Reactive navigation in dynamic environment using a multisensorpredictor

A reactive navigation system for an autonomous mobile robot in unstructured dynamic environments is presented. The motion of moving obstacles is estimated for robot motion planning and obstacle avoidance. A multisensor-based obstacle predictor is utilized to obtain obstacle-motion information. Sensory data from a CCD camera and multiple ultrasonic range finders are combined to predict obstacle positions at the next sampling instant. A neural network, which is trained off-line, provides the desired prediction on-line in real time. The predicted obstacle configuration is employed by the proposed virtual force based navigation method to prevent collision with moving obstacles. Simulation results are presented to verify the effectiveness of the proposed navigation system in an environment with multiple mobile robots or moving objects. This system was implemented and tested on an experimental mobile robot at our laboratory. Navigation results in real environment are presented and analyzed.     

Reactive navigation through multiscroll systems: from theory to real-time implementation

In this paper a new reactive layer for multi-sensory integration applied to robot navigation is proposed. The new robot navigation technique exploits the use of a chaotic system able to be controlled in real-time towards less complex orbits, like periodic orbits or equilibrium points, considered as perceptive orbits. These are subject to real-time modifications on the basis of environment changes acquired through a distributed sensory system. The strategy is inspired to the olfactory bulb neural activity observed in rabbits subject to external stimuli. The mathematical details of the approach are given including simulation results in a virtual environment. Furthermore the proposed strategy has been tested on an experimental environment consisting of an FPGA-based hardware driving an autonomous roving robot. The obtained results demonstrate the capability to perform a real-time navigation control.     

双臂搬运机器人反应式导航控制系统设计

为增强双臂搬运机器人在作业任务过程中的行进避障能力,使其运动行为得到连续有效控制,设计双臂搬运机器人的反应式导航控制系统。根据单片机与电机电路的连接形式,选择合适的ARM微处理器元件与PIC单片机结构,再联合HN-9移动平台、智能导航平台、ROS操作平台,完善反应式导航子模块的运行能力,实现控制系统的硬件单元设计。求取绝对位姿向量、相对位姿向量的计算结果,以此作为自变量系数,确定速度雅可比指标,并推断得出动力学递推表达式,完成对双臂搬运机器人的协调控制,联合相关硬件应用结构,实现双臂搬运机器人反应式导航控制系统的设计。对比实验结果：反应式导航控制系统可使机器人准确躲避行进障碍物,且躲避过程中机器人完成作业任务的能力不会受到影响,符合连续有效控制机器人搬运行为的实际应用需求。     

Adaptive behavior navigation of a mobile robot

Describes a neural network model for the reactive behavioral navigation of a mobile robot. From the information received through the sensors the robot can elicit one of several behaviors (e.g., stop, avoid, stroll, wall following), through a competitive neural network. The robot is able to develop a control strategy depending on sensor information and learning operation. Reinforcement learning improves the navigation of the robot by adapting the eligibility of the behaviors and determining the linear and angular robot velocities     

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.     

Fuzzy logic techniques for mobile robot obstacle avoidance

This paper is concerned with the problem of reactive navigation for a mobile robot in an unknown clustered environment. We will define reactive navigation as a mapping between sensory data and commands. Building a reactive navigation system means providing such a mapping. It can come from a family of predefined functions (like potential fields methods) or it can be built using ‘universal’ approximators (like neural networks). In this paper, we will consider another ‘universal’ approximator: fuzzy logic. We will explain how to choose the rules using a behaviour decomposition approach. It is possible to build a controller working quite well but the classical problems are still there: oscillations and local minima. Finally, we will conclude that learning is necessary for a robust navigation system and fuzzy logic is an easy way to put some initial knowledge in the system to avoid learning from zero.     

Model-based learning for mobile robot navigation from the dynamicalsystems perspective

This paper discusses how a behavior-based robot can construct a "symbolic process" that accounts for its deliberative thinking processes using models of the environment. The paper focuses on two essential problems; one is the symbol grounding problem and the other is how the internal symbolic processes can be situated with respect to the behavioral contexts. We investigate these problems by applying a dynamical system's approach to the robot navigation learning problem. Our formulation, based on a forward modeling scheme using recurrent neural learning, shows that the robot is capable of learning grammatical structure hidden in the geometry of the workspace from the local sensory inputs through its navigational experiences. Furthermore, the robot is capable of generating diverse action plans to reach an arbitrary goal using the acquired forward model which incorporates chaotic dynamics. The essential claim is that the internal symbolic process, being embedded in the attractor, is grounded since it is self-organized solely through interaction with the physical world. It is also shown that structural stability arises in the interaction between the neural dynamics and the environmental dynamics, which accounts for the situatedness of the internal symbolic process, The experimental results using a mobile robot, equipped with a local sensor consisting of a laser range finder, verify our claims.     

