自主微小型移动机器人的协作学习

自主微小型移动机器人的协作学习研究是多智能体机器人系统理论的主要研究方向。因为单个微小型移动机器人能力有限，所以机器人之间的协作在某些重要的基础工业和生物医学领域方面显得非常重要。该文介绍了几种用于协作学习的方法并且比较了它们之间的优点和缺点。最后，简要介绍了一些研究工作。     

模块化可重构履带式微小型机器人的研究

研制了一种模块化可重构履带式微小型机器人．单个微小型机器人可以独立运行,多个微小型机器人可以重构成链形机器人和环形机器人．微小型机器人结构紧凑、体积小、重量轻．采用了微控制器和PC机两级控制体系,两级间采用蓝牙通讯．实验结果验证链形机器人具有较强的越障能力,能爬越楼梯;环形机器人具有高速及路面适应能力强的特点．     

未知环境中小型机器人路径规划策略的研究

针对微小型机器人在进行路径规划时存在系统硬件资源有限,数据处理能力盖及系统感知能力有限,只能获取局部信息,且信息不完备的问题,分析了人在未知环境中路径规划策略,提出了一种微小型机器人的路径规划策略。实验结果表明,该策略可以满足微小型机器人在复杂未知环境中路径规划的要求,为微小型自主机器人的设计提供了新的方法。     

自主式微小型移动机器人的自动避障行为研究

针对多微小型移动机器人工作环境的模型未知或不确定,以及该机器人本身 的某些限制,采用基于行为的研究方法,实现了自行设计的自主式微小型移动机器人在未知 、动态环境中的自动避障,设计了该机器人的障碍物回避行为,采用了电机神经元网络选择 机器人的自动避障动作,并用增强式学习的动作评判结果在线修改网络的权值,结合机器人 的漫步行为,采用机器人的安全漫步任务验证了该方法的有效性．     

微小型自主水下机器人研究现状

综述了国内外微小型自主水下机器人（AUV）的研究现状。首先，对微小型AUV的定义进行了梳理。其次，基于微小型AUV的平台结构特征，对微小型AUV的应用背景和特征进行了归纳和总结。最后，对微小型AUV的发展趋势进行了分析和探讨。为微小型AUV的设计提供了参考，对微小型AUV的应用发展具有借鉴意义。     

基于相对定位的多移动微机器人协作定位方法

为了提高微机器人定位的精确性,研究了基于相对定位的多移动微机器人协作定位方法.结合微机器人尺寸小的特点,定位系统中采用低功耗的红外传感器作为微机器人的测距传感器,利用粒子滤波实现微机器人的自定位.在自定位及相对定位的基础上,提出了多微机器人的协作定位算法,通过保留一定数目的粒子在自身粒子集合中,而非交换全部的粒子的策略,保证了一定的定位精度.仿真与实验结果验证了协作定位算法的有效性.     

微小型自主侦察机器人控制系统设计

狭窄区域侦察和大面积探测在军用和民用领域都有着较大的应用需求.这使得微小型自主机器人成为了研究热点之一.然而,体积和重量的限制导致微小型机器人运算能力、通信能力和传感能力十分有限.因此.微小型自主侦察机器人控制系统的设计成为难题.在微小型自主侦察机器人BMS-1中.通过配置不同传感器和采用适当的控制算法.实现了避障和寻找并躲避在隐蔽处等基本自主行为,最后进行了实验测试和得出了相关结论,提出了本文的创新所在.     

一种微小型嵌入式图像处理平台的研究

微小型自重构移动机器人自主对接方法的研究是一个较新的方向,针对微小型自重构机器人系统的要求,研究了一种基于DSP ADSP-BF533和CMOS数字摄像头VS6524的微小型嵌入式图像采集和处理平台,用于机器人对接引导;并在实验中实现了对自重构微小型机器人对接部件特征的识别,验证了平台的可靠性。     

基于协作增强式学习的自主式微直升机控制

自主式微直升机为了获得完成任务的行为，可以通过在任务环境中飞行来学习控制规则，由于增强式学习不需要精确的环境模型，而是采用逐次逼近的机理，所以微直升机需要很长的计算开销，难以满足实时性要求，另外由于微直升机尺寸的限制，不能安装功能很强的传感器来获得完全的环境信息，所以微直升机必须通过其他智能体协作来获得环境信息，本文利用高档台式机强大的计算和扩展功能，将其作为一个协作智能体与微直升机协作来完成增强式学习，仿真实验结果和理论分析证明这种方法的有效性，最后，给出我们今后的研究重点。     

基于蚯蚓原理的多节蠕动机器人

介绍了多节蠕动机器人的机体构造和运动原理，建立了机器人运动模型并进行了分析. 阐述了该机器人系统的控制组成和软件设计. 讨论了机器人在不同倾角橡胶管道内的驱动性能试验.进行了机器人温度试验及转弯性能试验.结果表明：该微小型机器人运行可靠、平稳，控制方便，有一定的爬坡能力；连续工作时机器人温度不超过35℃；可通过大于36mm的弯曲半径.该研究为非结构环境狭小空间及人体消化道探察机器人的研制奠定了基础．     

深度学习在智能机器人中的应用研究综述

机器人发展的趋势是人工智能化,深度学习是智能机器人的前沿技术,也是机器学习领域的新课题。深度学习技术被广泛运用于农业、工业、军事、航空等领域,与机器人的有机结合能设计出具有高工作效率、高实时性、高精确度的智能机器人。为了增强智能机器人在各方面的能力,使其更智能化,介绍了深度学习与机器人有关的研究项目与深度学习在机器人中的各种应用,包括室内和室外的场景识别、机器人的工业服务和家庭服务以及多机器人协作等。最后,对深度学习在智能机器人中应用的未来发展、可能面临的机遇和挑战等进行了讨论。     

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.     

