基于神经网络的室外移动机器人前轮转向模型

针对室外移动机器人的行驶特点,将车体模型划分为前轮转向模型、速度模型和位姿模型三个部分.提出用模糊集合与神经网络相结合来建立车体前轮转向模型的方法.首先将对前轮转向特性影响较大的行车速度模糊化,然后利用神经网络建立各模糊速度下的前轮转向模型,最后由逆模糊化过程求得模型的实际输出.实验结果表明,该方法能较准确地反映车体的前轮转向特性并具有鲁棒性强和易于实现的特点.     

Wheeled inverted pendulum type assistant robot: design concept and mobile control

This paper describes the design concept of the human assistant robot I-PENTAR (Inverted PENdulum Type Assistant Robot) aiming at the coexistence of safety and work capability and its mobile control strategy. I-PENTAR is a humanoid type robot which consists of a body with a waist joint, arms designed for safety, and a wheeled inverted pendulum mobile platform. Although the arms are designed low-power and lightweight for safety, it is able to perform tasks that require high power by utilizing its self-weight, which is the feature of a wheeled inverted pendulum mobile platform. I-PENTAR is modeled as a three dimensional robot; with controls of inclination angle, horizontal position, and steering angle to achieve high mobile capability. The motion equation is derived considering the non-holonomic constraint of the two-wheeled mobile robot, and a state feedback control method is applied for basic mobile controls wherein the control gain is calculated by the LQR method. Through several experiments of balancing, linear running, and steering, it was confirmed that the robot could realize stable mobile motion in a real environment by the proposed controller.     

未知环境中移动机器人实时导航与避障的分层模糊控制

为了解决单模糊控制器的“规则库爆炸”问题,设计了一种分层的模糊控制器,用于指导移动机器人通过未知环境到达指定的目标点．控制器根据8个超声传感器的信息和目标相对于机器人的方位确定机器人的运动．首先,每个超声传感器的信息被输入到危险度模糊控制器（DFC）中,产生关于周围环境中障碍物危险度的模糊向量．这些模糊向量经过融合与归一化处理后分别输入到上层的速度模糊控制器（VFC）和角速度模糊控制器（RFC）的推理机中．VFC根据目标的距离和障碍物的危险度控制机器人的前进速度．RFC根据目标的方向和障碍物的危险度控制机器人的转向,并采用最大隶属度法的反模糊化策略解决“对称不确定”问题．仿真与实验结果证明了所设计的模糊控制器简单而有效．     

Evolutionary Design of Fuzzy Logic Based Position Controller for Mobile Robot

This paper develops a fuzzy logic based position controller whose membership functions are tuned by genetic algorithm. The main goal is to ensure successful velocity and position trajectories tracking between the mobile robot and the virtual reference cart. The proposed fuzzy controller has two inputs and two outputs. The first input represents the distance between the mobile robot and the reference cart. The second input is the angle formed by the straight line defined with the orientation of the robot, and the straight line that connects the robot with the reference cart. The outputs represent linear and angular velocity commands, respectively. The performance of the fuzzy controller is validated through comparison with previously developed mobile robot position controller based on control Lyapunov functions (CLF). Simulation results indicate good performance of position tracking while at the same time a substantial reduction of the control torques is achieved.     

Dynamic Model and Control for a Holonomic Omnidirectional Mobile Robot

In the recent past, mobile robots with high mobility have been developed actively. We have already proposed a holonomic and omnidirectional mobile robot using two active dual-wheel caster assemblies and also derived the kinematic models for the assembly and the mobile robot. This paper presents dynamic analysis and control for the mobile robot. The dynamic model has been derived based on the forces acting on the steering axis. Then a model-based resolved acceleration controller is constructed. The validity of the model and the effectiveness of the control system are confirmed by experiments using a prototype robot as well as simulations.     

