An immunological approach to mobile robot reactive navigation

In this paper, a reactive immune network (RIN) is proposed and employed for mobile robot navigation within unknown environments. Rather than building a detailed mathematical model of artificial immune systems, this study tries to explore the principle in an immune network focusing on its self-organization, adaptive learning capability, and immune feedback. In addition, an adaptive virtual target method is integrated to solve the local minima problem in navigation. Several trapping situations designed by the early researchers are adopted to evaluate the performance of the proposed architecture. Simulation results show that the mobile robot is capable of avoiding obstacles, escaping traps, and reaching the goal efficiently and effectively.     

Intelligent adaptive immune-based motion planner of a mobile robot in cluttered environment

Learning of an autonomous mobile robot for path generation includes the use of previous experience to obtain the better path within its work space. When the robot is moving in its search space for target seeking, each task requires different form of learning. Therefore, the modeling of an efficient learning mechanism is the hardest problem for an autonomous mobile robot. To solve this problem, the present research work introduced an adaptive learning-based motion planner using artificial immune system, called adaptive immune-based path planner. Later the developed adaptive mechanism has been integrated to the innate immune-based path planner in order to obtain the better results. To verify the effectiveness of the proposed adaptive immune-based motion planner, simulation results as well as experimental results are presented in various unknown environments.     

Tri-tier Immune System in Anti-virus and Software Fault Diagnosis of Mobile Immune Robot Based on Normal Model

In this paper, anti-virus problem and software fault diagnosis of mobile robot, an immune robot, is discussed with proposal of a novel tri-tier immune system (TTIS). TTIS is a novel artificial immune system, which is comprised of three computing tiers and based on the normal model. The three tiers include inherent immune tier, adaptive immune tier and parallel immune tier. The tri-tier immune model is built on some theories of human immune system and has many good features, such as adaptability, immunity, memory, learning, and robustness. At the same time, for such immune robot, a novel normal model for the robot software is also proposed. The normal model is built on the space–time properties of each component in the robot software and can uniquely identify the normal state of the robot software. Such tri-tier immune system based on the normal model is suitable for anti-virus and fault diagnosis, which enable the immune robot to detect all viruses and faults in the robot software, recognize many viruses and faults, eliminate the viruses and faults, and repair the damaged robot software to its normal state. Meanwhile, simulation results show that the tri-tier immune system has the properties of immunity, security and robustness.     

A reinforcement learning with evolutionary state recruitment strategy for autonomous mobile robots control

In recent robotics fields, much attention has been focused on utilizing reinforcement learning (RL) for designing robot controllers, since environments where the robots will be situated in should be unpredictable for human designers in advance. However there exist some difficulties. One of them is well known as ‘curse of dimensionality problem’. Thus, in order to adopt RL for complicated systems, not only ‘adaptability’ but also ‘computational efficiencies’ should be taken into account. The paper proposes an adaptive state recruitment strategy for NGnet-based actor-critic RL. The strategy enables the learning system to rearrange/divide its state space gradually according to the task complexity and the progress of learning. Some simulation results and real robot implementations show the validity of the method.     

Tracking control of a nonholonomic mobile robot using compound cosine function neural networks

The purpose of this paper is to propose a compound cosine function neural network with continuous learning algorithm for the velocity and orientation angle tracking control of a nonholonomic mobile robot with nonlinear disturbances. Herein, two neural network (NN) controllers embedded in the closed-loop control system have the simple continuous learning and rapid convergence capability without the dynamics information of the mobile robot to realize the adaptive control of the mobile robot. The neuron function of the hidden layer in the three-layer feed-forward network structure is on the basis of combining a cosine function with a unipolar sigmoid function. The developed neural network controllers have simple algorithm and fast learning convergence because the weight values are only adjusted between the nodes in hidden layer and the output nodes, while the weight values between the input layer and the hidden layer are one, i.e. constant, without the weight adjustment. Therefore, the main advantages of this control system are the real-time control capability and the robustness by use of the proposed neural network controllers for a nonholonomic mobile robot with nonlinear disturbances. Through simulation experiments applied to the nonholonomic mobile robot with the nonlinear disturbances which are considered as dynamics uncertainty and external disturbances, the simulation results show that the proposed NN control system of nonholonomic mobile robots has real-time control capability, better robustness and higher control precision. The compound cosine function neural network provides us with a new way to solve tracking control problems for mobile robots.     

