remaining wheels that can generate driving force autonomously control the yaw rate caused by the unbalanced driving force and driving force reduction to maintain the vehicle behavior. To realize autonomous distributed control, we focus on broadcast control from the control theory of multi-agent systems, which deals with the control of systems composed of many elements. We verify by simulation that the proposed architecture performs as desired for the assumed scenario.

Cloud-edge orchestration-based bi-level autonomous process control for mass individualization of rapid printed circuit boards prototyping services

Rapid printed circuit boards (PCB) prototyping refers to the pilot production of PCB before mass production for verifying the circuit design. The rapid PCB prototyping service providers are pursuing the mass individualization paradigm. Yet, these providers suffer from low efficiency and high cost in the large-scale small-batched mixed pilot production of rapid PCB prototyping. To improve the production efficiency in mass individualization of rapid PCB prototyping, this paper proposed a cloud-edge orchestration-based bi-level autonomous process control (CEO-BAPC) framework. A blockchained smart contracts-driven multi-agent system is established at the edge to realize efficient tasks coordination under disturbances. Based on the operation events data and control decisions collected at the edge, a customized deep learning-driven prediction model is established at the cloud for supporting the rescheduling decisions. The blockchained smart contracts (e.g., Stackelberg game solution) at the edge proactively decentralize short-term fine-grained individualized task execution among manufacturing units/machines and make the results available on upper-level deep learning model at the cloud for supporting holistic rescheduling decisions. The bi-level autonomous process control architecture avoids the inconsistency between holistic control and local execution under frequent random disturbances and thereby realizes efficient manufacturing of mass individualization. Through a simulated case based on data collected from a rapid PCB prototyping service provider in China, the feasibility and efficiency of the proposed CEO-BAPC framework are verified.

Metamodel of autonomous control architecture for transport process flows in open pit mines

Metamodel of autonomous control architecture for transport process flows in open pit mines

An Adaptive Model Predictive Control Scheme for Energy-Efficient Control of Building HVAC Systems

Abstract An autonomous adaptive model predictive control (MPC) architecture is presented for control of heating, ventilation, and air condition (HVAC) systems to maintain indoor temperature while reducing energy use. Although equipment use and occupant changes with time, existing MPC methods are not capable of automatically relearning models and computing control decisions reliably for extended periods without intervention from a human expert. We seek to address this weakness. Two major features are embedded in the proposed architecture to enable autonomy: (i) a system identification algorithm from our prior work that periodically re-learns building dynamics and unmeasured internal heat loads from data without requiring re-tuning by experts. The estimated model is guaranteed to be stable and has desirable physical properties irrespective of the data; (ii) an MPC planner with a convex approximation of the original nonconvex problem. The planner uses a descent and convergent method, with the underlying optimization problem being feasible and convex. A yearlong simulation with a realistic plant shows that both of the features of the proposed architecture—periodic model and disturbance update and convexification of the planning problem—are essential to get performance improvement over a commonly used baseline controller. Without these features, long-term energy savings from MPC can be small while with them, the savings from MPC become substantial.

Intelligent Target Visual Tracking and Control Strategy for Open Frame Underwater Vehicles

SUMMARYVisual tracking is an essential building block for target tracking and capture of the underwater vehicles. On the basis of remotely autonomous control architecture, this paper has proposed an improved kernelized correlation filter (KCF) tracker and a novel fuzzy controller. The model is trained to learn an online correlation filter from a plenty of positive and negative training samples. In order to overcome the influence from occlusion, the improved KCF tracker has been designed with an added self-discrimination mechanism based on system confidence uncertainty. The novel fuzzy logic tracking controller can automatically generate and optimize fuzzy rules. Through Q-learning algorithm, the fuzzy rules are acquired through the estimating value of each state action pairs. An S surface based fitness function has been designed for the improvement of learning based particle swarm optimization. Tank and channel experiments have been carried out to verify the proposed tracker and controller through pipe tracking and target grasp on the basis of designed open frame underwater vehicle.

