Functional Redesign of Mantis 2.0, a Hybrid Leg-Wheel Robot for Surveillance and Inspection

The paper discusses the redesign of the second version of the Mantis hybrid leg-wheel mobile robot, conceived for surveillance and inspection tasks in unstructured indoor and outdoor environments. This small-scale ground mobile robot is characterized by a main body equipped with two front actuated wheels, a passive rear axle and two rotating legs. Motion on flat and even ground is purely wheeled in order to obtain high speed, high energetic efficiency and stable camera vision; only in case of obstacles or ground irregularities the front legs realize a mixed leg-wheel locomotion to increase the robot climbing ability; in particular, the outer profile of the legs, inspired by the praying mantis, is specially designed to climb square steps. The multibody simulations and the experimental tests on the first prototype have shown the effectiveness of the mixed leg-wheel locomotion not only for step climbing, but also on uneven and yielding terrains. Nevertheless, extensive experimental tests have shown that the front wheels may slip in the last phase of step climbing in case of contact with some materials. In order to overcome this problem, the leg design has been modified with the introduction of auxiliary passive wheels, which reduce friction between legs and step upper surface; these wheels are connected to the legs by one-way bearings, in order to rotate only when they are pulled by the front wheels, and remaining locked when they have to push forward the robot. The influence of the auxiliary wheels on the front wheels slippage is investigated by means of theoretical analysis and multibody simulations.     

Laser Pose Estimation and Tracking Using Fuzzy Extended Information Filtering for an Autonomous Mobile Robot

This paper presents methodologies and techniques for posture estimation and tracking of an autonomous mobile robot (AMR) using a laser scanner with at least three retro-reflectors. A three-point laser triangulation method is presented to find an initial posture of the robot and then a fuzzy extended information filtering (FEIF) method is used to improve the accuracy of the robot’s posture estimation. With the odometric information from the driving wheels, a FEIF-based posture tracking algorithm is proposed to continuously keep trace of the robot’s posture at slow speeds. Simulation and experimental results are conducted to show the efficacy and usefulness of the proposed methods.     

基于Cortex-M4的多传感器组合导航系统设计

针对移动机器人导航定位需求,设计了一种低成本、便携式的多传感器组合导航系统.采用ARM Cortex-M4内核的STM32F407处理器为电路核心,利用微惯性测量单元(MIMU)、全球卫星定位系统(GPS)以及电子罗盘作为导航数据源,进行了相关的硬件设计;采用移动导航平台进行实验验证,通过实测数据进行分析,结果表明:该组合导航系统可以有效提高导航精度,使其导航定位经纬度误差稳定在1.0m左右,航向角误差稳定在0.8°以内,对移动机器人导航有一定的参考价值.     

Visual Contact Angle Estimation and Traction Control for Mobile Robot in Rough-Terrain

For a wheeled mobile robot traversing a rough terrain, knowledge of terrain variables is very important for developing effective traction control algorithms. A key variable of the most prevalent information that should be taken into account is the contact angle between the robot wheels and the ground. This paper presents an algorithm for visual estimation of wheel-ground contact angle on uneven terrain. We call it the Visual Contact Angle Estimation (VCAE) method. Given a white LED light source, a monocular camera is required to be mounted on the front wheel and the rear wheel respectively, with a field of view containing the wheel-ground contact interface and its location relative to the wheel is known and fixed during robot travel. This arrangement is used to measure the contact angle with an edge detection strategy. Then a traction control methodology based on multi-objective optimization is presented. This exploits the wheel-ground contact angle obtained in the VCAE system to improve ground traction and reduce power consumption. Simulation and experiment results for a wheeled robot traversing a symmetrical uneven testbed demonstrate the effectiveness of the VCAE method and traction control algorithms.     

Estimation of attraction domains in wheeled robot control

Considered is the control design problem for planar motion of a wheeled robot. The mathematical model of the robot accounts for kinematic relationships between the velocity of a given point of chassis referred to as the reference point, orientation of the chassis, and control. Among the kinematic relations is the requirement that each of the four wheels perform a slip-free motion. The rear wheels are assumed to be driving while the front wheels are responsible for the rotation of the chassis. The control objective is to place the reference point in the prespecified trajectory and to stabilize the motion of the reference point along the prespecified trajectory. The trajectory consists of line segments and circular arcs. In the mathematical model under consideration, the current curvature of the trajectory of the reference point is taken as control; it is related to the steering angle of the front wheels by a simple algebraic expression. The control is subject to two-sided constraints due to limitations on the steering angle of the front wheels. For the control law proposed, the attraction domain in the space “distance to the trajectory—orientation” is analyzed. For the initial conditions from this domain, the system is guaranteed to hit a trajectory with given index of exponential stability.     

