Navigation of non-holonomic mobile robot using neuro-fuzzy logic with integrated safe boundary algorithm

In the present work, autonomous mobile robot (AMR) system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neuro-fuzzy inference system (ANFIS) controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.     

Autonomous smart robot for path predicting and finding in maze based on fuzzy and neuro‐Fuzzy approaches

The navigation of autonomous mobile robots has in recent times gained interest from many researchers in different areas such as the industrial, agricultural, and military sectors. This paper aims at carefully investigating two advanced types of approaches for guiding a non‐holonomic mobile robot to navigate in an environment area cluttered with static obstacles. Firstly, a Fuzzy logic controller (FLC) was designed, using trapezoidal shape Membership functions (MF's). Secondly, an Adaptive neuro fuzzy inference system (ANFIS) controller was used to optimize the results obtained from trapezoidal fuzzy controller. To validate the feasibility and effectiveness of the proposed models, V‐REP and MATLAB software are used. A comparative evaluation is, then, done on the basis of speed. The simulations results showed that the mobile robot could navigate successfully into maze environment with both proposed approaches but ANFIS controller provided better results in comparison to fuzzy controller.     

Intelligent Adaptive Mobile Robot Navigation

This paper deals with the application of a neuro-fuzzy inference system to a mobile robot navigation in an unknown, or partially unknown environment. The final aim of the robot is to reach some pre-defined goal. For this purpose, a sort of a co-operation between three main sub-modules is performed. These sub-modules consist in three elementary robot tasks: following a wall, avoiding an obstacle and running towards the goal. Each module acts as a Sugeno–Takagi fuzzy controller where the inputs are the different sensor information and the output corresponds to the orientation of the robot. The rule-base is generated by the controller after some learning process based on a neural architecture close to that used by Wang and Menger. This leads to adaptive neuro-fuzzy inference systems (ANFIS) (one for each module). The adaptive navigation system (ANFIS), based on integrated reactive-cognitive parts, learns and generates the required knowledge for achieving the desired task. However, the generated rule-base suffers from redundancy and abundance of data, most of which are less useful. This makes the assignment of a linguistic label to the associated variable difficult and sometimes counter-intuitive. Consequently, a simplification phase allowing elimination of redundancy is required. For this purpose, an algorithm based on the class of fuzzy c-means algorithm introduced by Bezdek and we have developed an inclusion structure. Experimental results confirm the meaningfulness of the elaborated methodology when dealing with navigation of a mobile robot in unknown, or partially unknown environment.     

Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system

In recent years, the interest in research on robots has increased extensively; mainly due to avoid human to involve in hazardous task, automation of Industries, Defence, Medical and other household applications. Different kinds of robots and different techniques are used for different applications. In the current research proposes the Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for navigation of single as well as multiple mobile robots in highly cluttered environment. In this research it has tried to design a control system which will be able decide its own path in all environmental conditions to reach the target efficiently. Some other requirement for the mobile robot is to perform behaviours like obstacle avoidance, target seeking, speed controlling, knowing the map of the unknown environments, sensing different objects and sensor-based navigation in robot’s environment.     

Fuzzy-logic-assisted interacting multiple model (FLAIMM) for mobile robot localization

Improvement of dead reckoning accuracy is essential for robotic localization systems and has been intensively studied. However, existing solutions cannot provide accurate positioning when a robot suffers from changing dynamics such as wheel slip. In this paper, we propose a fuzzy-logic-assisted interacting multiple model (FLAIMM) framework to detect and compensate for wheel slip. Firstly, two different types of extended Kalman filter (EKF) are designed to consider both no-slip and slip dynamics of mobile robots. Then a fuzzy inference system (FIS) model for slip estimation is constructed using an adaptive neuro-fuzzy inference system (ANFIS). The trained model is utilized along with the two EKFs in the FLAIMM framework. The approach is evaluated using real data sets acquired with a robot driving in an indoor environment. The experimental results show that our approach improves position accuracy and works better in slip detection and compensation compared to the conventional multiple model approach.     

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.     

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.     

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.     

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.     

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．     

A layered goal-oriented fuzzy motion planning strategy for mobile robot navigation.

Most conventional motion planning algorithms that are based on the model of the environment cannot perform well when dealing with the navigation problem for real-world mobile robots where the environment is unknown and can change dynamically. In this paper, a layered goal-oriented motion planning strategy using fuzzy logic is developed for a mobile robot navigating in an unknown environment. The information about the global goal and the long-range sensory data are used by the first layer of the planner to produce an intermediate goal, referred to as the way-point, that gives a favorable direction in terms of seeking the goal within the detected area. The second layer of the planner takes this way-point as a subgoal and, using short-range sensory data, guides the robot to reach the subgoal while avoiding collisions. The resulting path, connecting an initial point to a goal position, is similar to the path produced by the visibility graph motion planning method, but in this approach there is no assumption about the environment. Due to its simplicity and capability for real-time implementation, fuzzy logic has been used for the proposed motion planning strategy. The resulting navigation system is implemented on a real mobile robot, Koala, and tested in various environments. Experimental results are presented which demonstrate the effectiveness of the proposed fuzzy navigation system.     