An agent architecture for autonomous UAV flight control in object classification and recognition missions

An agent architecture for autonomous UAV flight control in object classification and recognition missions

Decentralised resilient autonomous control architecture for dynamic microgrids

Microgrids serve as an integral part of future power distribution systems. Typically, microgrids are managed by centralised controllers. There are two major concerns about using a single centralised controller. The controller can become a performance and reliability bottleneck for the entire system, where its failure can bring the entire system down. Excessive communication delays can also degrade the system performance. As a solution, a true decentralised control architecture for microgrids is proposed, designed, developed, and tested here. Distributing the controls to local agents decreases the possibility of network congestion to occur. Decentralisation will also enhance the reliability of the system since the single point of failure is replaced by a distributed architecture. The proof-of-concept of true decentralisation of microgrid control architecture is implemented using Hardware-in-the-Loop Platform. Device level and system level controller and interaction models are defined for a self-coordination. Also, microgrid energy management system (EMS) and control case scenarios are demonstrated. The experimental results show the robustness of the proposed architecture.

Exploiting External Events for Resource Adaptation in Virtual Computer and Network Systems

Current network service requirements are increasing the pressure on the level of flexibility and reliability that must be provided by the virtual computer and network systems that host them while enforcing close-to-optimum resource allocations to minimize both monetary and operational costs. A key requirement is the fast detection and adaptation to workload changes, which is beyond human abilities, so the operation must be automated. To do so we propose to use an autonomic resource control architecture. In this paper, we formalize the architecture and discuss how it is able to detect the CPU load changes of a networked service by analyzing heterogeneous observations, including both resource load measurements and external events, and adapt (increase or decrease) the allocated resources consequently. The key benefit of our proposal is that, in most situations, including external information into the analysis, such as seismometer measurements, allows the control architecture to detect new situations earlier than analyzing only load measurements. We demonstrate such claims with the results obtained from the execution of a proof-of-concept implementation of the architecture we propose on an emergency scenario.

A Survey on Behavior Control Architectures for Social Robots in Healthcare Interventions

The use of Robot-Assisted Therapy (RAT) in healthcare interventions has increasingly received research attention. However, a lot of RAT studies are conducted under Wizard of Oz (WoZ) techniques in which the robots are teleoperated or pre-programmed. The trend of RAT is moving towards (partially) autonomous control in which the robot behavior control architecture plays a significant role in creating effective human–robot interaction by engaging and motivating human users into the therapeutic processes. This paper describes the state-of-the-art of the autonomous behavior control architectures currently developed for social robots in healthcare interventions, considering both clinical and exploratory studies. We also present certain requirements that an architecture used in RAT study should acquire, which provide roboticists and therapists an inspiration to orient their designs and implementations on the basis of their targeted RAT applications.

A station-keeping control method for GEO spacecraft based on autonomous control architecture

Based on the analysis of perturbation force for GEO (geostationary orbit, GEO) spacecraft and the reference of intelligent autonomous control idea from DS1 (Deep Space 1, DS1), this paper presents a station-keeping control method for GEO spacecraft based on autonomous control architecture, which realizes north-south station-keeping and west-east station-keeping simultaneously. To further verify the effectiveness of the proposed method, corresponding autonomous station-keeping control software is designed and ground simulation test is carried out. Simulation results show that the proposed method achieves a better performance in autonomous station-keeping.

A Blackboard-style decision-making system for multi-tier craft control and its evaluation

This article presents an approach for decision-making in support of the control of an autonomous system of multiple tiers of robots (e.g., satellite, aerial and ground) based on the Blackboard architectural style. Under the proposed approach, the system evaluates prospective approaches for goal satisfaction (identified by user selected final rules), identifies the lowest-cost solution and determines the best path to achieving the goal, via the analysis of the Blackboard rule and action set. Two different approaches to this rule and action path generation are discussed. This article presents the proposed Blackboard-style architecture for autonomous multi-tier control and describes its implementation. The benefits and drawbacks of the Blackboard-style approach are analysed, its extrapolation to the control of multiple heterogeneous craft is presented and the tradeoffs between the two approaches to rule-path generation are assessed.

