Evolvable Hardware in Evolutionary Robotics

In recent decades the research on Evolutionary Robotics (ER) has developed rapidly. This direction is primarily concerned with the use of evolutionary computing techniques in the design of intelligent and adaptive controllers for robots. Meanwhile, much attention has been paid to a new set of integrated circuits named Evolvable Hardware (EHW), which is capable of reconfiguring its architectures unlimited time based on artificial evolution techniques. This paper surveys the application of evolvable hardware in evolutionary robotics. The evolvable hardware is an emerging research field concerning the development of evolvable robot controllers at the hardware level to adapt to dynamic changes in environments. The context of evolvable hardware and evolutionary robotics is reviewed, and a few representative experiments in the field of robotic hardware evolution are presented. As an alternative to conventional robotic controller designs, the potentialities and limitations of the EHW-based robotic system are discussed and summarized.     

Combining Simulation and Reality in Evolutionary Robotics

Evolutionary Robotics (ER) is a promising methodology, intended for the autonomous development of robots, in which their behaviors are obtained as a consequence of the structural coupling between robot and environment. It is essential that there be a great amount of interaction to generate complex behaviors. Thus, nowadays, it is common to use simulation to speed up the learning process; however simulations are achieved from arbitrary off-line designs, rather than from the result of embodied cognitive processes. According to the reality gap problem, controllers evolved in simulation usually do not allow the same behavior to arise once transferred to the real robot. Some preliminary approaches for combining simulation and reality exist in the ER literature; nonetheless, there is no satisfactory solution available. In this work we discuss recent advances in neuroscience as a motivation for the use of environmentally adapted simulations, which can be achieved through the co-evolution of robot behavior and simulator. We present an algorithm in which only the differences between the behavior fitness obtained in reality versus that obtained in simulations are used as feedback for adapting a simulation. The proposed algorithm is experimentally validated by showing the successful development and continuous transference to reality of two complex low-level behaviors with Sony AIBO¹ robots: gait optimization and ball-kicking behavior. ¹AIBO is a trademark of Sony Corporation     

蓬勃发展的空间机器人技术与应用

随着人类太空探索的深入,机器人已经在空间各领域得到了广泛应用,尤其是为载人航天、月球与深空探测、小天体采样返回等航天任务提供了重要的支撑。空间机器人可代替或协助人类在空间环境中进行各种作业,在成本、效率、安全等方面具有突出优势,但空间的大温差、高真空、强辐射、大时延、微重力等苛刻环境,以及航天器的质量、体积、功耗、寿命、可靠性等因素的约束,对空间机器人的研发和应用提出了极大的挑战。面向未来空间大型设施在轨构建、航天器在轨维护维修、宇宙资源就位开发利用等任务,越来越多的新型空间机器人概念被提出,多种样机被开发出来进行地面验证和在轨试验以推进空间机器人技术的发展。 迄今为止,空间机器人的研究主要面向在轨服务（on-orbit servicing,OOS）机器人和星球探测车（planetary rover）两大领域,涉及到新型机器人结构与机构设计、动力学、运动规划与控制、目标定位和捕获、空间遥操作、人机交互、多机协同等核心技术。就目前的研究现状来看,空间机器人仍然存在许多不足之处。(1) 当前空间机器人承担的任务仍然比较简单。对于在轨操作类机器人来说,高速目标捕获、精密装配、多臂协调或多机器人协同操作等任务目前仍大多处于地面实验阶段,仅有少量在轨应用;对于星球探测机器人,机器人在复杂地面环境上进行高速移动探测,以及对深层坚硬样品自主采集等任务尚存在一定困难。(2) 当前机器人的空间环境适应能力不足。高精度、高承载工况下的长寿命传动与润滑、极端高低温环境下的材料适应能力及长时间无光照条件下的能源供给问题亟需解决。(3) 当前空间机器人自主性较低。目前常用的预编程自动控制或遥操作执行效率低,机器人缺乏自主性,主要应用于已知的、结构化环境。这些难题仍需要世界各国的研究者共同探索解决。 近年来,物联网、云计算、人工智能等技术的飞速发展,推动空间机器人技术发生深刻的变化,空间机器人逐渐向构形多样化、执行任务复杂化、在轨服务对象多元化、操控模式自主化的方向发展。这也对空间机器人的研发和应用提出了更多要求。未来面向太空探索,考虑到任务和环境的多样性,充分利用机械、感知、控制、材料、力学、人工智能等方面的最新科技成果,促进空间机器人理论和技术的快速发展,提高机器人的环境感知能力成为重中之重。一方面,要使空间机器人能够进行更为精细复杂的操作,实现自然灵活的人机交互,完成由半自主向自主操作的转变。另一方面,共享型、协同化、智能化成为空间机器人发展的必然趋势,机器人有望能自主识别和重构出新一代的模块化航天器、能实现多机和多人协同操作、能够就位利用空间资源、能完成自主学习和决策,为人类的深空探索做出更大的贡献。     