Sonar-based mobile robot navigation through supervised learning on a neural net

For mobile robot navigation in an unknown and changing environment, a reactive approach is both simple to implement and fast in response. A neural net can be trained to exhibit such a behaviour. The advantage is that, it relates the desired motion directly to the sensor inputs, obviating the need of modeling and planning. In this work, a feedforward neural net is trained to output reactive motion in response to ultrasonic range inputs, with data generated artificially on the computer screen. We develop input and output representations appropriate to this problem.A purely reactive robot, being totally insensitive to context, often gets trapped in oscillations in front of a wide object. To overcome this problem, we introduce a notion of memory into the net by including context units at the input layer. We describe the mode of training for such a net and present simulated runs of a point robot under the guidance of the trained net in various situations. We also train a neural net for the navigation of a mobile robot with a finite turning radius. The results of the numerous test runs of the mobile robot under the control of the trained neural net in simulation as well as in experiments carried out in the laboratory, are reported in this paper.     

Model-driven vision for in-door navigation

For navigation in a partially known environment it is possible to provide a model that may be used for guidance in the navigation and as a basis for selective sensing. In this paper a navigation system for an autonomous mobile robot is presented. Both navigation and sensing is built around a graphics model, which enables prediction of the expected scene content. The model is used directly for prediction of line segments which, through matching, allow estimation of position and orientation. In addition, the model is used as a basis for a hierarchical stereo matching that enables dynamic updating of the model with unmodelled objects in the environment. For short-term path planning a set of reactive behaviours is used. The reactive behaviours include use of inverse perspective mapping for generation of occupancy grids, a sonar system and simple gaze holding for monitoring of dynamic obstacles. The full system and its component processes are described and initial experiments with the system are briefly outlined.     

基于模糊神经网络的六足机器人自主导航闭环控制系统设计

针对未知环境中六足机器人的自主导航问题,设计了一种基于模糊神经网络的自主导航闭环控制算法,并依据该算法设计了六足机器人的导航控制系统．算法融合了模糊控制的逻辑推理能力与神经网络的学习训练能力,并引入闭环控制方法对算法进行优化．所设计的控制系统由信息输入、模糊神经网络、指令执行以及信息反馈4个模块组成．环境及位置信息的感知由GPS（全球定位系统）传感器、电子罗盘传感器和超声波传感器共同完成．采用C语言重建模糊神经网络控制算法,并应用于该系统．通过仿真实验,从理论上论证了基于模糊神经网络的闭环控制算法性能优于开环控制算法,闭环控制算法能够减小六足机器人在遇到障碍物时所绕行的距离,行进速度提高了6.14%,行进时间缩短了8.74%．在此基础上,开展了实物试验．试验结果表明,该控制系统能够实现六足机器人自主导航避障控制功能,相对于开环控制系统,能有效地缩短行进路径,行进速度提高了5.66%,行进时间缩短了7.25%,验证了闭环控制系统的可行性和实用性．     

基于模糊神经网络的强化学习及其在机器人导航中的应用

研究基于行为的移动机器人控制方法.将模糊神经网络与强化学习理论相结合,构成模糊强化系统.它既可获取模糊规则的结论部分和模糊隶属度函数参数,也可解决连续状态空间和动作空间的强化学习问题.将残差算法用于神经网络的学习,保证了函数逼近的快速性和收敛性.将该系统的学习结果作为反应式自主机器人的行为控制器,有效地解决了复杂环境中的机器人导航问题.     

Learning action models for the improved execution of navigation plans

Most state-of-the-art navigation systems for autonomous service robots decompose navigation into global navigation planning and local reactive navigation. While the methods for navigation planning and local navigation themselves are well understood, the plan execution problem, the problem of how to generate and parameterize local navigation tasks from a given navigation plan is largely unsolved.

This paper describes how a robot can autonomously learn to execute navigation plans. We formalize the problem as a Markov Decision Process (MDP) and derive a decision theoretic action selection function from it. The action selection function employs models of the robot’s navigation actions, which are autonomously acquired from experience using neural networks or regression tree learning algorithms. We show, both in simulation and on an RWI B21 mobile robot, that the learned models together with the derived action selection function achieve competent navigation behavior.     

一种混合结构的移动机器人导航控制策略

研究了未知环境下移动机器人实时的导航控制问题.采用分布式系统将反射式行为、反应式行为与慎思规划相结合，设计了移动机器人导航控制策略.根据激光雷达传感器信息设计了基于栅格的实时避障算法和解锁策略.通过慎思规划解决了复杂环境下的局部势能陷阱问题.通过自行研制的移动机器人IMR01的实验验证了导航策略的有效性.     

