Behavior of Delivery Robot in Human-Robot Collaborative Spaces During Navigation

Navigation is an essential skill for robots.It becomes a cumbersome task for the robot in a human-populated environment,and Industry 5.0 is an emerging trend that focuses on the interaction between humans and robots.Robot behavior in a social setting is the key to human acceptance while ensuring human comfort and safety.With the advancement in robotics technology,the true use cases of robots in the tourism and hospitality industry are expanding in number.There are very few experimental studies focusing on how people perceive the navigation behavior of a delivery robot.A robotic platform named“PI”has been designed,which incorporates proximity and vision sensors.The robot utilizes a real-time object recognition algorithm based on the You Only Look Once(YOLO)algorithm to detect objects and humans during navigation.This study is aimed towards evaluating human experience,for which we conducted a study among 36 participants to explore the perceived social presence,role,and perception of a delivery robot exhibiting different behavior conditions while navigating in a hotel corridor.The participants’responses were collected and compared for different behavior conditions demonstrated by the robot and results show that humans prefer an assistant role of a robot enabled with audio and visual aids exhibiting social behavior.Further,this study can be useful for developers to gain insight into the expected behavior of a delivery robot.

Behavior selection strategy for soccer robots

The soccer robot system and the effective multi agents cooperation strategy applied to the MASKARO team composed of a set of action controllers, a set of behavior module and a behavior selector are introduced. The action is the primitive low level component of the robot control system necessary to move the robots on the playground. Each action controller determines the linear and angular velocity commands of the robots corresponding to its own purpose. The behavior is the high level component of the robot control system composed of necessary action sequences. Each behavior module determines the desired action sequences and action commands corresponding to its own objective. The behavior selector considering the information that comes from the vision system selects the behavior of each robot every sampling time. Thus, the behavior of each robot is changed dynamically. The presented strategy is successfully applied to the MASKARO team and the team is ranked in the first place in the 2000 FIRA Korea Cup K League.

The Intellectualized Architecture of the Autonomous Micro- Mobile Robot Based- Behavior

Given the difficulty in hand coding task schemes, an intellectualized architecture of the autonomous micro mobile robot based behavior for fault repair was presented. Integrating the reinforcement learning and the group behavior evolution simulating the human's learning and evolution, the autonomous micro mobile robot will automatically generate the suited actions satisfied the environment. However, the designer only devises some basic behaviors, which decreases the workload of the designer and cognitive deficiency of the robot to the environment. The results of simulation have shown that the architecture endows micro robot with the ability of learning, adaptation and robustness, also with the ability of accomplishing the given task.

Real Time Behavior-Based Control on a Mobile Robot

This paper discusses and compares some common architectures used inautonomous mobile robotics. Then it describes a behavior-based autonomous mobile robot that wasimplemented successfully in the Robotics Lab of the Department of Precision Mechanical Engineering.Fuzzy controller was used to implement the emergency behavior, the behaviors arbitration wasimplemented using the subsumption architecture. In an unknown dynamic indoor environment, the robotachieved real-time obstacle avoidance properties that are cruel for mobile robotics.

Application of Null Space Based Behavior Control to the Swarm Robot’s Control

This paper proposes a solution to controls warm robots in an effort to avoid obstacles, moving to the goal by the method of Null Space based Behavior (NSB) control of an individual in the swarm. This paper also provides the stability analysis of the converging process by investigating the relationship between single agents, and the analysis result is proved by using the Lyapunov theory. Finally, the simulation results in two-dimensional space have confirmed the obtained theoretical results.

