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.

Human-Robot Collaboration Framework Based on Impedance Control in Robotic Assembly

Human–robot(HR)collaboration(HRC)is an emerging research field because of the complementary advantages of humans and robots.An HRC framework for robotic assembly based on impedance control is proposed in this paper.In the HRC framework,the human is the decision maker,the robot acts as the executor,while the assembly environment provides constraints.The robot is the main executor to perform the assembly action,which has the position control,drag and drop,positive impedance control,and negative impedance control modes.To reveal the characteristics of the HRC framework,the switch condition map of different control modes and the stability analysis of the HR coupled system are discussed.In the end,HRC assembly experiments are conducted,where the HRC assembly task can be accomplished when the assembling tolerance is 0.08 mm or with the interference fit.Experiments show that the HRC assembly has the complementary advantages of humans and robots and is efficient in finishing complex assembly tasks.

Towards Collaborative Robotics in Top View Surveillance:A Framework for Multiple Object Tracking by Detection Using Deep Learning

Collaborative Robotics is one of the high-interest research topics in the area of academia and industry.It has been progressively utilized in numerous applications,particularly in intelligent surveillance systems.It allows the deployment of smart cameras or optical sensors with computer vision techniques,which may serve in several object detection and tracking tasks.These tasks have been considered challenging and high-level perceptual problems,frequently dominated by relative information about the environment,where main concerns such as occlusion,illumination,background,object deformation,and object class variations are commonplace.In order to show the importance of top view surveillance,a collaborative robotics framework has been presented.It can assist in the detection and tracking of multiple objects in top view surveillance.The framework consists of a smart robotic camera embedded with the visual processing unit.The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization.The detection models are further combined with different tracking algorithms,including GOTURN,MEDIANFLOW,TLD,KCF,MIL,and BOOSTING.These algorithms,along with detection models,help to track and predict the trajectories of detected objects.The pre-trained models are employed;therefore,the generalization performance is also investigated through testing the models on various sequences of top view data set.The detection models achieved maximum True Detection Rate 93%to 90%with a maximum 0.6%False Detection Rate.The tracking results of different algorithms are nearly identical,with tracking accuracy ranging from 90%to 94%.Furthermore,a discussion has been carried out on output results along with future guidelines.

Trajectory Planning of High Precision Collaborative Robots

In order to satisfy the high efficiency and high precision of collaborative robots,this work presents a novel trajectory planning method.First,in Cartesian space,a novel velocity look-ahead control algorithm and a cubic polynomial are combined to construct the end-effector trajectory of robots.Then,the joint trajectories can be obtained through the inverse kinematics.In order to improve the smoothness and stability in joint space,the joint trajectories are further adjusted based on the velocity look-ahead control algorithm and quintic B-spline.Finally,the proposed trajectory planning method is tested on a 4-DOF serial collaborative robot.The experimental results indicate that the collaborative robot achieves the high efficiency and high precision,which validates the effectiveness of the proposed method.

Human-Robot Collaborative Planning for Navigation Based on Optimal Control Theory

Navigation modules are capable of driving a robotic platform without direct human participation. However, for some specific contexts, it is preferable to give the control to a human driver. The human driver participation in the robotic control process when the navigation module is running raises the share control issue. This work presents a new approach for two agents collaborative planning using the optimal control theory and the three-layer architecture. In particular, the problem of a human and a navigation module collaborative planning for a trajectory following is analyzed. The collaborative plan executed by the platform is a weighted summation of each agent control signal. As a result, the proposed architecture could be set to work in autonomous mode, in human direct control mode or in any aggregation of these two operating modes. A collaborative obstacle avoidance maneuver is used to validate this approach. The proposed collaborative architecture could be used for smart wheelchairs, telerobotics and unmanned vehicle applications.