Mobile robot navigation using motor schema and fuzzy context dependent behavior modulation

This paper presents a novel technique to autonomously select different motor schemas using fuzzy context dependant blending of robot behaviors for navigation. First, a set of motor schemas is formed as behaviors. Both strategic and reactive type schemas have been employed in order to facilitate both the aspects of global and local motion planning. While strategic schemas are formed using the prior knowledge of the environment, the reactive schemas are activated using current sensory data of the robot. For global path planning, a safe path is first created using a Voronoi diagram. For local planning, the Voronoi vertices are treated as immediate subgoals and are used to form schemas leading to achieve optimized traveled distance and goal oriented robot navigation. Two motor schemas are formed as reactive behaviors for obstacle avoidance. The unknown obstacles are modeled using the sensory data. The coordinated behavior is achieved while employing weighed vector summation of the schemas. The adaptation of weights are achieved through a fuzzy inference system where fuzzy rules are used to dynamically generate the weights during navigation. A novel approach is proposed for fuzzy context-dependent blending of schemas. Fuzzy rules are formed using two main criteria into account: the first criterion reasons out the context dependent activity of a schema for achieving goal and the second criterion reasons out cooperative activity of strategic schemas with high priority reactive schemas. Comprehensive results validate that the proposed technique eliminates the existing drawbacks of motor schema approaches available in literature and provides collision free goal oriented robot navigation.     

INTELLIGENT SENSOR FUSION AND LEARNING FOR AUTONOMOUS ROBOT NAVIGATION

ABSTRACT

This paper presents the design and implementation of an autonomous robot navigation system for intelligent target collection in dynamic environments. A feature-based multi-stage fuzzy logic (MSFL) sensor fusion system is developed for target recognition, which is capable of mapping noisy sensor inputs into reliable decisions. The robot exploration and path planning are based on a grid map oriented reinforcement path learning system (GMRPL), which allows for long-term predictions and path adaptation via dynamic interactions with physical environments. In our implementation, the MSFL and GMRPL are integrated into subsumption architecture for intelligent target-collecting applications. The subsumption architecture is a layered reactive agent structure that enables the robot to implement higher-layer functions including path learning and target recognition regardless of lower-layer functions such as obstacle detection and avoidance. The real-world application using a Khepera robot shows the robustness and flexibility of the developed system in dealing with robotic behaviors such as target collecting in the ever-changing physical environment.     

记忆神经网络在机器人导航领域的应用与研究进展

记忆神经网络非常适合解决时间序列决策问题,将其用于机器人导航领域是非常有前景的新兴研究领域。本文主要讨论记忆神经网络在机器人导航领域的研究进展。给出几种基本记忆神经网络结合导航任务的工作机理,总结了不同模型的优缺点;对记忆神经网络在导航领域的研究进展进行简要综述;进一步介绍导航验证环境的发展;最后梳理了记忆神经网络在导航问题所面临的复杂性挑战,并预测了记忆神经网络在导航领域未来的发展方向。     

Guide robot intelligent navigation in urban environments

This paper presents an intelligent robotic system to guide visually impaired people in urban environments. The robot is equipped with two laser range finders, global positioning system (GPS), camera, and compass sensors. All the sensors data are processed by a single laptop computer. We have implemented different navigation algorithms enabling the robot to move autonomously in different urban environments. In pedestrian walkways, we utilize the distance to the edge (left, right, or both) to determine the robot steering command. In difference from pedestrian walkways, in open squares where there is no edge information, artificial neural networks map the GPS and compass sensor data to robot steering command guiding the visually impaired to the goal location. The neural controller is designed such as to be employed even in environments different from those in which they have been evolved. Another important advantage is that a single neural network controls the robot to reach multiple goal locations inside the open square. The proposed algorithms are verified experimentally in a navigation task inside the University of Toyama Campus, where the robot moves from the initial to goal location.     

新型仿生六足机器人导航系统的建模与仿真

针对新型仿生六足机器人工作任务和作业环境的要求,设计了一种基于INS-GPS器件的专用组合式导航系统。该导航系统采用集中开环式组合方式,以INS和GPS器件输出的导航数据差作为滤波器的输入值,运用经典卡尔曼滤波理论对该导航系统进行了实时修正,并根据仿生六足机器人运动特性和测量任务的要求,建立了该导航系统的位置、速度组合测量方程,并运用MATLAB软件进行了仿真,仿真结果表明:采用该组合式导航系统可大大提高仿生六足机器人的导航精度,为仿生六足机器人实现智能化、实时化控制奠定了基础。     

Self-supervised learning for docking and target reaching

A robotic system using simple visual processing and controlled by neural networks is described. The robot performs docking and target reaching without prior geometric calibration of its components. All effects of control signals on the robot are learned by the controller through visual observation during a training period, and refined during actual operation. Minor changes in the system's configuration result in a brief period of degraded performance while the controller adapts to the new mappings.

It is shown that a neural network-based controller can perform rapidly and accurately, taking into account the non-linearities of various mapping functions. Such a controller is easy to train, tolerant of imprecise equipment configurations, and insensitive to camera perturbations following training. This method features real-time adaptivity to changes in mappings, and is simpler than traditional control techniques, which require the solution of the inverse perspective projection and inverse kinematics of the system.

Various operations including approaching, centering, paralleling, reaching and adjusting are performed by the robot as it navigates towards the target. The robot attempts to grasp targets that are sufficiently close, or approach them while avoiding collisions with obstacles.     

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.