Fuzzy tracking method with a switching grey prediction for mobile robot

[[abstract]]A mobile robot fuzzy tracking mechanism for a mobile robot is proposed in this paper. A switching grey prediction is proposed such that the mobile robot can track the target effectively. Conventional position-based tracking strategies usually define the dynamics models either for motion control on the tracker or for position prediction on the target. This makes the system inflexible and difficult to implement. Our fuzzy tracking systems based on switching grey prediction can be extended as a flexible strategy in dealing with uncertain environments in robotics applications. The grey prediction attempts to establish a grey model from the recent historical information. The model is updated online as the input changes. According to the grey model, the switching mechanism can adjust the forecasting step size for prediction. The performances of the simulations are presented. It is clear that the proposed tracking systems are effective and could easily be made available.[[conferencetype]]國際[[conferencedate]]20011202~20011205[[iscallforpapers]]Y[[conferencelocation]]Melbourne, Australi

[[alternative]]Scene Analysis and Intelligent Control Strategy Design for the Robot Soccer(III)

計畫編號：NSC91-2213-E032-002研究期間：200208~200307研究經費：410,000[[sponsorship]]行政院國家科學委員

Control of autonomous multibody vehicles using artificial intelligence

The field of autonomous driving has been evolving rapidly within the last few years and a lot of research has been dedicated towards the control of autonomous vehicles, especially car-like ones. Due to the recent successes of artificial intelligence techniques, even more complex problems can be solved, such as the control of autonomous multibody vehicles. Multibody vehicles can accomplish transportation tasks in a faster and cheaper way compared to multiple individual mobile vehicles or robots. But even for a human, driving a truck-trailer is a challenging task. This is because of the complex structure of the vehicle and the maneuvers that it has to perform, such as reverse parking to a loading dock. In addition, the detailed technical solution for an autonomous truck is challenging and even though many single-domain solutions are available, e.g. for pathplanning, no holistic framework exists. Also, from the control point of view, designing such a controller is a high complexity problem, which makes it a widely used benchmark. In this thesis, a concept for a plurality of tasks is presented. In contrast to most of the existing literature, a holistic approach is developed which combines many stand-alone systems to one entire framework. The framework consists of a plurality of modules, such as modeling, pathplanning, training for neural networks, controlling, jack-knife avoidance, direction switching, simulation, visualization and testing. There are model-based and model-free control approaches and the system comprises various pathplanning methods and target types. It also accounts for noisy sensors and the simulation of whole environments. To achieve superior performance, several modules had to be developed, redesigned and interlinked with each other. A pathplanning module with multiple available methods optimizes the desired position by also providing an efficient implementation for trajectory following. Classical approaches, such as optimal control (LQR) and model predictive control (MPC) can safely control a truck with a given model. Machine learning based approaches, such as deep reinforcement learning, are designed, implemented, trained and tested successfully. Furthermore, the switching of the driving direction is enabled by continuous analysis of a cost function to avoid collisions and improve driving behavior. This thesis introduces a working system of all integrated modules. The system proposed can complete complex scenarios, including situations with buildings and partial trajectories. In thousands of simulations, the system using the LQR controller or the reinforcement learning agent had a success rate of >95 % in steering a truck with one trailer, even with added noise. For the development of autonomous vehicles, the implementation of AI at scale is important. This is why a digital twin of the truck-trailer is used to simulate the full system at a much higher speed than one can collect data in real life.Tesi

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

A novel control architecture based on behavior trees for an omni-directional mobile robot

Robotic systems are increasingly present in dynamic environments. This paper proposes a hierarchical control structure wherein a behavior tree (BT) is used to improve the flexibility and adaptability of an omni-directional mobile robot for point stabilization. Flexibility and adaptability are crucial at each level of the sense–plan–act loop to implement robust and effective robotic solutions in dynamic environments. The proposed BT combines high-level decision making and continuous execution monitoring while applying non-linear model predictive control (NMPC) for the point stabilization of an omni-directional mobile robot. The proposed control architecture can guide the mobile robot to any configuration within the workspace while satisfying state constraints (e.g., obstacle avoidance) and input constraints (e.g., motor limits). The effectiveness of the controller was validated through a set of realistic simulation scenarios and experiments in a real environment, where an industrial omni-directional mobile robot performed a point stabilization task with obstacle avoidance in a workspace.This work was financed by national funds from the FCT (Foundation for Science and Technology), I.P., through IDMEC under LAETA, project UIDB\50022\2020. The work of Rodrigo Bernardo was supported by the PhD Scholarship BD\6841\2020 from the FCT. This work indirectly received funding from the European Union’s Horizon 2020 programme under StandICT.eu 2026 (Grant Agreement No. 101091933).info:eu-repo/semantics/publishedVersio

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Research on Application of Cognitive-Driven Human-Computer Interaction

Human-computer interaction is an important research content of intelligent manufacturing human factor engineering. Natural human-computer interaction conforms to the cognition of users' habits and can efficiently process inaccurate information interaction, thus improving user experience and reducing cognitive load. Through the analysis of the information interaction process, user interaction experience cognition and human-computer interaction principles in the human-computer interaction system, a cognitive-driven human-computer interaction information transmission model is established. Investigate the main interaction modes in the current human-computer interaction system, and discuss its application status, technical requirements and problems. This paper discusses the analysis and evaluation methods of interaction modes in human-computer system from three levels of subjective evaluation, physiological measurement and mathematical method evaluation, so as to promote the understanding of inaccurate information to achieve the effect of interaction self-adaptation and guide the design and optimization of human-computer interaction system. According to the development status of human-computer interaction in intelligent environment, the research hotspots, problems and development trends of human-computer interaction are put forward

A novel robust predictive control system over imperfect networks

This paper aims to study on feedback control for a networked system with both uncertain delays, packet dropouts and disturbances. Here, a so-called robust predictive control (RPC) approach is designed as follows: 1- delays and packet dropouts are accurately detected online by a network problem detector (NPD); 2- a so-called PI-based neural network grey model (PINNGM) is developed in a general form for a capable of forecasting accurately in advance the network problems and the effects of disturbances on the system performance; 3- using the PINNGM outputs, a small adaptive buffer (SAB) is optimally generated on the remote side to deal with the large delays and/or packet dropouts and, therefore, simplify the control design; 4- based on the PINNGM and SAB, an adaptive sampling-based integral state feedback controller (ASISFC) is simply constructed to compensate the small delays and disturbances. Thus, the steady-state control performance is achieved with fast response, high adaptability and robustness. Case studies are finally provided to evaluate the effectiveness of the proposed approach