Assessment of Potentially Attractive Tasks to Collaborative Robotics Application:A Case Study Based on Electronics Industry

The use of robotics in the electronics industry has been of great importance to raise productivity and quality levels.When compared to the classic industrial robots,the collaborative ones present themselves as a trend,bringing greater flexibility,improving ergonomics,shortening implementation time and degree of configurability.However,the correct definition of their use,when compared to industrial robots,still needs more understanding and discussion so as not to become an intuitive process.The objective of this work is to present a methodology based on a time and motion study to define the tasks which have the greatest potential to be automated and to be implemented with simplicity.To validate this methodology,two consecutive stations of a packaging assembly line of smartphones were considered.The obtained results show feasibility and applicability in the tested solution,allowing it to be applied in other situations.

Singular perturbation composite control of a free-floating flexible dual-arm space robot

The Free-floating Flexible Dual-arm Space Robot is a highly nonlinear and coupled dynamics system. In this paper, the dynamic model is derived of a Free-floating Flexible Dual-arm Space Robot holding a rigid payload. Furthermore, according to the singular perturbation method, the system is separated into a slow subsystem representing rigid body motion of the robot and a fast subsystem representing the flexible link dynamics. For the slow subsystem, based on the second method of Lyapunov, using simple quantitative bounds on the model uncertainties, a robust tracking controller design is used during the trajectory tracking phase. The optimal control method is designed in the fast subsystem to guarantee the exponential stability. With the combination of the two above, the system can track the expected trajectory accurately, even though with uncertainty in model parameters, and its flexible vibration gets suppressed, too. Finally, some simulation tests have been conducted to verify the effectiveness of the proposed methods.

Adaptive neural network control for coordinated motion of a dual-arm space robot system with uncertain parameters

Control of coordinated motion between the base attitude and the arm joints of a free-floating dual-arm space robot with uncertain parameters is discussed. By combining the relation of system linear momentum conversation with the Lagrangian approach, the dynamic equation of a robot is established. Based on the above results, the free-floating dual-arm space robot system is modeled with RBF neural networks, the GL matrix and its product operator. With all uncertain inertial system parameters, an adaptive RBF neural network control scheme is developed for coordinated motion between the base attitude and the arm joints. The proposed scheme does not need linear parameterization of the dynamic equation of the system and any accurate prior-knowledge of the actual inertial parameters. Also it does not need to train the neural network offline so that it would present real-time and online applications. A planar free-floating dual-arm space robot is simulated to show feasibility of the proposed scheme.

The Fuzzy Neural Network Control Scheme With H∞ Tracking Characteristic of Space Robot System With Dual-arm After Capturing a Spin Spacecraft

In this paper,the dynamic evolution for a dualarm space robot capturing a spacecraft is studied,the impact effect and the coordinated stabilization control problem for postimpact closed chain system are discussed.At first,the pre-impact dynamic equations of open chain dual-arm space robot are established by Lagrangian approach,and the dynamic equations of a spacecraft are obtained by Newton-Euler method.Based on the results,with the process of integral and simplify,the response of the dual-arm space robot impacted by the spacecraft is analyzed by momentum conservation law and force transfer law.The closed chain system is formed in the post-impact phase.Closed chain constraint equations are obtained by the constraints of closed-loop geometry and kinematics.With the closed chain constraint equations,the composite system dynamic equations are derived.Secondly,the recurrent fuzzy neural network control scheme is designed for calm motion of unstable closed chain system with uncertain system parameter.In order to overcome the effects of uncertain system inertial parameters,the recurrent fuzzy neural network is used to approximate the unknown part,the control method with H∞tracking characteristic.According to the Lyapunov theory,the global stability is demonstrated.Meanwhile,the weighted minimum-norm theory is introduced to distribute torques guarantee that cooperative operation between manipulators.At last,numerical examples simulate the response of the collision,and the efficiency of the control scheme is verified by the simulation results.

Optimization and Design of Cloud-Edge-End Collaboration Computing for Autonomous Robot Control Using 5G and Beyond

Robots have important applications in industrial production, transportation, environmental monitoring and other fields, and multi-robot collaboration is a research hotspot in recent years. Multi-robot autonomous collaborative tasks are limited by communication, and there are problems such as poor resource allocation balance, slow response of the system to dynamic changes in the environment, and limited collaborative operation capabilities. The combination of 5G and beyond communication and edge computing can effectively reduce the transmission delay of task offloading and improve task processing efficiency. First, this paper designs a robot autonomous collaborative computing architecture based on 5G and beyond and mobile edge computing(MEC).Then, the robot cooperative computing optimization problem is studied according to the task characteristics of the robot swarm. Then, a reinforcement learning task offloading scheme based on Qlearning is further proposed, so that the overall energy consumption and delay of the robot cluster can be minimized. Finally, simulation experiments demonstrate that the method has significant performance advantages.