Hybrid autonomous control for multi mobile robots

Reinforcement learning can be an adaptive and flexible control method for autonomous system. It does not need a priori knowledge; behaviors to accomplish given tasks are obtained automatically by repeating trial and error. However, with increasing complexity of the system, the learning costs are increased exponentially. Thus, application to complex systems, like a many redundant d.o.f. robot and multi-agent system, is very difficult. In the previous works in this field, applications were restricted to simple robots and small multi-agent systems, and because of restricted functions of the simple systems that have less redundancy, effectiveness of reinforcement learning is restricted. In our previous works, we had taken these problems into consideration and had proposed new reinforcement learning algorithm, 'Q-learning with dynamic structuring of exploration space based on GA (QDSEGA)'. Effectiveness of QDSEGA for redundant robots has been demonstrated using a 12-legged robot and a 50-link manipulator. However, previous works on QDSEGA were restricted to redundant robots and it was impossible to apply it to multi mobile robots. In this paper, we extend our previous work on QDSEGA by combining a rule-based distributed control and propose a hybrid autonomous control method for multi mobile robots. To demonstrate the effectiveness of the proposed method, simulations of a transportation task by 10 mobile robots are carried out. As a result, effective behaviors have been obtained.     

Interactive Teaching for Vision-Based Mobile Robots: A Sensory-Motor Approach

For the last decade, we have been developing a vision-based architecture for mobile robot navigation. Using our bio-inspired model of navigation, robots can perform sensory-motor tasks in real time in unknown indoor as well as outdoor environments. We address here the problem of autonomous incremental learning of a sensory-motor task, demonstrated by an operator guiding a robot. The proposed system allows for semisupervision of task learning and is able to adapt the environmental partitioning to the complexity of the desired behavior. A real dialogue based on actions emerges from the interactive teaching. The interaction leads the robot to autonomously build a precise sensory-motor dynamics that approximates the behavior of the teacher. The usability of the system is highlighted by experiments on real robots, in both indoor and outdoor environments. Accuracy measures are also proposed in order to evaluate the learned behavior as compared to the expected behavioral attractor. These measures, used first in a real experiment and then in a simulated experiment, demonstrate how a real interaction between the teacher and the robot influences the learning process.     

Convergence and robustness of a discrete-time learning control scheme for constrained manipulators

The constrained motion control is one of the most common control tasks found in many industrial robot applications. The nonlinear and nonclassical nature of the dynamic model of constrained robots make designing a controller for accurate tracking of both motion and force a difficult problem. In this article, a discrete-time learning control problem for precise path tracking of motion and force for constrained robots is formulated and solved. The control system is able to reduce the tracking error iteratively in the presence of external disturbances and errors in initial condition as the robot repeats its action. Computer simulation result is presented to demonstrate the performance of the proposed learning controller. © 1994 John Wiley & Sons, Inc.     

Bio-insect and artificial robot interaction using cooperative reinforcement learning

In this paper, we propose fuzzy logic-based cooperative reinforcement learning for sharing knowledge among autonomous robots. The ultimate goal of this paper is to entice bio-insects towards desired goal areas using artificial robots without any human aid. To achieve this goal, we found an interaction mechanism using a specific odor source and performed simulations and experiments [1]. For efficient learning without human aid, we employ cooperative reinforcement learning in multi-agent domain. Additionally, we design a fuzzy logic-based expertise measurement system to enhance the learning ability. This structure enables the artificial robots to share knowledge while evaluating and measuring the performance of each robot. Through numerous experiments, the performance of the proposed learning algorithms is evaluated.     

Color learning and illumination invariance on mobile robots: A survey

Recent developments in sensor technology have made it feasible to use mobile robots in several fields, but robots still lack the ability to accurately sense the environment. A major challenge to the widespread deployment of mobile robots is the ability to function autonomously, learning useful models of environmental features, recognizing environmental changes, and adapting the learned models in response to such changes. This article focuses on such learning and adaptation in the context of color segmentation on mobile robots in the presence of illumination changes. The main contribution of this article is a survey of vision algorithms that are potentially applicable to color-based mobile robot vision. We therefore look at algorithms for color segmentation, color learning and illumination invariance on mobile robot platforms, including approaches that tackle just the underlying vision problems. Furthermore, we investigate how the inter-dependencies between these modules and high-level action planning can be exploited to achieve autonomous learning and adaptation. The goal is to determine the suitability of the state-of-the-art vision algorithms for mobile robot domains, and to identify the challenges that still need to be addressed to enable mobile robots to learn and adapt models for color, so as to operate autonomously in natural conditions.