Autonomic computing technology for autonomous marine vehicles

Autonomous Marine Vehicles (AMVs) are not only being required to carry out more complex tasks but also longer missions. This mainly requires effective resilient operation and efficient resource management to succeed in persistent presence at sea or ocean with minimal human interaction while maintaining seakeeping performance. Even though some of the current AMVs have a large degree of self-governance, most of them fail to support self-management (e.g. auto-maintenance during pre/in/post-mission phases). Autonomic Computing (AC) basically provides the following self-managing capabilities: self-healing, self-protecting, self-optimizing, and self-configuring. In addition, it provides systems with self-aware, self-adjusted, and self-situated abilities. AC comes from a biological metaphor based on the self-regulating capabilities of the autonomic nervous system in the human body. This paper introduces the AC concept to control architectures of AMVs to endow them with resilience and environmental efficiency. The above capabilities are to help persistent autonomy and automation endure over complex and long AMV operations. This paper presents the architectural aspects, and details of design and realization of this promising AC-based approach. It also discusses four key aspects from existing methodologies and technologies that are potential approaches to support the autonomic control architecture proposed for AMVs. Finally, future research directions are presented.

On the analysis of hierarchical autonomic control of multiparty services

The increasing interest in group-based multimedia services, followed by the larger resource demands and the quest for seamless mobility support, have been propelling research on novel approaches capable of overcoming the challenges posed by future networking environments. One of those challenges is heterogeneity, which can also be leveraged in favor of more enriched services through context awareness and thus enhance user service perception. In this article we describe a context-driven framework for multiparty content delivery and discuss the rewards of employing the abstract multiparty transport concept, which provides autonomic control of personalized group-based services to users through a hierarchical strategy. Since scalability is the major concern when dealing with group-based services, we evaluated the framework and its embedded concepts regarding this feature. Herein, we describe an analytical study focused on quantifying the necessary reconfigurations in the network due to any type of context change. The outcomes of this study show that through the concept of abstract multiparty trees, we obtain considerable gains regarding link savings and consequent network control operations, thus increasing the scalability of the autonomic control architecture.

Design, Simulation, Fabrication and Testing of a Bio-Inspired Amphibious Robot with Multiple Modes of Mobility

Surf-zone environments represent an extreme challenges to robot operation. A robot that autonomously navigates rocky terrain, constantly changing underwater currents, hard-packed moist sand and loose dry sand characterizing this environment, would have significant utility in a range of defence and civilian missions. The study of animal locomotion mechanisms can elucidate specific movement principles that can be applied to address these demands. In this work, we report on the design and optimization of a biologically inspired amphibious robot for deployment and operation in an ocean beach environment. We specifically report a new design fusing a range of insectinspired passive mechanisms with active autonomous control architectures to seamlessly adapt to and traverse a range of challenging substrates both in and out of the water, and the design and construction of SeaDog, a proof-of-concept amphibious robot built for navigating rocky or sandy beaches and turbulent surf zones. The robot incorporates a layered hull and chassis design that is integrated into a waterproof Explorer Case in order to provide a large, protected payload in an easy-to-carry package. It employs a rugged drivetrain with four wheel-legs and a unique tail design and actuation strategy to aid in climbing, swimming and stabilization. Several modes of terrestrial and aquatic locomotion are suggested and tested versus range of mobility metrics, including data obtained in simulation and hardware testing. A waterproofing strategy is also tested and discussed, providing a foundation for future generations of amphibious mobile robots.

Special Issue on Focused Areas and Future Trends of Bio-Inspired Robots “Analysis, Control, and Design for Bio-Inspired Robotics”

Special Issue on Focused Areas and Future Trends of Bio-Inspired Robots “Analysis, Control, and Design for Bio-Inspired Robotics”

Design and testing of a highly mobile Insect-inspired autonomous robot In a beach environment

The capability of autonomous platforms to function on beaches and in the ocean surf-zone is critical for a wide range of military and civilian operations. Of particular importance is the ability to navigate autonomously through the rocky terrain, hard-packed moist sand, and loose dry sand characterizing this environment. The study of animal locomotion mechanisms can elucidate specific movement principles that can be applied to address these demands. In this work, we report the design, fabrication, control system development, simulation, and field testing of a biologically inspired autonomous robot for deployment and operation in an ocean beach environment. The robot successfully fuses a range of insect-inspired passive mechanisms with active autonomous control architectures to seamlessly adapt to and traverse through a range of challenging substrates. field testing establishes the performance of the robot to navigate semi-rugged terrain in the surf-zone environment including soft to hard-packed sand, mild to medium inclines, and rocky terrain. Platform autonomy is shown to be effective for navigation and communication. The fusion of passive mechanisms and active control algorithms results in a robot with mobility comparable to a legged vehicle with a control system of comparable simplicity to a wheeled robot. based on the success of this platform, we further introduce the design of a fully amphibious robot designed to extend its performance to completely undersea surroundings.