Robot Zero-Moment Control Algorithm Based on Parameter Identification of Low-Speed Dynamic Balance

This paper proposes a zero-moment control torque compensation technique.After compensating the gravity and friction of the robot,it must overcome a small inertial force to move in compliance with the external force.The principle of torque balance was used to realise the zero-moment dragging and teaching function of the lightweight collaborative robot.The robot parameter identification based on the least square method was used to accurately identify the robot torque sensitivity and friction parameters.When the robot joint rotates at a low speed,it can approximately satisfy the torque balance equation.The experiment uses the joint position and the current motor value collected during the whole moving process under the low-speed dynamic balance as the excitation signal to realise the parameter identification.After the robot was compensated for gravity and static friction,more precise torque control was realised.The zero-moment dragging and teaching function of the robot was more flexible,and the drag process was smoother.

Deep-reinforcement-learning-based robot motion strategies for grabbing objects from human hands

Background Robot grasping encompasses a wide range of research areas;however, most studies have been focused on the grasping of only stationary objects in a scene;only a few studies on how to grasp objects from a user's hand have been conducted. In this paper, a robot grasping algorithm based on deep reinforcement learning (RGRL) is proposed. Methods The RGRL takes the relative positions of the robot and the object in a user's hand as input and outputs the best action of the robot in the current state. Thus, the proposed algorithm realizes the functions of autonomous path planning and grasping objects safely from the hands of users. A new method for improving the safety of human-robot cooperation is explored. To solve the problems of a low utilization rate and slow convergence of reinforcement learning algorithms, the RGRL is first trained in a simulation scene, and then, the model para-meters are applied to a real scene. To reduce the difference between the simulated and real scenes, domain randomization is applied to randomly change the positions and angles of objects in the simulated scenes at regular intervals, thereby improving the diversity of the training samples and robustness of the algorithm. Results The RGRL's effectiveness and accuracy are verified by evaluating it on both simulated and real scenes, and the results show that the RGRL can achieve an accuracy of more than 80% in both cases. Conclusions RGRL is a robot grasping algorithm that employs domain randomization and deep reinforcement learning for effective grasping in simulated and real scenes. However, it lacks flexibility in adapting to different grasping poses, prompting future research in achieving safe grasping for diverse user postures.

多机协同智能发展战略研究

多个自主智能系统通过信息、行为交互构成的多机协同智能,代表着未来智能系统的必然发展趋势,是我国新一代人工智能规划部署的主攻方向,也是支撑国防、社会安全的核心技术和推动制造业由大到强的必由之路。开展突破多机协同智能技术发展研究,对于推动我国军事智能、智能产业高质量发展、加快工业转型升级具有重要意义。本文基于多机协同智能系统当前面临的难以适应复杂任务这一挑战,从基础理论和核心关键技术两个层面出发,系统地梳理了多机协同智能的研究现状,分析了制约基础理论与关键技术发展的主要瓶颈性问题,并以多机协同智能制造为典型应用,剖析理论与技术发展中存在的问题。研究认为,多机协同智能将朝着人机群组智能的方向发展,为抢占发展先机,需及早布局人机群组智能的基础理论探索,加速核心技术突破,并加快应用示范。

