机器人仿生学研究综述

机器人仿生学是从仿生的角度对机器人进行研究，是机器人领域的重要分支．本文从 结构仿生，材料仿生，功能仿生，控制仿生，群体仿生五个方面，归纳和评述了国内外机器 人仿生学的研究现状、特点、主要成果以及发展趋势．     

普及信息技术基础教育之十：SL-ROBOT-1通用仿生机器人的创新开发

每年的全国青少年科技创新大赛设有专门展区展示机器人创意设计的优秀作品．其宗旨是培养青少年的实践能力和创新精神。机器人可分为轮式、仿生、虚拟及网络机器人。SL-ROBOT-1是双龙电子开发的具有四足动物仿生功能的机器人．可用图形软件和流程图软件．也可用汇编语言及C语言开发通用智能仿生机器人。     

基于柔性长鳍波动推进的仿生水下机器人设计与实现

以基于柔性长鳍波动推进的仿生水下机器人试验模型为背景，主要研究其设计与实现问题．首先，介绍了仿生水下机器人试验模型的设计原则及其系统总体结构，然后重点研究了仿生柔性长鳍、主控模块与通讯系统、运动控制子系统的设计方法、系统构成和工作原理，最后介绍了试验模型的系统测试与航行试验结果及其结论，并指出了仿生水下机器人试验模型的改进重点和柔性长鳍波动推进技术今后的研究方向．基于柔性长鳍波动推进的仿生水下机器人试验模型的研制成功，初步验证了柔性长鳍波动推进方式应用于水下机器人推进控制系统在原理上和技术上是可行的．     

机器人仿生面部系统研究综述

机器人仿生面部系统是近几年机器人研究领域较活跃并引起广泛兴趣的研究方向之 一．本文综合分析了在机器人仿生面部系统的制造及其表情的产生等方面的研究现状，并对 该领域未来的研究重点、发展方向作出了展望．     

北京联合大学机电学院机器人技术成果

北京联合大学机电学院本着“应用为本”的理念,在机器人领域的研究团队重点研究机器人直流伺服驱动技术,并以直流伺服驱动技术为基础,开发了各类仿生机器人,如仿生人形机器人、仿生机械狗、仿生机械蜘蛛,6自由度仿生机械臂,以及仿人大型娱乐机器人. 仿生人形机器人(图1)已作为北京市机器人比赛的运动平台,完成了2013、2014两届中小学机器人比赛的任务.     

SL-ROBOT-1通用仿生机器人的创新开发

SL-ROBOT-1是双龙电子开发的、具有四足动物仿生功能的机器人．可用图形软件、流程图软件、汇编语言及C语言开发通用智能仿生机器人。     

六足微型仿生机器人及其控制系统的研究

介绍了一种微型六足仿生机器人的结构与控制系统，分析了这种微型六足仿生机器人的移动原理，阐述了如何通过计算机来控制微型六足仿生机器人的运动，该机器人基于仿生学原理，结构独特，简单，新颖，能方便地实现前进和后退，其样相外形尺寸为：长30mm,宽40mm,高20mm，重6．3克，并对该样机进行了实验，实验结果表明该机器人具有较好的机动性。     

六足移动式微型仿生机器人的研究

本文描述了一种微型六足仿生机器人的结构与控制，分析了这种微型六足仿生机器人 的移动原理. 该机器人基于仿生学原理，结构独特、简单、新颖，能方便地实现前进和后退 ，其样机外形尺寸为：长30mm，宽40mm，高20mm，重6.3g．并对该样机进行了实验，实验 结果表明该机器人具有较好的机动性．     

灵长类仿生机器人悬臂运动仿生控制综述

灵长类仿生机器人是通过智能机械手段模仿灵长类运动的一类机器人,针对其悬臂运动仿生的控制研究是该领域的热点.综述了目前灵长类仿生机器人悬臂运动仿生控制的研究方法,给出了悬臂运动仿生控制的一般方法与基于"动态伺服"理论的悬臂运动仿生控制策略,提出了悬臂运动仿生控制中亟待解决的若干问题,并对今后灵长类仿生机器人悬臂运动仿生控...     