面向无人作业的天空地协同网络:需求、挑战、技术变革机遇

在偏远、危险地区,机器人可感知环境、智能交互,协同完成复杂任务。以卫星和无人机为主体的天空地协同网络能够为这些机器人提供全域泛在、随遇接入的通信服务。首先,分析无人作业发展趋势和机器人通信需求。基于此,面向机器人通信的特点,建议跳出“信息传输管道”的传统视角,在支持信息传输的同时,关注信息的内容和价值。进一步综合考虑机器人通信中信息生成的动作及其反馈,提出潜在技术变革的切入点。具体地,从结构化通信资源配置、机器人行为调控和感-传-算-控闭环模型三个方面阐述维纳控制论启发的通信范式变革途径。

机器人餐厅顾客体验的感知、满意与行为意向研究

通过文献和顾客评论提炼消费者感知因子,采用结构方程模型验证消费者行为意向及其影响机制。研究发现:(1)基于机器人餐厅顾客的线上评论,顾客行为意向的三个测评维度为:智能化环境设计、智能化协同服务、智能化食物控制。(2)智能化食物控制、人机协同、环境设计对顾客感知和满意度的影响均正向显著。(3)基于机器人餐厅消费者语境服务深入分析,发现食物控制对顾客满意度影响最大,同时,验证了机器人餐厅顾客感知、满意与行为的逻辑关系。

社交机器人在信息行为研究中的概念、任务及应用

[目的/意义]在社交机器人应用范围不断拓宽的背景下,系统性地综述社交机器人及其信息行为研究有助于洞察该领域的发展趋势,为未来深入探索提供研究借鉴。[方法/过程]详细梳理了社交机器人在信息行为研究中的国内外研究。首先,提炼社交机器人的理论基础和研究热点;其次,从数据资源建设角度和语言理解、情感计算、信息检索、智能对话任务角度归纳社交机器人的基础任务;再次,对社交机器人在具体应用场景中的信息行为研究进行总结和讨论;最后,深入讨论该领域的未来发展趋势。[结果/结论]社交机器人在信息行为研究领域的未来发展具有巨大的潜力和挑战,如交互质量评估、社会认知增强、用户信息披露、情感理解能力等方面,并积极探索解决方案。

酒店机器人包容性服务定位对顾客公民行为的影响——基于社会认同和社会反应理论视角

随着人工智能在酒店业的广泛应用,服务机器人在提升服务交付效率、改善顾客消费体验的同时,也引发了顾客的非公民行为。在此背景下,通过对机器人进行包容性服务定位已经成为许多酒店引导顾客公民行为的重要策略。基于社会认同理论和社会反应理论,通过四项实验检验了酒店机器人包容性服务定位对顾客公民行为的影响机制及边界条件。结果表明:(1)与非包容性服务定位相比,机器人包容性服务定位更能促进顾客公民行为。(2)社会归属感中介了机器人包容性服务定位对顾客公民行为的影响。(3)机器人共情在上述关系中存在调节作用。对于高共情的机器人而言,包容性服务定位能够进一步激发顾客的社会归属感,并促进顾客公民行为。(4)机器人共情的调节效应受到顾客特质调节聚焦的再调节。对于防御聚焦顾客而言,机器人共情的调节效应消失。对于促进聚焦顾客而言,机器人共情的调节效应增强。

基于软件机器人的工控靶场应用软件行为模拟

工控靶场为开展工业控制系统(ICS)安全研究提供重要支撑。面向工控靶场关键任务之一的应用软件行为模拟,提出一种软件机器人方法以实现工控靶场应用软件行为的逼真模拟。考虑软件图形界面及软件内在显隐式规则,提出基于尺度不变特征变换(SIFT)图像相似度的软件菜单采集算法及混合状态机模型对应用软件行为进行建模。针对软件机器人的智能化问题,使用深度Q网络(DQN)算法驱动软件机器人对应用软件行为进行自主学习,同时结合多重经验回访和多重目标网络对DQN算法进行优化。实验结果表明:基于DQN的软件机器人能够对工控软件进行有效学习,且优化后的DQN算法自主学习效果更佳。