Behavioral task processing for cognitive robots using artificial emotions

This paper presents an artificial emotional-cognitive system-based autonomous robot control architecture for a four-wheel driven and four-wheel steered mobile robot. Discrete stochastic state-space mathematical model is considered for behavioral and emotional transition processes of the autonomous mobile robot in the dynamic realistic environment. The term of cognitive mechanism system which is composed from rule base and reinforcement self-learning algorithm explain all of the deliberative events such as learning, reasoning and memory (rule spaces) of the autonomous mobile robot. The artificial cognitive model of autonomous robot control architecture has a dynamic associative memory including behavioral transition rules which are able to be learned for achieving multi-objective robot tasks. Motivation module of architecture has been considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors for long-term action planning. Also reinforcement self-learning and reasoning ability of artificial cognitive model and motivational gain effects of proposed architecture can be observed on the executing behavioral sequences during simulation. The posture and speed of the robot and the configurations, speeds and torques of the wheels and all deliberative and cognitive events can be observed from the simulation plant and virtual reality viewer. This study constitutes basis for the multi-goal robot tasks and artificial emotions and cognitive mechanism-based behavior generation experiments on a real mobile robot.

Cooperative Control of Multiple Marine Vehicles Theoretical Challenges and Practical Issues

This paper is a brief overview of some of the theoretical and practical issues that arise in the process of developing advanced motion control systems for cooperative multiple autonomous marine vehicles (AMVs). Many of the problems addressed have been formulated in the scope of the EU GREX project, entitled Coordination and Control of Cooperating Heterogeneous Unmanned Systems in Uncertain Environments. The paper offers a concise introduction to the general problem of cooperative motion control that is well rooted in illustrative mission scenarios developed collectively by the GREX partners. This is followed by the description of a general architecture for cooperative autonomous marine vehicle control in the presence of time-varying communication topologies and communication losses. The results of simulations with the NetMar SyS (Networked Marine Systems Simulator) of ISR/IST are presented and show the efficacy of the algorithms developed for cooperative motion control. The paper concludes with a description of representative results obtained during a series of tests at sea in the Azores, in 2008.

Spacecraft Task Hierarchical Scheduling Analysis Within Fixed Priorities

在航天器自主控制结构中, 采用分层结构构建系统, 实现不同分组的软件互不影响执行. 针对固定优先级调度模型, 通过对分区可调度性和分区设计问题进行研究, 仔细考察了任务最大响应时间迭代计算过程, 提出了一种更为精确的求解算法; 在固定优先级任务利用率上限的基础上, 给出了分区任务可设计的判定条件; 通过利用价值函数, 给出了分区参数解析模型, 在此基础上, 提出了一种局部最优的设计方法来实现整个处理器的分区设计, 通过具体实例对所提出的分区分析和设计方法进行了验证.

Learning behavior fusion from demonstration

A critical challenge in robot learning from demonstration is the ability to map the behavior of the trainer onto a robot’s existing repertoire of basic/primitive capabilities. In part, this problem is due to the fact that the observed behavior of the teacher may consist of a combination (or superposition) of the robot’s individual primitives. The problem becomes more complex when the task involves temporal sequences of goals. We introduce an autonomous control architecture that allows for learning of hierarchical task representations, in which: (1) every goal is achieved through a linear superposition (or fusion) of robot primitives and (2) sequencing across goals is achieved through arbitration. We treat learning of the appropriate superposition as a state estimation problem over the space of possible linear fusion weights, inferred through a particle filter. We validate our approach in both simulated and real world environments with a Pioneer 3DX mobile robot.