IEEE机器人与仿生技术国际会议（ROBIO 2009）征文通知

由IEEE机器人与自动化协会、国防科学技术大学、香港中文大学、广西师范大学和香港城市大学联合承办的2009 IEEE机器人与仿生技术国际会议将于2009年12月18日至22日在广西桂林举行．此次会议旨在为机器人与仿生技术及其交叉领域的专家学者提供一个公诸最新研究成果的论坛．2009 ROBIO的主题是：Robot—assisted bioengineering to serve humans．     

免疫机器人的仿生计算与控制

传统的移动机器人研究一般假设环境是安全的，为了增强机器人在危险、变化的环境中适应无人作业的能力，提高机器人对外界干扰、攻击和破坏的抵抗力、容错力和免疫力，提出了危险环境的自体／异体建模方法和免疫机器人的仿生计算模型与控制方法．模仿生物免疫系统，构建机器人的免疫计算模型和免疫控制结构，实现类似于生物免疫系统的自体／异体检测、辨别、学习和修复及鲁棒性、免疫性等功能．免疫机器人技术用来检测、识别和预报危险、变化的环境，检测并修复机器人的正常状态，实现恶劣环境中机器人仿生控制，具有重要的理论创新意义、明显的技术创新价值和可观的应用前景．     

BioDKM: Bio-inspired domain knowledge modeling method for humanoid delivery robots’ planning

A bio-inspired human domain knowledge modeling method, BioDKM, is proposed and developed to make delivery robots think more humanly and act more effectively. This presented method focused on feasible fusion between artificial intelligent and bionics in the field of tasks planning or scheduling in delivery robots. BioDKM is designed and implemented with several components, in terms of human knowledge, workflow (WF), hierarchical task network (HTN), and planner. In detail, WF is utilized as the human domain knowledge modeling tool, because of its convenient applications, friendly user interface and explicit representation. Moreover, WF can effectively complement conventional HTN planning with great convenience to formalize human domain knowledge. Translation from WF to HTN is also considered and established to make task planning smooth. Finally, examples and simulations are carried out to validate the effectiveness of this proposed bio-inspired domain knowledge modeling method.     

A saliency-driven robotic head with bio-inspired saccadic behaviors for social robotics

This paper presents a robotic head for social robots to attend to scene saliency with bio-inspired saccadic behaviors. Scene saliency is determined by measuring low-level static scene information, motion, and object prior knowledge. Towards the extracted saliency spots, the designed robotic head is able to turn gazes in a saccadic manner while obeying eye–head coordination laws with the proposed control scheme. The results of the simulation study and actual applications show the effectiveness of the proposed method in discovering of scene saliency and human-like head motion. The proposed techniques could possibly be applied to social robots to improve social sense and user experience in human–robot interaction.     

On a bio-inspired hybrid pheromone signalling for efficient map exploration of multiple mobile service robots

This paper presents a novel bio-inspired hybrid communication framework that incorporates the repelling behaviour of anti-aphrodisiac pheromones and attractive behaviour of pheromones for efficient map exploration of multiple mobile service robots. The proposed communication framework presents a scheme for robots to efficiently serve large areas of map, while cooperating with each other through proper pheromone deposition. This eliminates the need of explicitly programming each service robot to serve particular areas of the map. The paths taken by robots are represented as nodes across which pheromones are deposited. This reduces the search space for tracking pheromones and reduces data size to be communicated between robots. A novel pheromone deposition model is presented which takes into account the uncertainty in the robot’s position. This eliminates robots to deposit pheromones at wrong places when localization fails. The framework also integrates the pheromone signalling mechanism in landmark-based Extended Kalman Filter (EKF) localization and allows the robots to capture areas or sub-areas of the map, to improve the localization. A scheme to resolve conflicts through local communication is presented. We discuss, through experimental and simulation results, two cases of floor cleaning task, and surveillance task, performed by multiple robots. Results show that the proposed scheme enables multiple service robots to perform cooperative tasks intelligently without any explicit programming.     