基于模糊Elman网络算法的移动机器人路径规划分析

为了调正移动机器人避障线路,建立了基于模糊Elman网络算法的移动机器人路径规划模型,并应用进行Matlab仿真分析。利用现有障碍物的距离信息来实现机器人步长的实施可控制与调节,防止移动机器人在做出准确避障行为之后因为没有设定合适的步长而导致撞上障碍物,以0.5作为机器人的最初运动步长。仿真结果表明,采用模糊Elman网络可以获得比其它两种方法更优的路径规划效果,同时对障碍物进行高效避让,由此实现最优的路径规划。采用模糊Elman网络来构建得到的路径规划算法能够满足规划任务的要求,同时还能够根据机器人处于不同工作空间中的情况进行灵活调整。     

Supervised fuzzy reinforcement learning for robot navigation

This paper addresses a new method for combination of supervised learning and reinforcement learning (RL). Applying supervised learning in robot navigation encounters serious challenges such as inconsistent and noisy data, difficulty for gathering training data, and high error in training data. RL capabilities such as training only by one evaluation scalar signal, and high degree of exploration have encouraged researchers to use RL in robot navigation problem. However, RL algorithms are time consuming as well as suffer from high failure rate in the training phase. Here, we propose Supervised Fuzzy Sarsa Learning (SFSL) as a novel idea for utilizing advantages of both supervised and reinforcement learning algorithms. A zero order Takagi–Sugeno fuzzy controller with some candidate actions for each rule is considered as the main module of robot's controller. The aim of training is to find the best action for each fuzzy rule. In the first step, a human supervisor drives an E-puck robot within the environment and the training data are gathered. In the second step as a hard tuning, the training data are used for initializing the value (worth) of each candidate action in the fuzzy rules. Afterwards, the fuzzy Sarsa learning module, as a critic-only based fuzzy reinforcement learner, fine tunes the parameters of conclusion parts of the fuzzy controller online. The proposed algorithm is used for driving E-puck robot in the environment with obstacles. The experiment results show that the proposed approach decreases the learning time and the number of failures; also it improves the quality of the robot's motion in the testing environments.     

Monocular Vision for Mobile Robot Localization and Autonomous Navigation

This paper presents a new real-time localization system for a mobile robot. We show that autonomous navigation is possible in outdoor situation with the use of a single camera and natural landmarks. To do that, we use a three step approach. In a learning step, the robot is manually guided on a path and a video sequence is recorded with a front looking camera. Then a structure from motion algorithm is used to build a 3D map from this learning sequence. Finally in the navigation step, the robot uses this map to compute its localization in real-time and it follows the learning path or a slightly different path if desired. The vision algorithms used for map building and localization are first detailed. Then a large part of the paper is dedicated to the experimental evaluation of the accuracy and robustness of our algorithms based on experimental data collected during two years in various environments.     

Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult то control using conventional techniques. Here, a novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling input displacement of a new adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Since the conventional control strategy is a very challenging task, fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS controller, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.     

在未知环境中基于模糊逻辑的移动机器人行为控制

本文介绍了一种在未知环境中基于模糊逻辑的移动机器人行为控制方法．传统的行为控制方法存在两个弱点：①行为不易描述；②多个行为之间的冲突和竞争难以协调．这篇文章的主要思想是将模糊逻辑控制与行为控制相结合致使这两个问题得到有效的解决．仿真实验结果表明：所提的方法通过多个行为如避障边沿行走和目标导向的融合，能够有效地对机器人在复杂和未知环境中导航．另外，该方法还适用于多传感器的融合与集成．     

路网约束下异构机器人系统路径规划方法

由无人机(Unmanned aerial vehicles, UAV)和地面移动机器人组成的异构机器人系统在协作执行任务时,可以充分发挥两类机器人各自的优势.无人机运动灵活,但通常续航能力有限;地面机器人载荷多,适合作为无人机的着陆平台和移动补给站,但运动受路网约束.本文研究这类异构机器人系统协作路径规划问题.为了降低完成任务的时间代价,提出一种由蚁群算法(Ant colony optimization, ACO)和遗传算法(Genetic algorithm, GA)相结合的两步法对地面机器人和无人机的路线进行解耦,同时规划地面机器人和无人机的路线.第1步使用蚁群算法为地面机器人搜索可行路线.第2步对无人机的最优路径建模,采用遗传算法求解并将无人机路径长度返回至第1步中,用于更新路网的信息素参数,从而实现异构协作系统路径的整体优化.另外,为了进一步降低无人机的飞行时间代价,研究了无人机在其续航能力内连续完成多任务的协作路径规划问题.最后,通过大量仿真实验验证了所提方法的有效性.     

A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots

Autonomous mobile robots navigating in changing and dynamic unstructured environments like the outdoor environments need to cope with large amounts of uncertainties that are inherent of natural environments. The traditional type-1 fuzzy logic controller (FLC) using precise type-1 fuzzy sets cannot fully handle such uncertainties. A type-2 FLC using type-2 fuzzy sets can handle such uncertainties to produce a better performance. In this paper, we present a novel reactive control architecture for autonomous mobile robots that is based on type-2 FLC to implement the basic navigation behaviors and the coordination between these behaviors to produce a type-2 hierarchical FLC. In our experiments, we implemented this type-2 architecture in different types of mobile robots navigating in indoor and outdoor unstructured and challenging environments. The type-2-based control system dealt with the uncertainties facing mobile robots in unstructured environments and resulted in a very good performance that outperformed the type-1-based control system while achieving a significant rule reduction compared to the type-1 system.     

基于多传感器信息融合的移动机器人避障

为了更好地解决移动机器人在未知环境下的自主避障问题,采用多传感器信息融合的方法,通过多个超声传感器对障碍物信息进行采集。合理确立模糊控制器的输入输出,通过模糊推理将障碍物距离信息模糊化,建立模糊规则并解模糊,以达到对移动机器人的安全避障的控制。通过建立移动机器人运动模型,设计了仿真平台,得到实验结果表明:该算法具有良好的可行性。