智能采煤机器人关键技术

采煤机是综采工作面的核心装备,研发智能采煤机器人是实现综采工作面智能化的关键。综合分析当前采煤机机器人化研究进程中的传感检测、位姿控制、速度控制、截割轨迹规划与跟踪控制等技术的研究现状,提出研发智能采煤机器人必须破解的“智能感知、位姿控制、速度控制、截割轨迹规划与跟踪控制、位-姿-速协同控制”五大关键技术,并给出解决方案。针对智能感知问题,提出了构建智能感知系统思路,给出了智能采煤机器人智能感知系统的架构,实现对运行状态、位姿、环境等全面感知,为智能采煤机器人安全、可靠运行提供保障;针对位姿控制问题,提出了智能PID位姿控制思路,给出了改进遗传算法的PID位姿控制方法,实现了智能采煤机器人位姿精准控制;针对速度控制问题,提出了融合“力-电”异构数据的截割载荷测量思路,给出了基于神经网络算法的截割载荷测量方法,实现了截割载荷的精准测量;提出牵引与截割速度自适应控制思路,给出了人工智能算法牵引与截割速度决策方法和滑模自抗扰控制的牵引与截割速度控制方法,实现了智能采煤机器人速度精准自适应控制;针对截割轨迹规划与跟踪控制问题,提出了截割轨迹精准规划思路,给出了融合地质数据和历史截割数据的截割轨迹规划模型,实现了截割轨迹的精准规划;提出了截割轨迹精准跟踪控制思路,给出了智能插补算法的截割轨迹跟踪控制方法,实现了智能采煤机器人截割轨迹高精度规划与精准跟踪控制;针对“位-姿-速”协同控制问题,提出了“位-姿-速”协同控制参数智能优化思路,给出了基于多系统互约束的改进粒子群“位-姿-速”协同控制参数优化方法,实现了智能采煤机器人智能高效作业。深入研究五大关键技术破解思路,有利于加快推动研发高性能、高效率、高可靠的智能采煤机器人。

Digital twin-and extended reality-based telepresence for collaborative robot programming in the 6G perspective

In the context of Industry 4.0,a paradigm shift from traditional industrial manipulators to Collaborative Robots(CRs)is ongoing,with the latter serving ever more closely humans as auxiliary tools in many production processes.In this scenario,continuous technological advancements offer new opportunities for further innovating robotics and other areas of next-generation industry.For example,6G could play a prominent role due to its human-centric view of the industrial domains.In particular,its expected dependability features will pave the way for new applications exploiting highly effective Digital Twin(DT)-and eXtended Reality(XR)-based telepresence.In this work,a novel application for the above technologies allowing two distant users to collaborate in the programming of a CR is proposed.The approach encompasses demanding data flows(e.g.,point cloud-based streaming of collaborating users and robotic environment),with network latency and bandwidth constraints.Results obtained by analyzing this approach from the viewpoint of network requirements in a setup designed to emulate 6G connectivity indicate that the expected performance of forthcoming mobile networks will make it fully feasible in principle.

煤矿防冲钻孔机器人全自主钻进系统关键技术

针对高地应力矿井钻孔卸压作业智能化程度低的技术难题,总结分析了国内外钻孔卸压技术和装备的研究现状,指出研发高性能、高可靠、高效率的防冲钻孔机器人全自主钻进系统是破解冲击地压防治难题的重要发展方向。为此,凝练了影响钻进系统性能的“孔位精准识别、钻具姿态精确感知、无线电磁随钻智能检测、钻具运行状态智能识别和钻进系统精确控制”五大关键技术,并给出了解决思路和方法。针对在复杂恶劣环境下卸压孔的精确识别问题,设计了融合图像尺寸调节和多阶段训练模式的卸压孔图像样本扩充SinGAN模型,引入多层特征融合优化的FasterRCNN,构建了基于改进SqueezeNet轻量级网络架构的孔位识别模型,以实现卸压孔位的准确快速识别;针对钻具姿态精确感知问题,提出了基于改进梯度下降法算法优化无迹卡尔曼滤波的惯性测量单元(Inertial Measurement Unit,IMU)初始对准方法,设计了多个IMU的空间阵列布局方式,研究了基于BP神经网络的钻具姿态误差补偿方法,旨在提高钻具姿态的解算精度,实现精准钻孔卸压;针对复杂地质环境下钻进工况的精确检测问题,搭建了煤矿井下随钻测量无线电磁传输系统架构,探讨了微弱电磁波信号自适应调制和随钻高速双向电磁传输技术原理,研究了孔底地质参数、几何参数和工程参数的测量原理和实现过程;针对钻进系统运行状态识别问题,构建了钻进信号时域、频域、时频域的多域特征和深度网络高级特征提取架构,提出了钻进系统关键零部件健康状态评估和故障诊断技术,构建了基于改进蝙蝠优化长短期记忆网络的卡钻风险因子预测模型,实现对卸压钻具卡钻状态的准确预测;针对钻进系统的精确控制问题,分析了钻进系统的液压系统工作原理,构建了考虑煤岩性状的钻进系统精确控制方案,探讨了基于转矩和位置的钻进系统最优控制参数求解原理,旨在实现钻进回转系统和给进系统的智能协同控制和并行作业。