Development and Control of Underwater Gliding Robots: A Review

As one of the most effective vehicles for ocean development and exploration, underwater gliding robots (UGRs) have the unique characteristics of low energy consumption and strong endurance. Moreover, by borrowing the motion principles of current underwater robots, a variety of novel UGRs have emerged with improving their maneuverability, concealment, and environmental friendliness, which significantly broadens the ocean applications. In this paper, we provide a comprehensive review of underwater gliding robots, including prototype design and their key technologies. From the perspective of motion characteristics, we categorize the underwater gliding robots in terms of traditional underwater gliders (UGs), hybrid-driven UGs, bio-inspired UGs, thermal UGs, and others. Correspondingly, their buoyancy driven system, dynamic and energy model, and motion control are concluded with detailed analysis. Finally, we have discussed the current critical issues and future development. This review offers valuable insight into the development of next-generation underwater robots well-suited for various oceanic applications, and aims to gain more attention of researchers and engineers to this growing field.      

An adaptive, self-organizing dynamical system for hierarchical control of bio-inspired locomotion

In this paper, dynamical systems made up of locally coupled nonlinear units are used to control the locomotion of bio-inspired robots and, in particular, a simulation of an insect-like hexapod robot. These controllers are inspired by the biological paradigm of central pattern generators and are responsible for generating a locomotion gait. A general structure, which is able to change the locomotion gait according to environmental conditions, is introduced. This structure is based on an adaptive system, implemented by motor maps, and is able to learn the correct locomotion gait on the basis of a reward function. The proposed control system is validated by a large number of simulations carried out in a dynamic environment for simulating legged robots.     

Experimental studies on chemical concentration map building by a multi-robot system using bio-inspired algorithms

In this article we describe implementations of various bio-inspired algorithms for obtaining the chemical gas concentration map of an environment filled with a contaminant. The experiments are performed using Khepera III and miniQ miniature mobile robots equipped with chemical gas sensors in an environment with ethanol gas. We implement and investigate the performance of decentralized and asynchronous particle swarm optimization (DAPSO), bacterial foraging optimization (BFO), and ant colony optimization (ACO) algorithms. Moreover, we implement sweeping (sequential search algorithm) as a base case for comparison with the implemented algorithms. During the experiments at each step the robots send their sensor readings and position data to a remote computer where the data is combined, filtered, and interpolated to form the chemical concentration map of the environment. The robots also exchange this information among each other and cooperate in the DAPSO and ACO algorithms. The performance of the implemented algorithms is compared in terms of the quality of the maps obtained and success of locating the target gas sources.     

Flexible robotic grasping strategy with constrained region in environment

Grasping is a significant yet challenging task for the robots. In this paper, the grasping problem for a class of dexterous robotic hands is investigated based on the novel concept of constrained region in environment, which is inspired by the grasping operations of the human beings. More precisely, constrained region in environment is formed by the environment, which integrates a bio-inspired co-sensing framework. By utilizing the concept of constrained region in environment, the grasping by robots can be effectively accomplished with relatively low-precision sensors. For the grasping of dexterous robotic hands, the attractive region in environment is first established by model primitives in the configuration space to generate offline grasping planning. Then, online dynamic adjustment is implemented by integrating the visual sensory and force sensory information, such that the uncertainty can be further eliminated and certain compliance can be obtained. In the end, an experimental example of BarrettHand is provided to show the effectiveness of our proposed grasping strategy based on constrained region in environment.     

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.     

Hybrid approach to implement multi-robotic navigation system using neural network,fuzzy logic,and bio-inspired optimization methodologies

Mobile robots have been increasingly popular in a variety of industries in recent years due to their ability to move in variable situations and perform routine jobs effectively. Path planning, without a dispute, performs a crucial part in multi-robot navigation, making it one of the very foremost investigated issues in robotics. In recent times, meta-heuristic strategies have been intensively investigated to tackle path planning issues in the similar way that optimizing issues were handled, or to design the optimal path for such multi-robotics to travel from the initial point to such goal. The fundamental purpose of portable multi-robot guidance is to navigate a mobile robot across a crowded area from initial point to target position while maintaining a safe route and creating optimum length for the path. Various strategies for robot navigational path planning were investigated by scientists in this field. This work seeks to discuss bio-inspired methods that are exploited to optimize hybrid neuro-fuzzy analysis which is the combination of neural network and fuzzy logic is optimized using the particle swarm optimization technique in real-time scenarios. Several optimization approaches of bio-inspired techniques are explained briefly. Its simulation findings, which are displayed for two simulated scenarios reveal that hybridization increases multi-robot navigation accuracy in terms of navigation duration and length of the path.