基于逆强化学习与行为树的机械臂复杂操作技能学习方法

现有分层强化学习方法不仅在学习过程中存在样本效率低、奖励稀疏以及学习时间过长等问题,而且大多基于仿真环境,导致学习策略在机器人真实操作环境中部署困难。针对上述问题,提出一种基于逆强化学习与行为树的机械臂复杂操作技能学习方法。在对复杂操作任务进行分割的基础上,首先,根据专家演示轨迹确定分割后每个子任务的强化学习参数及其对应的专家策略;其次,根据子任务专家策略并使用生成对抗模仿学习算法进行预训练,得到每个子任务的奖励函数;再次,运用每个子任务的奖励函数并基于SAC算法做进一步训练,获取到每个子任务的最优策略;最后,将子任务最优策略从仿真环境部署到真实环境,并将部署后的每个子任务作为叶节点构建行为树,实现机械臂复杂任务规划。实验结果表明,所提方法能较好地学习机械臂复杂操作技能,并在性能上优于其他传统强化学习算法。

酒店机器人包容性服务定位与社会阶层的交互作用对顾客公民行为的影响

基于共情理论,探讨酒店机器人包容性服务定位和社会阶层的交互作用对顾客公民行为的影响机制及边界条件。通过3个情境实验,研究发现:对于低社会阶层,相比非包容性服务定位,机器人包容性服务定位更能促进其顾客公民行为;对于高社会阶层,机器人包容性服务定位和非包容性服务定位对顾客公民行为的影响不存在显著差异。机器人包容性服务定位与社会阶层的交互作用对顾客公民行为产生影响,顾客情感共情在其中起到中介作用,其他群体导向存在显著调节作用。若个体的其他群体导向较高,则高社会阶层消费者面对机器人包容性服务定位时,其顾客公民行为会显著增加。

采摘机器人全果园视觉感知及自主作业综述

[目的/意义]采摘机器人是智慧农业的重要组成部分,其感知、规划、控制相关基础方法理论目前已有系统化研究。然而,构建具备全果园“感知-移动-采摘”一体化作业能力的实用型采摘系统仍面临诸多挑战。针对该问题,本文调研并报道了本领域近期案例,将全果园自主作业的关键技术划分为局部目标感知、全局地图构建和自主作业行为规划三个子问题并进行综述。[进展]首先回顾了近距离、局部范围内水果目标的精细视觉感知方法,包括基于低级特征融合、高级特征学习、RGB-D信息融合,以及多视角信息融合的4种方法;介绍与分析了全局尺度下的果园地图构建与大规模场景视觉感知案例;在感知的基础上,调研分析采摘机器人自主作业行为规划方法,包括底盘移动路径规划、机械臂视点规划与避障路径规划等方面的最新研究;最后对采摘机器人自主作业系统构建案例进行报道与分析。[结论/展望]感知、移动、采摘模块的高效协同是实现采摘机器人从基础功能样机进一步迈向实用型机器的关键,已有的视觉感知、规划与控制算法的鲁棒性与稳定性均需增强,协同程度需进一步提高。此外,提及了采摘机器人应用的几个开放性研究问题,并描述了其未来发展趋势。

基于模型预测的四足机器人运动控制

针对具有多自由度的四足机器人,结合中枢模式发生器(CPG)和模型预测控制机理(MPC),提出2种模型融合的神经控制方法.该方法以模型预测原理为基础,通过模拟生物神经控制机制,构建腿足机器人行为运动神经控制架构.该架构能够处理外部环境信息,自适应调节机身和腿部位置,实现机器人位置跟踪、全向运动和多种非典型步态.实验结果表明,基于MPC-CPG控制架构的机器人可以快速响应并消除位置误差和角度误差,机身轨迹跟踪的位置误差始终保持在−0.1~0.1 m,姿态角误差保持在−0.05~0.05 rad.在MPC-CPG控制器的作用下,机器人不仅具有较高的轨迹跟踪精度,还表现出行为多样性,验证了所提出的MPC-CPG控制器的有效性.

Formation Feedback Applied to Behavior-Based Approach to Formation Keeping

Approaches to the study of formation keeping for multiple mobile robots are analyzed and a behavior-based robot model is built in this paper. And, a kind of coordination architecture is presented, which is similar to the infantry squad organization and is used to realize multiple mobile robots to keep formations. Simulations verify the validity of the approach to keep formation, which combines the behavior-based method and formation feedback. The effects of formation feedback on the performance of the system are analyzed.

