This paper addresses a new method for navigation in dynamic environment. The proposed method is based on force field method and it is supposed that the robot performs SLAM and autonomous navigation in dynamic environm...
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This paper addresses a new method for navigation in dynamic environment. The proposed method is based on force field method and it is supposed that the robot performs SLAM and autonomous navigation in dynamic environment without any predefined information about dynamic obstacles. The movement of dynamic obstacles is predicted by Kalman filter and is used for collision detection purpose. In the time of collision detection, a modifying force is added to repulsive and attractive forces corresponding to the static environment and leads robot to avoid collision. Moreover, a safe turning angle is defined to assure safe navigation of the robot. The performance of proposed method, named Escaping Algorithm, is verified through different simulation and experimental tests. The results show the proper performance of Escaping Algorithm in term of dynamic obstacle avoidance as a practical method for autonomous navigation.
In this paper,we proposed a hybrid method in extracting the attitude parameters of unmanned aerial vehicle(UAV), which is based on computer vision and improved artificial bee colony(ABC) *** is used as a characteristi...
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ISBN:
(纸本)9781479900305
In this paper,we proposed a hybrid method in extracting the attitude parameters of unmanned aerial vehicle(UAV), which is based on computer vision and improved artificial bee colony(ABC) *** is used as a characteristic line, whose two parameters,Φandσare utilized to obtain UAV's parameters:roll angleΨand pitch angleθ.Defogging Algorithm is used to make the original pictures clearer and gain the transmission image for horizon *** UAV could obtain the horizon through improved ABC *** also analyzed the relationship between line parameters and UAV parameters and calculated the UAV *** results verified the feasibility and effectiveness of our presented approach.
Visual Servoing is generally comprised of feature tracking and control. According to the literature, no attempt has already been made to optimize these two parts together. In kernel based visual servoing method, the m...
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Visual Servoing is generally comprised of feature tracking and control. According to the literature, no attempt has already been made to optimize these two parts together. In kernel based visual servoing method, the main objective is to combine and optimize the entire control loop. By kernel definition, a Lyapanov candidate function is formed and the control input is computed so that the Lyapanov stability can be verified. This is performed in four degrees of freedom. In the present study, previous kernel algorithm from the recorded literature has been implemented. We have used the KBVS for our purpose such that an object without any marker is tracked. This method is chosen because of its robustness, speed and featureless properties. Furthermore, in order to show the visual tracking performance, all four degrees of freedom have been synthesized. Experimental results verifies the effectiveness of this method implemented for four degrees of freedom movements.
This paper presents a nonlinear controller for visual servoing system. Pose estimation is one of the fundamental issues in position-based visual servoing (PBVS) approach. A few researches have focused on controller sy...
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This paper presents a nonlinear controller for visual servoing system. Pose estimation is one of the fundamental issues in position-based visual servoing (PBVS) approach. A few researches have focused on controller synthesis under modeling uncertainty and measurement noise of estimated position. In this research, PD-type sliding surface is designed for tracking target. The control signal is obtained from the sliding surface and the stability of the algorithm is verified by Lyapunov theory. Moreover, a recent designed robust estimator based on unscented Kalman observer (UKO) cascading with Kalman filter (KF) is used to estimate the pose, velocity and acceleration of the target. The combination of the implemented estimator and the proposed controller provide a stable and robust structure in PBVS. The reported experimental results, verify the effectiveness of the proposed method in an industrial visual servoing system.
The problem of unsupervised classification of 3D objects from depth information is investigated in this paper. The range images are represented efficiently as sensor observations. Considering the high-dimensionality o...
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The problem of unsupervised classification of 3D objects from depth information is investigated in this paper. The range images are represented efficiently as sensor observations. Considering the high-dimensionality of 3D object classification, little attention has been paid to the curse of dimensionality in the existing state-of-the-art algorithms. In order to remedy this problem, a low-dimensional representation is defined here. The sparse model of every range image is constructed from a parametric dictionary. Employing the algorithmic information theory, a universal normalized metric is used for comparison of Kolmogorov complexity based representations of sparse models. Finally, most similar objects are grouped together. Experimental results show efficiency and accuracy of the proposed method in comparison to a recently proposed method.