Fuzzy sliding mode control for a robot manipulator

This work presents the design of a robust control system using a sliding mode controller that incorporates a fuzzy control scheme. The presented control law superposes a sliding mode controller and a fuzzy logic controller. A fuzzy tuning scheme is employed to improve the performance of the control system. The proposed fuzzy sliding mode control (FSMC) scheme utilizes the complementary cooperation of the traditional sliding mode control (SMC) and the fuzzy logic control (FLC). In other words, the proposed control scheme has the advantages which it can guarantee the stability in the sense of Lyapunov function theory and can ameliorate the tracking errors, compared with the FLC and SMC, respectively. Simulation results for the trajectory tracking control of a two-link robot manipulator are presented to show the feasibility and robustness of the proposed control scheme. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Leader–follower formation control of nonholonomic mobile robots based on a bioinspired neurodynamic based approach

This paper investigates the leader–follower formation control problem for nonholonomic mobile robots based on a bioinspired neurodynamics based approach. The trajectory tracking control for a single nonholonomic mobile robot is extended to the formation control for multiple nonholonomic mobile robots based on the backstepping technique, in which the follower can track its real-time leader by the proposed kinematic controller. An auxiliary angular velocity control law is proposed to guarantee the global asymptotic stability of the followers and to further guarantee the local asymptotic stability of the entire formation. Also a bioinspired neurodynamics based approach is further developed to solve the impractical velocity jumps problem. The rigorous proofs are given by using Lyapunov theory. Simulations are also given to verify the effectiveness of the theoretical results.     

Application of a hybrid controller to a mobile robot

This paper presents the application of a hybrid controller to the optimization of the movement of a mobile robot. Through hybrid controller processes, the optimal angle and velocity of a robot moving in a work space was determined. More effective movement resulted from these hybrid controller processes. The experimental scenarios involved a five-versus-five soccer game and a MATLAB simulation, where the proposed system dynamically assigned the robot to the target position. The hybrid controller was able to choose a better position according to the circumstances encountered. The hybrid controller that is proposed includes a support vector machine and a fuzzy logic controller. We used the method of generalized predictive control to predict the target position, and the support vector machine to determine the optimal angle and velocity required for the mobile robot to reach the goal. First, we used the generalized predictive control to predict the target position. Then, the support vector machine is used to classify the angle that must be followed by the mobile robot to reach the goal. Next, a fuzzy logic controller is designed to determine the velocity of the left and right wheels of the mobile robot. Thus generated, the velocity was optimized according to the measures obtained by the support vector machine. Finally, based on the optimal velocity of robot, the output membership function was modified. Consequently, the proposed hybrid controller allowed the robot to reach the goal quickly and effectively.     

Emergent behaviors of a fuzzy sensory-motor controller evolved bygenetic algorithm

Recently, there has been extensive work on the construction of fuzzy controllers for mobile robots by a genetic algorithm (GA); therefore, we can realize evolutionary optimization as a promising method for developing fuzzy controllers. However, much investigation on the evolutionary fuzzy controller remains because most of the previous works have not seriously attempted to analyze the fuzzy controller obtained by evolution. This paper develops a fuzzy logic controller for a mobile robot with a GA in simulation environments and analyzes the behaviors of the controller with a state transition diagram of the internal model. Experimental results show that appropriate control mechanisms of the fuzzy controller are obtained by evolution. The controller has evolved wen enough to smoothly drive the robot in different environments. The robot produces emergent behaviors by the interaction of several fuzzy rules obtained.     

Autonomous fuzzy parking control of a car-like mobile robot

This paper is devoted to design and implement a car-like mobile robot (CLMR) that possesses autonomous garage-parking and parallel-parking capability by using real-time image processing. For fuzzy garage-parking control (FGPC) and fuzzy parallel-parking control (FPPC), feasible reference trajectories are provided for the fuzzy logic controller to maneuver the steering angle of the CLMR. We propose two FGPC methods and two FPPC methods to back-drive or head-in the CLMR to the garage and the parking lot, respectively. Simulation results illustrate the effectiveness of the developed schemes. The overall experimental setup of the parking system developed in this paper is composed of a host computer, a communication module, a CLMR, and a vision system. Finally, the image-based real-time implementation experiments of the CLMR demonstrate the feasibility and effectiveness of the proposed schemes.     

基于Fuzzy-PID的移动机器人运动控制

移动机器人涉及到许多研究方向,运动控制是其中的基础。通过对移动机器人运动学模型进行分析,以足球机器人系统为实验平台,论证了Fuzzy-PID技术应用于移动机器人运动控制的可行性。将传统的PID控制与模糊控制相结合,通过PID控制实现控制的准确性,利用模糊控制提高控制的快速性。针对移动机器人运动控制中的实际问题,着重提出了基于误差分区的PID控制器和模糊控制器的设计方法。实验证明该方法不仅增强了控制器的调节能力,还在一定程度上简化了控制器的设计。     

A Neuro-interface with fuzzy compensator for controlling nonholonomic mobile robots