Humanoid Robots That Behave, Speak, and Think Like Humans:A Reduction to Practice RRC-Humanoid Robot

A radical new approach is presented to programming human-like levels of Artificial Intelligence (AI) into a humanoid robot equipped with a verbal-phoneme sound generator. The system shares 3 important characteristics with human-like input data and processing: 1) The raw data and preliminary processing of the raw data are human-like. 2) All the data are subjective, that is related and correlated with a robotic self-identity coordinate frame. 3) All the data are programmed behaviorally into the system. A multi-tasking Relational Robotic Controller (RRC)-Humanoid Robot, described and published in the peer-reviewed literature, has been specifically designed to fulfill those 3 characteristics. A RRC-controlled system may be behaviorally programmed to achieve human-like high I.Q. levels of subjective AI for the visual signals and the declarative-verbal words and sentences heard by the robot. A proof of concept RRC-Humanoid Robot is under development and present status is presented at the end of the paper.

Influence of Data Clouds Fusion From 3D RealTime Vision System on Robotic Group Dead Reckoning in Unknown Terrain

This paper proposes the solution of tasks set required for autonomous robotic group behavior optimization during the mission on a distributed area in a cluttered hazardous terrain.The navigation scheme uses the benefits of the original real-time technical vision system(TVS)based on a dynamic triangulation principle.The method uses TVS output data with fuzzy logic rules processing for resolution stabilization.Based on previous researches,the dynamic communication network model is modified to implement the propagation of information with a feedback method for more stable data exchange inside the robotic group.According to the comparative analysis of approximation methods,in this paper authors are proposing to use two-steps post-processing path planning aiming to get a smooth and energy-saving trajectory.The article provides a wide range of studies and computational experiment results for different scenarios for evaluation of common cloud point influence on robotic motion planning.

新技术形势下四足、双足机器人技术的变革

从科研思维、行为模式方面,论述四足、双足机器人国内发展现状,对比国内与波士顿动力公司间存在的差距。从科研范式的角度分析新技术推动下中国在四足、双足机器人换道超车的核心技术及发展方向,总结国内在电驱动关节及足式机器人控制方面所面临的机遇,提出基于AI驱动的足式机器人系统设计方法。

四足机器人特定复杂运动技能控制

为了提高四足机器人的运动多样性和地形适应性,提出一种复杂运动行为控制方法,通过构建四足机器人动力学模型,在此基础上进行离线滚动优化预测,生成四足机器人复杂行为的期望轨迹。在运动学、关节扭矩、接触力、运动状态和地形高度等非线性约束下更全面地优化了轨迹,设计在线轨迹跟踪控制器与落足控制器,实现四足机器人复杂行为的在线控制。在多复杂运动的动态仿真环境下评估了所提方法,机器人可以实现前跳、后空翻、前空翻和旋转跳跃,并可以在给定地形信息下跳跃障碍物。将在线轨迹跟踪控制器迁移到四足机器人物理样机中,完成了四足机器人向前跳跃的实验。实验结果表明,所提出的方法能够使四足机器人有效地完成多种特定复杂运动技能的稳定控制。

基于WHD-CRM的群体机器人故障检测

使用交叉调节模型(CRM)进行故障检测,是一种重要的基于人工免疫模型的群体机器人故障检测方法.针对CRM在不同群体行为下,检测群体机器人故障通用性受限的问题,通过研究行为特征在不同群体行为下的影响力等级,提出了使用加权汉明距离的交叉调节模型(WHD-CRM).相比于CRM,WHD-CRM对不同群体行为的机器人行为特征赋予了不同的权值,获得了更加精确的中间结果(亲和力值),进而提高了各类群体行为下检出故障机器人的概率.实验结果表明:相比于使用原始CRM,WHD-CRM的故障检测率提高了13%.