智能人机交互在有人太空探索中的应用与展望

近年来,空间机器人技术发展迅速,国内外已有诸多空间站机器人、深空探测机器人的应用实例。未来有人太空探索过程当中,人与众多智能机器人协同开展任务将成为必然趋势。对国内外空间协作机器人应用实例进行了总结,在分析人机协作机制的基础上,提出了空间人机交互的关键技术,包含人体检测与跟踪、手势识别与分类、动作识别与意图预测、低重力环境下的人机协同控制等,对上述关键技术的难点与研究进展进行了分析综述,并对后续的发展方向进行了展望。

复杂人机共融场景中人体姿态识别及避碰策略综述

智能机器人与人类智慧的融合,即人机协作共融,已经实现了将机器人的机械优势和人类的高级认知能力集中于同一个工作架构之中,能够在复杂环境中协同作业,从而提高效率。针对复杂的人机共融场景,特别是机器人在诸如光线条件变化、背景干扰以及运动过程,对比总结了基于机器视觉的人体姿态识别方法和基于机器学习的避碰策略,详细比较各类方法的研究现状及应用,并探讨了基于深度学习的目标识别和避碰方法的发展及应用。

基于双混联机器人协同运动控制的薄壁件镜像铣削研究

为提高薄壁件镜像铣削过程中的加工质量,本文提出一种针对双混联机器人镜像铣削的协同运动控制策略。首先,结合机器人运动控制特点,研发出一种双CPU主从控制架构的开放式数控系统,以实现人机交互和运动控制;然后,为实现双机协同运动并提高薄壁件加工质量,在数控系统中集成了4大关键技术,分别是双机器人的镜像路径生成、同步速度规划、协同运动学和运动误差实时补偿;最后,基于自主开发的集成式镜像铣削数控系统,开展了单点和多点支撑、双机协同与非协同加工的平面薄壁件槽铣削试验,多点支撑的平面铣削试验,大曲率路径高速协同运动试验与曲面薄壁件槽铣削加工试验。试验结果表明,提出的协同运动控制策略能够保证双机器人具有较高的同步位置精度。此外,多点支撑方式在保证薄壁件铣削壁厚的同时,还可提高工件铣削的颤振稳定性。

面向任务的机器人动作泛化研究

动作泛化是机器人被期望具备的功能,要求机器人不仅能重复人类示教的灵巧动作,还可以根据任务目标生成新动作,在小批量、多种类的生产要求下具有广阔的应用场景.针对面向任务需求快速学习和灵活泛化的目标,设计开发了机器人动作泛化框架.首先,进行少量示教,使用概率运动基元(probabilistic movement primitives,ProMP)构建动作的轨迹分布,以较少参数概括示教信息.其次,使用期望最大(expected maximum,EM)算法迭代估计模型参数,引入先验信息补充模型以保证稳定性.研究先验信息对泛化效果的影响,确定了合适的先验参数;研究傅里叶基函数对周期轨迹的拟合效果,提出轨迹分割与滤波方法以保证拟合精确平滑.利用模型的概率分布性质,将动作的概率范围收缩到特定位置,并进行整条轨迹的时间尺度缩放与空间位置变换,生成符合泛化要求的轨迹.最后,搭建了一个六自由度机器人喷涂场景进行动作泛化实验.结果表明:该框架能够快速生成多种泛化轨迹.单自由度轨迹学习平均消耗1.924 s,轨迹生成平均需0.004 s;六自由度轨迹学习平均消耗35.608 s,轨迹生成平均需0.011s.