This paper describes a control method for mobile robots represented by a nonlinear dynamical system, which is subjected to an output deviation caused by drastically changed disturbances. We here propose some controllers in the framework of neuro-interface. It is assumed that a neural network (NN)-based feedforward controller is construcetd by following the concept of virtual master-slave robot, in which a virtual master robot as a feedforward controller is used to control the slave (i.e., actual) robot. The whole system of the present neuro-interface consists of an NN-based feedforward controller, a feedback PD controller and an adaptive fuzzy feedback compensator. The NN-based feedforward controller is trained offline by using a gradient method, the gains of the PD controller are to be chosen constant, and the adaptive fuzzy compensator is constructed with a simplified fuzzy reasoning. Some simulations are presented to confirm the validity of the present approach, where a nonholonomic mobile robot with two independent driving wheels is assmued to have a disturbance due to the change of mass for the robot.     

Adaptive Polar-Space Motion Control for Embedded Omnidirectional Mobile Robots with Parameter Variations and Uncertainties

This paper presents an adaptive polar-space motion controller for trajectory tracking and stabilization of a three-wheeled, embedded omnidirectional mobile robot with parameter variations and uncertainties caused by friction, slip and payloads. With the derived dynamic model in polar coordinates, an adaptive motion controller is synthesized via the adaptive backstepping approach. This proposed polar-space robust adaptive motion controller was implemented into an embedded processor using a field-programmable gate array (FPGA) chip. Furthermore, the embedded adaptive motion controller works with a reusable user IP (Intellectual Property) core library and an embedded real-time operating system (RTOS) in the same chip to steer the mobile robot to track the desired trajectory by using hardware/software co-design technique and SoPC (system-on-a-programmable-chip) technology. Simulation results are conducted to show the merit of the proposed polar-space control method in comparison with a conventional proportional-integral (PI) feedback controller and a non-adaptive polar-space kinematic controller. Finally, the effectiveness and performance of the proposed embedded adaptive motion controller are exemplified by conducting several experiments on steering an embedded omnidirectional mobile robot.     

Design of a PID optimized neural networks and PD fuzzy logic controllers for a two‐wheeled mobile robot

In this paper, two intelligent techniques for a two‐wheeled differential mobile robot are designed and presented: A smart PID optimized neural networks based controller (SNNPIDC) and a PD fuzzy logic controller (PDFLC). Basically, mobile robots are required to work and navigate under exigent circumstances where the environment is hostile, full of disturbances such as holes and stones. The robot navigation leads to an autonomous decision making to overcome an obstacle and/or to stop the engine to protect it. In fact, the actuators that drive the robot should in no way be damaged and should stop to change direction in case of insurmountable disturbances. In this context, two controllers are implemented and a comparative study is carried out to demonstrate the effectiveness of the proposed approaches. For the first one, neural networks are used to optimize the parameters of a PID controller and for the second a fuzzy inference system type Mamdani based controller is adopted. The goal is to implement control algorithms for safe robot navigation while avoiding damage to the motors. In these two control cases, the smart robot has to quickly perform tasks and adapt to changing environment conditions while ensuring stability and accuracy and must be autonomous with regards to decision making. Simulations results aren't done in real environments, but are obtained with the Matlab/Simulink environment in which holes and stones are modeled by different load torques and are applied as disturbances on the mobile robot environment. These simulation results and the robot performances are satisfactory and are compared to a PID controller in which parameters are tuned by the Ziegler–Nichols tuning method. The applied methods have proven to be highly robust.     

Dynamic modeling and nonlinear tracking control of a novel modified quadrotor

In this article, a nonlinear tracking controller is designed based on Lyapunov stability for a novel aerial robot. The proposed 6‐rotor configuration improves stability and payload lifting capacity of the robot compared with conventional quadrotors while avoiding further complexities in the robot dynamics and steering principles. The dynamical model of the robot is derived using Newton‐Euler method. The model represents a nonlinear, coupled, and underactuated system. The proposed control strategy includes 2 main parts: an attitude controller and a position controller. Both the attitude and position controls are Lyapunov‐based nonlinear tracking controllers that guarantee the asymptotic convergence of the states' tracking errors to zero. Simulation results are presented to illustrate appropriate performance of the closed‐loop system in terms of position/attitude tracking even in the presence of wind disturbance.     