Concurrent controller and Simulator Neural Network development for a differentially-steered robot in Evolutionary Robotics

Evolutionary Robotics (ER) strives for the automatic creation of robotic controllers and morphologies. The ER process is normally performed in simulation in order to reduce the time required and robot wear. Simulator development is a time consuming process which requires expert knowledge and must traditionally be completed before the ER process can commence. Traditional simulators have limited accuracy, can be computationally expensive and typically do not account for minor operational differences between physical robots.This research proposes the automatic creation of simulators concurrently with the normal ER process. The simulator is derived from an Artificial Neural Network (ANN) to remove the need for formulating an analytical model for the robot. The ANN simulator is improved concurrently with the ER process through real-world controller evaluations which continuously generate behavioural data. Simultaneously, the ER process is informed by the improving simulator to evolve better controllers which are periodically evaluated in the real-world. Hence, the concurrent processes provide further targeted behavioural data for simulator improvement.The concurrent and real-time creation of both controllers and ANN-based simulators is successfully demonstrated for a differentially-steered mobile robot. Various parameter settings in the proposed algorithm are investigated to determine factors pertinent to the success of the proposed approach.     

Monte Carlo Filter in Mobile Robotics Localization: A Clustered Evolutionary Point of View

Localization, i.e., estimating a robot pose relative to a map of an environment, is one of the most relevant problems in mobile robotics. The research community has devoted a big effort to provide solutions for the localization problem. Several methodologies have been proposed, among them the Kalman filter and Monte Carlo Localization filters. In this paper, the Clustered Evolutionary Monte Carlo filter (CE-MCL) is presented. This algorithm, taking advantage of an evolutionary approach along with a clusterization method, is able to overcome classical MCL filter drawbacks. Exhaustive experiments, carried on the robot ATRV-Jr manufactured by iRobot, are shown to prove the effectiveness of the proposed CE-MCL filter.     

空间机器人地面试验系统建模与规划

在空间机器人研究中,空间机器人的地面试验是必不可少的,通过地面试验系统模拟空间环境,可以进行实验,验证空间系统上所采用的理论、方法是否合理,为下一步的空间实验提供依据.为了给下一步的研究提供平台,针对小型智能空间机器人系统的气浮地面实验系统采用拉格朗日法建立系统的运动学、动力学模型,采用5-3-5混合插值法进行了路径规划,通过仿真试验,验证了空间建模理论和规划方法的正确性,为空间机器人实验的改进提供了依据.     