一种基于免疫原理的自律机器人行为控制算法

生物体免疫系统是一个高度复杂的分布协调自适应系统。文章基于免疫学的细胞克隆选择学说和 Ｊｅｒｎｅ网络调节理论,介绍一种人工免疫系统模型及算法,并应用于自律移动机器人的行为控制研究,模拟实验结果表明,该算法能有效增强自律移动机器人在动态环境中的自适应能力。     

Supervisory Fault Adaptive Control of a Mobile Robot and Its Application in Sensor-Fault Accommodation

A new architecture for fault adaptive control (FAC) of a mobile robot is presented. This architecture is based on the hybrid supervisory control theory. A systematic design procedure for the major components of this architecture is discussed in the context of sensor-fault accommodation by a mobile robot. The FAC architecture is implemented in a mobile robot for laboratory experimentation. Experimental results are provided to demonstrate the feasibility of the proposed FAC architecture     

基于人工情感的拟人机器人控制体系结构

简要概括了当前人工情感的应用,提出一种基于人工情感的拟人机器人控制体系结构,并给出了仿真示例．基于情感的控制结构具有混合分层的特点,情感状态影响到机器人的整个信息处理过程．这种结构不仅体现了机器人的个性化,同时增强了机器人在动态环境中的学习和自适应能力．     

基于大脑情感学习的四轮驱动机器人速度补偿控制

由于4轮驱动机器人的轮间耦合特性及系统非线性的存在,即使单个驱动电机的控制精度达到最优,机器人整体的运动控制效果也未必理想.针对这一问题,提出一种基于大脑情感学习的机器人速度补偿控制方法.基于大脑情感学习计算模型,设计了融合机器人整体速度跟踪误差及其积分、微分信息的补偿控制器,通过计算模型内部各节点权值的在线学习,及时地调整控制器的参数,实现对4个轮子速度的自适应补偿.仿真实验表明,该方法有效减小了非线性干扰对系统的影响,具有较高的稳态控制精度和较快的响应速度,大大提高了机器人整体的速度和轨迹跟踪精度.     

Reactive navigation of underwater mobile robot using ANFIS approach in a manifold manner

Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot’s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system (ANFIS) has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace. An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.     

移动机器人条件反射能力的实现

Brooks的包容体系结构中,移动机器人控制器各个行为之间的关系是固定不变 的,可以看作实现了机器人的非条件反射能力,因此控制系统没有适应性．本文提出了条件 反射能力的实现方法,该方法能够和包容体系结构紧密结合,增强移动机器人的适应能力． 实验证明该方法是可行的．     

Emergent synthesis of motion patterns for locomotion robots

Emergence of stable gaits in locomotion robots is studied in this paper. A classifier system, implementing an instance-based reinforcement-learning scheme, is used for the sensory-motor control of an eight-legged mobile robot and for the synthesis of the robot gaits. The robot does not have a priori knowledge of the environment and its own internal model. It is only assumed that the robot can acquire stable gaits by learning how to reach a goal area. During the learning process the control system is self-organized by reinforcement signals. Reaching the goal area defines a global reward. Forward motion gets a local reward, while stepping back and falling down get a local punishment. As learning progresses, the number of the action rules in the classifier systems is stabilized to a certain level, corresponding to the acquired gait patterns. Feasibility of the proposed self-organized system is tested under simulation and experiment. A minimal simulation model that does not require sophisticated computational schemes is constructed and used in simulations. The simulation data, evolved on the minimal model of the robot, is downloaded to the control system of the real robot. Overall, of 10 simulation data seven are successful in running the real robot.     

Hybrid trigonometric compound function neural networks for tracking control of a nonholonomic mobile robot

The purpose of this paper is to propose a hybrid trigonometric compound function neural network (NN) to improve the NN-based tracking control performance of a nonholonomic mobile robot with nonlinear disturbances. In the mobile robot control system, two NN controllers embedded in the closed-loop control system have the simple continuous learning and rapid convergence capability without the dynamics information of the mobile robot to realize the tracking control of the mobile robot. The neuron functions of the hidden layer in the three-layer feedforward network structure consist of the compound cosine function and the compound sine function combining a cosine or a sine function with a unipolar sigmoid function. The main advantages of this NN-based mobile robot control system are better real-time control capability and control accuracy by use of the proposed NN controllers for a nonholonomic mobile robot with nonlinear disturbances. Through simulation experiments applied to the nonholonomic mobile robot with the nonlinear disturbances of dynamics uncertainty and external disturbances, the simulation results show that the proposed NN control system of a nonholonomic mobile robot has better real-time control capability and control accuracy than the compound cosine function NN control system of a nonholonomic mobile robot and then verify the effectiveness of the proposed hybrid trigonometric compound function NN controller for improving the tracking control performance of a nonholonomic mobile robot with nonlinear disturbances.     