In this paper robust PID control of fully-constrained cable-driven robots with elastic cables is studied in detail. To develop the idea, a robust PID control for cable-driven robots with ideal rigid cables is firstly ...
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In this paper robust PID control of fully-constrained cable-driven robots with elastic cables is studied in detail. To develop the idea, a robust PID control for cable-driven robots with ideal rigid cables is firstly designed and then, this controller is extended for the robots with elastic cables. To overcome vibrations caused by inevitable elasticity of cables, a composite control law is proposed based on singular perturbation theory. The proposed control algorithm includes robust PID control for corresponding rigid model and a corrective term. Using the proposed control algorithm the dynamics of the cable-driven robot is divided into slow and fast subsystems. Then, based on the results of singular perturbation theory, stability analysis of the total system is performed. Finally, the effectiveness of the proposed control law is investigated through several simulations on a planar cable-driven robot.
In this paper design and control of planar cable-driven parallel robots are studied in an experimental prospective. Since in this class of manipulators, cable tensionability conditions must be met, feedback control of...
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In this paper design and control of planar cable-driven parallel robots are studied in an experimental prospective. Since in this class of manipulators, cable tensionability conditions must be met, feedback control of such robots becomes more challenging than for conventional robots. To meet these conditions, internal force control structure is introduced and used in addition to a PID control scheme to ensure that all cables remain in tension. A robust PID controller is proposed for partial knowledge of the robot, to keep the tracking errors bounded. Finally, the effectiveness of the proposed control algorithm is examined through experiments on K.N. Toosi planar cable-driven robot and it is shown that the proposed control structure is able to provide suitable performance in practice.
The DC motor speed control is commonly used in several robotic applications. Thus the controller design for the DC motor speed control is a compulsory to optimize the performance of the motor towards the system design...
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作者:
Marko BunicStjepan BogdanUniversity of Zagreb
Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering LARICS–Laboratory for Robotics and Intelligent Control Systems Zagreb Croatia
This paper presents the extension of the previously proposed method for multi-agent formation control based on potential function. The method derived for 2D space is extended to 3D. It has been shown that the control ...
This paper presents the extension of the previously proposed method for multi-agent formation control based on potential function. The method derived for 2D space is extended to 3D. It has been shown that the control algorithm keeps all the properties of the original scheme in case of multi-agent formation moving in 3D. An adaptation mechanism that assures avoidance of unwanted stable equilibria, used in 2D, is implemented in the same form for 3D formations. The obtained simulation results demonstrate stable behavior of the system for various sets of parameters - the desired 3D formation is reached in finite time and maintained during trajectory execution.
This paper focuses on evaluating the robustness and knowledge generalization properties of a model-free learning mechanism, applied for the kinematic control of robot manipulation chains based on a nested-hierarchical...
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ISBN:
(纸本)9781457711992
This paper focuses on evaluating the robustness and knowledge generalization properties of a model-free learning mechanism, applied for the kinematic control of robot manipulation chains based on a nested-hierarchical multi-agent architecture. In the proposed topology, the agents correspond to independent degrees-of-freedom (DOF) of the system, managing to gain experience over the task that they collaboratively perform by continuously exploring and exploiting their state-to-action mapping space. Each agent forms a local (partial) view of the global system state and task progress, through a recursive learning process. By organizing the agents in a nested topology, the goal is to facilitate modular scaling to more complex kine-matic topologies, with loose control coupling among the agents. Reinforcement learning is applied within each agent, to evolve a local state-to-action mapping in a continuous domain, thus leading to a system that exhibits developmental properties. This work addresses problem settings in the domain of kinematic control of dexterous-redundant robot manipulation systems. The numerical experiments performed consider the case of a single-linkage open kinematic chain, presenting kinematic redundancies given the desired task-goal. The focal issue in these experiments is to assess the capacity of the proposed multi-agent system to progressively and autonomously acquire cooperative sensorimotor skills through a self-learning process, that is, without the use of any explicit model-based planning strategy. In this paper, generalization and robustness properties of the overall multi-agent system are explored. Furthermore, the proposed framework is evaluated in constrained motion tasks, both in static and non-static environments. The computational cost of the proposed multi-agent architecture is also assessed.
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