Multimodal sensing and active continuous closed‐loop feedback for achieving reliable manipulation in the outdoor physical world

The use of field robots can greatly decrease the amount of time, effort, and associated risk compared to if human workers were to carryout certain tasks such as disaster response. However, transportability and reliability remain two main issues for most current robot systems. To address the issue of transportability, we have developed a lightweight modularizable platform named AeroArm. To address the issue of reliability, we utilize a multimodal sensing approach, combining the use of multiple sensors and sensor types, and the use of different detection algorithms, as well as active continuous closed‐loop feedback to accurately estimate the state of the robot with respect to the environment. We used Challenge 2 of the 2017 Mohammed Bin Zayed International Robotics Competition as an example outdoor manipulation task, demonstrating the capabilities of our robot system and approach in achieving reliable performance in the fields, and ranked fifth place internationally in the competition.     

Accurate estimation of environment parameters from ultrasonic data

This paper describes a robust and accurate ultrasonic sensing system for a mobile robot. The system continuously updates a local map of the environment in which obstacles are represented by straight lines or points in a robot centered coordinate frame. The presented algorithms use a Kalman filter for the reduction of the noise in the ultrasonic data and use a systematical error correction (‘bundle correction’) to reduce the uncertainty in obstacle direction. Experiments are carried out in simulation and with a real mobile robot system. Results show that the accuracy with which line parameters can be estimated is in the order of 1 degree for the orientation and about 2 cm for the position. The effect of the bundle correction is significant and maximal when the robot approaches walls under a small angle.     

Cleanbot-Ⅰ擦窗机器人的智能化技术

本文简述了擦窗机器人智能化的主要概念．介绍了Ｃｌｅａｎｂｏｔ－Ｉ擦窗机器人系统的结构特点、主要组成部分、工作原理，操作方式，未知局部环境的模型建立，各类内、外传感器的结构、原理和信息融合技术，以及路径规划的方法．     

Real-time accurate hand path tracking and joint trajectory planning for industrial robots (I)

Previously, researchers raised the accuracy for a robot’s hand to track a specified path in Cartesian space mainly through increasing the number of knots on the path and the number of the path’s segments, which results in the heavier online computational burden for the robot controller. Aiming at overcoming this drawback, the authors propose a new kind of real-time accurate hand path tracking and joint trajectory planning method. Through selecting some extra knots on the specified hand path by a certain rule and introducing a sinusoidal function to the joint displacement equation of each segment, this method can greatly raise the path tracking accuracy of robot’s hand and does not change the number of the path’s segments. It also does not increase markedly the computational burden of robot controller. The result of simulation indicates that this method is very effective, and has important value in increasing the application of industrial robots. Foundation item: Foundation of the Robotics Laboratory, Chinese Academy of Sciences (No. RL200002) Biography of the first author: TAN Guan-zheng, Dr., professor, born in Oct. 1962, majoring in artificial intelligence, robotics and automation.     

Planetary exploration by robotic aerovehicles

Planetary aerobots are a new type of telerobotic science platform that can fly and navigate in a dynamic 3-dimensional atmospheric environment, thus enabling the global in situ exploration of planetary atmospheres and surfaces. Aerobots are enabled by a new concept in planetary balloon altitude control, developed at JPL, which employs reversible-fluid changes to permit repeated excursions in altitude. The essential physics and thermodynamics ofreversible-fluid altitude control have been demonstrated in a series of altitude-control experiments conducted in the Earth's atmosphere, which are described. Aerobot altitude-control technology will be important in the exploration of seven planets and satellites in our solar system. Three of these objects—Venus, Mars, and the Saturnian satellite Titan—have accessible solid surfaces and atmospheres dominated by the dense gases nitrogen or carbon dioxide. They will be explored with aerobots using helium or hydrogen as their primary means of buoyancy. The other four planets—Jupiter, Saturn, Uranus, and Neptune—have deep atmospheres that are predominantly hydrogen. It may be possible to explore these atmospheres with aerobots inflated with atmospheric gas that is then radiatively heated from the hotter gaseous depths below. To fulfill their potential, aerobots to explore the planets will need autonomous state estimators to guide their observations and provide information to the altitude-control systems. The techniques of acquiring these data remotely are outlined. Aerobots will also use on board altitude control and navigation systems to execute complex flight paths including descent to the surface and exploiting differential wind velocities to access different latitude belts. Approaches to control of these systems are examined. The application of aerobots to Venus exploration is explored in some detail: The most ambitious mission described, the Venus Flyer Robot (VFR), would have the capability to make repeated short excursions to the high-temperature surface environment of Venus to acquire data and then return to the Earth-like upper atmosphere to communicate and recool its electronic systems. Finally a Planetary Aerobot Testbed is discussed which will conduct Earth atmospheric flights to validate autonomous-state-estimator techniques and flight-path-control techniques needed for future planetary missions.     