Robotics for medical applications

The authors review the most important past and ongoing research projects in macro-robotics, micro-robotics and bio-robotics, three general areas of robotics which have the potential to provide significant improvements to the state of the art of medical technology. A brief analysis of the economic potentialities of robotics in medicine is also provided     

出行行为决策动力系统的演化分析

城市道路流量通过一个复杂的多维出行决策行为（同时包含出行模式、出发时间和路径）缓慢地进行演化。针对一般的出行行为决策过程,使用演化博弈理论,提出一个新的交通分配问题的动力系统模型,对驾驶者的多维出行选择进行相当一般和合理的假设,使用李雅普洛夫方法在一般网络上考察均衡点的稳定性,结果表明,当个体出行收益满足一些约束条件的时候,演化动力系统存在唯一的均衡解。这意味着从长远观点来看,交通系统可能存在着内在的驱动力,使得它的流量朝着稳定的模式发展。这些结果可以提高对于城市交通流演化的理解,并且为相关的管理部分提供有价值的参考。     

基于空间划分的细粒度并行演化算法

引入（μ＋１）选择策略，提出在群体形成的最小凸集中随机均匀地生成新个体的空间划分选择策略，并将其引入细粒度并行演化模型中，提出了应用于此模型的新算法。给出了并行动算求解的仿真实例，并分析了新算法在防止早熟收敛方面的特性。     

Robotics in Education

Collision avoidance is essential for safe robot manipulation. Especially with humans around, robots should work only when safety can be robustly guaranteed. In this paper, we propose using virtual impedance control for reactive, smooth, and consistent collision avoidance that interferes minimally with the original task. The virtual impedance control operates in the risk space, a vector space describing the possibilities of all forthcoming collisions, and is designed to elude all risks in a consistent response in order to create assuring human-robot interaction experiences. The proposed scheme intrinsically handles kinematic singularity and the activation of avoidance using a boundary layer defined on the spectrum of Jacobian. In cooperation with the original controller, the proposed avoidance scheme provides a proof of convergence if the original controller is stable with and without projection. In simulations and experiments, we verified the characteristics of the proposed control scheme and integrated the system with Microsoft Kinect to monitor the workspace for real-time collision detection and avoidance. The results show that the proposed approach is suitable for robot operation with humans nearby.     

Component Specifications for Robotics Integration

Robotics researchers have been unable to capitalize easily on existing software components to speed up their development efforts and maximize their system's capabilities. A component-based approach for building the software for robotics systems can provide reuse and sharing abilities to the research community. The software engineering community has been studying reuse techniques for three decades. We present several results from those efforts that are applicable to the robotics software integration problem. We describe how to specify a software component so that a potential user may understand its capabilities and facilitate its application to his or her system. At the National Institute of Standards and Technology, we have developed a three-stage, component-specification approach. We illustrate this approach for a component that is relevant to robotics.     

基于网页空间进化算法的暴雨灾害主题爬虫策略

针对单目标优化算法求解爬虫问题时难以获得最优加权因子和易于陷入局部最优的缺点,将多目标优化算法引入主题爬虫,提出一种基于多目标优化的网页空间进化算法。通过计算测试链接与种子链接库中链接的最短距离,将其与种子链接库中所有链接间的平均距离进行比较来更新种子链接库。针对多目标优化中Pareto最优解的选取问题,给出一种最近最远候选解法。实验结果表明,与宽度优先搜索等算法相比,该算法具有较高的爬准率和稳定性。     

Time-varying Algorithm for Swarm Robotics

Ever since the concept of swarm intelligence was brought out, a variety of control algorithms for swarm robotics has been put forward, and many of these algorithms are stable enough and efficient. Most of the researches only take an invariable controller which functions through the whole stage into consideration, the situation in which controller changes over time is rarely taken into account. However, there are limitations for invariable controller dominated algorithms in practical situation, which makes them unable to meet changing environment. On the contrary, variable controller is more flexible and can be able to adapt to complex environment. Considering such advantages, a time-varying algorithm for swarm robotics is presented in this paper. The algorithm takes time as one of the independent variables so that the controller is no longer fixed through the time, but can be changed over time, which brings more choices for the swarm robot system. In this paper, some relevant simulations are designed to test the algorithm. Different control strategies are applied on the same flock during the time, and a more complex, flexible and practical control effect is acquired successfully.