A 3-level autonomous mobile robot navigation system designed by using reasoning/search approaches

This paper describes how soft computing methodologies such as fuzzy logic, genetic algorithms and the Dempster–Shafer theory of evidence can be applied in a mobile robot navigation system. The navigation system that is considered has three navigation subsystems. The lower-level subsystem deals with the control of linear and angular volocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot is at a medium level and is nonlinear. The nonlinear control design is implemented by a backstepping algorithm whose parameters are adjusted by a genetic algorithm. We propose a new extension of the controller mentioned, in order to rapidly decrease the control torques needed to achieve the desired position and orientation of the mobile robot. The high-level subsystem uses fuzzy logic and the Dempster–Shafer evidence theory to design a fusion of sensor data, map building, and path planning tasks. The fuzzy/evidence navigation based on the building of a local map, represented as an occupancy grid, with the time update is proven to be suitable for real-time applications. The path planning algorithm is based on a modified potential field method. In this algorithm, the fuzzy rules for selecting the relevant obstacles for robot motion are introduced. Also, suitable steps are taken to pull the robot out of the local minima. Particular attention is paid to detection of the robot’s trapped state and its avoidance. One of the main issues in this paper is to reduce the complexity of planning algorithms and minimize the cost of the search. The performance of the proposed system is investigated using a dynamic model of a mobile robot. Simulation results show a good quality of position tracking capabilities and obstacle avoidance behavior of the mobile robot.     

Feed-forward multilayer neural network model for vehicle lateral guidance control

An advanced vehicle lateral guidance control technology is necessary in order to develop intelligent transportation and manufacturing systems with flexibility and immediate adaptability. PID control, optimal control, and fuzzy control have often been used for designing a vehicle lateral guidance controller; in addition automatic guidance methods by spline curve and inverse dynamics are also used in mobile robots (e.g. differential drive), but they are not sufficient to develop a highly intelligent vehicle lateral guidance controller which can adapt to varying environments, because they lack some behavior like learning ability and adaptability. In this paper, the possibility to apply neural networks for developing a vehicle lateral guidance controller is exposed. A new neuron activation function suitable for vehicle lateral guidance control is suggested, a feed-forward multilayer neural network (FMNN) with the suggested neuron activation function is proposed and a vehicle lateral guidance controller (VLGC) is developed by use of the FMNN. The VLGC can be applied to automobiles of different parameters and roads of various widths. It can be also applied to mobile robots. Its input variables are proposed to be defined as kind of relative quantities by using the road width, automotive parameter, automotive position, and orientation on the corner course as 90°. Its output variable is the automotive steering angle. Its teaching data are collected by automobile driving simulation, and its connection weights and threshold values are tuned through the error back-propagation algorithm. The training process and the result of neural network by different learning rate coefficients and momentum parameters are compared. Four VLGCs are generated through training by using different learning rate coefficients, momentum parameters, and repeat training times. Automated guided automobile simulations and mobile robot experiments for each VLGC are carried out. Good training result as well as automated guided simulation and experimental results are obtained.     

Experimental study on autonomous mobile robot acquiring optimal action to avoid moving obstacles

The principal aim of this study was to show how an autonomous mobile robot can acquire the optimal action to avoid moving multiobstacles through interaction with the real world. In this paper, we propose a new architecture using hierarchical fuzzy rules, a fuzzy evaluation system, and learning automata. By using our proposed method, the robot autonomously acquires finely tuned behavior which allows it to move to its goal and avoid moving obstacles by using the steering and velocity control inputs simultaneously. We also show experimental results which confirm the feasibility of our method.     

基于模糊控制的超声波避障方法的研究

依据移动机器人上超声波传感器的布置和分组情况,将反映移动机器人当前感知环境的期望类别进行了概括。在此基础上分析了移动机器人的模糊避障原理,并建立了一种基于人的驾驶经验的模糊逻辑控制的路障躲避方法。通过对人的驾驶经验的分析,针对移动机器人建立了路障躲避的模糊规则,并给出了输入与输出变量的隶属度函数。在Simulink中模拟机器人行驶的环境,建立相应的模型对系统进行仿真,结果表明移动机器人能实现自主行驶。     

Mobile robot tracking of pre-planned paths

A method of mobile robot steering control around pre-planned paths is presented. The system can maneuver accurately at low speeds by deriving control parameters as functions of vehicle velocity. The peak demand on the steering controller is reduced, by distributing steering curvature changes evenly over the extent of a maneuver.