Prune-Able Fuzzy ART Neural Architecture for Robot Map Learning and Navigation in Dynamic Environments

Mobile robots must be able to build their own maps to navigate in unknown worlds. Expanding a previously proposed method based on the fuzzy ART neural architecture (FARTNA), this paper introduces a new online method for learning maps of unknown dynamic worlds. For this purpose the new Prune-able fuzzy adaptive resonance theory neural architecture (PAFARTNA) is introduced. It extends the FARTNA self-organizing neural network with novel mechanisms that provide important dynamic adaptation capabilities. Relevant PAFARTNA properties are formulated and demonstrated. A method is proposed for the perception of object removals, and then integrated with PAFARTNA. The proposed methods are integrated into a navigation architecture. With the new navigation architecture the mobile robot is able to navigate in changing worlds, and a degree of optimality is maintained, associated to a shortest path planning approach implemented in real-time over the underlying global world model. Experimental results obtained with a Nomad 200 robot are presented demonstrating the feasibility and effectiveness of the proposed methods.     

未知环境中移动机器人柔性行为决策的调节发育学习

未知环境中移动机器人柔性的行为决策是完成各种任务的前提.目前的机器人行为决策方法在面对动态变化的环境时柔性较差,机器人难以获得持续稳定的学习能力.本文作者曾尝试通过集成小脑监督学习和基底神经节的强化学习来实现移动机器人动态环境下的柔性行为决策,但所提算法适应动态环境的能力有限.在前期工作基础上,本文设计了更有生物学意义的好奇度指标代替原来的警觉度指标,通过模拟蓝斑活动在基音模式和阶段模式之间的动态切换,实现移动机器人环境探索–利用的动态自适应调节.同时,设计随外部环境变化的自适应调节因子,实现移动机器人动态环境中基于小脑监督学习和基底神经节强化学习的柔性行为决策,使机器人可以获得持续稳定的学习能力.动态环境和实际环境中的实验结果验证了本文所提算法的有效性.     

Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot

An adaptive controller of nonlinear PID-based analog neural networks is developed for the velocity- and orientation-tracking control of a nonholonomic mobile robot. A superb mixture of a conventional PID controller and a neural network, which has powerful capability of continuously online learning, adaptation and tackling nonlinearity, brings us the novel nonlinear PID-based analog neural network controller. It is appropriate for a kind of plant with nonlinearity uncertainties and disturbances. Computer simulation for a differentially driven nonholonomic mobile robot is carried out in the velocity- and orientation-tracking control of the nonholonomic mobile robot. The effectiveness of the proposed control algorithm is demonstrated through the simulation experiment, which shows its superior performance and disturbance rejection.     

Dyna-Q-based vector direction for path planning problem of autonomous mobile robots in unknown environments

Reinforcement learning (RL) is a popular method for solving the path planning problem of autonomous mobile robots in unknown environments. However, the primary difficulty faced by learning robots using the RL method is that they learn too slowly in obstacle-dense environments. To more efficiently solve the path planning problem of autonomous mobile robots in such environments, this paper presents a novel approach in which the robot’s learning process is divided into two phases. The first one is to accelerate the learning process for obtaining an optimal policy by developing the well-known Dyna-Q algorithm that trains the robot in learning actions for avoiding obstacles when following the vector direction. In this phase, the robot’s position is represented as a uniform grid. At each time step, the robot performs an action to move to one of its eight adjacent cells, so the path obtained from the optimal policy may be longer than the true shortest path. The second one is to train the robot in learning a collision-free smooth path for decreasing the number of the heading changes of the robot. The simulation results show that the proposed approach is efficient for the path planning problem of autonomous mobile robots in unknown environments with dense obstacles.     

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.     

Learning control of mobile robots using a multiprocessor system

A real-time multiprocessor system is proposed for the solution of the tracking problem of mobile robots operating in a real context with environmental disturbances and parameter uncertainties. The proposed control scheme utilizes multiple models of the robot for its identification in an adaptive and learning control framework. Radial Basis Function Networks (RBFNs) are considered for the multiple models in order to exploit the net non-linear approximation capabilities for modeling the kinematic behavior of the vehicle and for reducing unmodeled contributions to tracking errors. The training of the nets and the tests of the achieved control performance have been done in a real experimental setup. The proposed control architecture improves the robot tracking performance achieving fast and accurate control actions in presence of large and time-varying uncertainties in dynamical environments. The experimental results are satisfactory in terms of tracking errors and computational efforts.