A real-time planning algorithm for obstacle avoidance of redundant robots

A computationally efficient, obstacle avoidance algorithm for redundant robots is presented in this paper. This algorithm incorporates the neural networks and pseudodistance function D _p in the framework of resolved motion rate control. Thus, it is well suited for real-time implementation. Robot arm kinematic control is carried out by the Hopfield network. The connection weights of the network can be determined from the current value of Jacobian matrix at each sampling time, and joint velocity commands can be generated from the outputs of the network. The obstacle avoidance task is achieved by formulating the performance criterion as D _p>d _min (d _min represents the minimal distance between the redundant robot and obstacles). Its calculation is only related to some vertices which are used to model the robot and obstacles, and the computational times are nearly linear in the total number of vertices. Several simulation cases for a four-link planar manipulator are given to prove that the proposed collision-free trajectory planning scheme is efficient and practical.     

一种基于光照变化补偿的颜色识别方法

针对RoboCup中型组足球机器人比赛中光照变化会使颜色发生色彩漂移,影响颜色识别的准确性问题,提出一种基于光照变化补偿的颜色识别方法。该方法首先利用球的历史信息预测球的位置,然后在较小的区域内用基于色调直方图反向投影的方法找到球后,通过球的亮度直方图的变化计算出光照变化率,用于动态补偿颜色查找表。实验表明该方法能提高颜色识别的光照自适应性。     

Robust pole assignment for incomplete nonlinear decoupling applied to robots

A robustness analysis and synthesis for incomplete nonlinear decoupling for a class of nonlinear systems is discussed. Rigid and elastic-joint robot models belong to this class. For the elastic case, a transformation facilitates the robustness analysis under a weak assumption. Charts with H ₁- and H - norms of closed-loop disturbance transfer functions of the nonlinear-decoupled system are presented for a robust pole assignment.     

An Intelligent Robust Tracking Control for a Class of Electrically Driven Mobile Robots

This paper addresses the problem of designing robust tracking control for a class of uncertain wheeled mobile robots actuated by brushed direct current motors. This class of electrically‐driven mechanical systems consists of the robot kinematics, the robot dynamics, and the wheel actuator dynamics. Via the backstepping technique, an intelligent robust tracking control scheme that integrates a kinematic controller and an adaptive neural network‐based (or fuzzy‐based) controller is developed such that all of the states and signals of the closed‐loop system are bounded and the tracking error can be made as small as possible. Two adaptive approximation systems are constructed to learn the behaviors of unknown mechanical and electrical dynamics. The effects of both the approximation errors and the unmodeled time‐varying perturbations in the input and virtual‐input weighting matrices are counteracted by suitably tuning the control gains. Consequently, the robust control scheme developed here can be employed to handle a broader class of electrically‐driven wheeled mobile robots in the presence of high‐degree time‐varying uncertainties. Finally, a simulation example is given to demonstrate the effectiveness of the developed control scheme.     

一种引入通信的多移动机器人编队方法*

提出以视觉跟踪为基础并引入通信进行多机器人的编队控制方法,根据需要编写了一种新的通信协议,采用闭环l-Φ实现编队算法.这种多机器人编队控制避免了视觉系统的局限,能够更好地在复杂未知环境中协作完成任务,解决了编队控制的无反馈和实时性不高的问题,使得机器人能够准确迅速地进行跟踪和通信编队,一起顺利达到目标点.试验结果证明了该方法的有效性.