基于Arduino DUE的防跟踪遇袭报警系统

介绍了一种集被动式与主动式于一体的防跟踪遇袭报警系统,实现跟踪、遇袭监测,鸣笛警告以及发送定位报警信息等功能.系统以Arduino DUE作为微处理控制系统,热释电红外传感器作为前端传感单元,全球定位系统(GPS)模块和全球移动通信系统/通用无线分组业务(GSM/GPRS)模块作为定位和通信模块.介绍了总体方案设计,同时阐述了系统模块设计,包括硬件设计和软件算法,给出了测试结果.经多次实验:系统反应灵敏,抗干扰能力强,安全可靠,同时具备携带方便的特点.     

Design and manufacturing a novel screw-in-pipe inspection robot with steering capability

In this article, a novel in-pipe inspection robot is designed and manufactured in Kharazmi University KharazmPipeBot. This robot is able to move through any pipeline with a predefined diameter range with a variable pitch rate and report any desired data within the pipe with the aid of the installed camera. To achieve the highest stability of the robot through the pipe, the robot's movement is based on the screw locomotion protocol provided by the aid of its rotor and stator. A simple suspension is designed for three legs of the robot by installing a passive prismatic joint equipped with a spring for each leg to provide a smoother movement for the robot chassis. The main novelty of the robot is adding an extra controlling actuator for the robot which is the steer of the front wheels. This input can control the pitch rate of the robot movement and consequently the spiral track of the wheels can be actively managed. This importance lets us to bypass the probable obstacles attached to the inner wall of the pipes. A brief presentation of the robot model is delivered. Afterward, to verify the claimed novelties of the system, a prototype of the robot is manufactured in Kharazmi University and the efficiency of the robot is demonstrated by conducting some initial experimental tests. It is shown that the robot can move with a variable pitch rate through the wall and pass a detected obstacle accordingly.     

Humanoid odometric localization integrating kinematic,inertial and visual information

We present a method for odometric localization of humanoid robots using standard sensing equipment, i.e., a monocular camera, an inertial measurement unit (IMU), joint encoders and foot pressure sensors. Data from all these sources are integrated using the prediction-correction paradigm of the Extended Kalman Filter. Position and orientation of the torso, defined as the representative body of the robot, are predicted through kinematic computations based on joint encoder readings; an asynchronous mechanism triggered by the pressure sensors is used to update the placement of the support foot. The correction step of the filter uses as measurements the torso orientation, provided by the IMU, and the head pose, reconstructed by a VSLAM algorithm. The proposed method is validated on the humanoid NAO through two sets of experiments: open-loop motions aimed at assessing the accuracy of localization with respect to a ground truth, and closed-loop motions where the humanoid pose estimates are used in real-time as feedback signals for trajectory control.     

Dynamic path tracking control of a vehicle on slippery terrain

This paper deals with accuracy and reliability for the path tracking control of a four wheel mobile robot with a double-steering system when moving at high dynamics on a slippery surface. An extended kinematic model of the robot is developed considering the effects of wheel–ground skidding. This bicycle type model is augmented to form a dynamic model that considers an actuation of the four wheels. Based on the extended kinematic model, an adaptive and predictive controller for the path tracking is developed to drive the wheels front and rear steering angles. The resulting control law is combined with a stabilization algorithm of the yaw motion which modulates the actuation torque of each four wheels, on the basis of the robot dynamic model. The global control architecture is experimentally evaluated on a wet grass slippery terrain, with speeds up to 7 m/s. Experimental results demonstrate enhancement of tracking performances in terms of stability and accuracy relative to the kinematic control.     

基于GPS/GIS/GSM的移动机器人定位技术研究

本文论述了基于GPS/GIS/GSM技术的移动机器人定位系统的主要内容和关键技术。采用了GPS接收机和地图匹配的联合方法对机器人进行定位,并利用无线移动通信系统进行信息的收发和对机器人的控制。实验表明,该方案具有良好的实时性和定位精度。     

精梳棉车间棉卷自动运输系统开发

连接生产工序参与生产流程是移动机器人真正应用于车间提高生产效率的关键。根据精梳棉车间的工作环境,研制了基于开源机器人操作系统（ROS）的棉卷自动运输机器人系统。移动机器人应用激光传感器采集车间环境数据,采用Gmapping算法构建精梳棉车间地图,应用经典A^*和人工势场法融合的混合路径规划算法自主规划行驶路径,并采用视觉二维码技术实现生产设备前的精确定位。测试结果显示,在1 m/s安全速度下,棉卷运输机器人定位误差与航向偏差的平均绝对误差（MAE）分别小于5 cm和07°,完全满足在精梳棉车间内自主避障运动以及在生产设备前精确定位的实际作业需求。     

Consistent triangulation for mobile robot localization using discontinuous angular measurements

Localization is a fundamental operation for the navigation of mobile robots. The standard localization algorithms fuse external measurements of the environment with the odometric evolution of the robot pose to obtain its optimal estimation. In this work, we present a different approach to determine the pose using angular measurements discontinuously obtained in time. The presented method is based on an Extended Kalman Filter (EKF) with a state-vector composed of the external angular measurements. This algorithm keeps track of the angles between actual measurements from robot odometric information. This continuous angular estimation allows the consistent use of the triangulation methods to determine the robot pose at any time during its motion. The article reports experimental results that show the localization accuracy obtained by means of the presented approach. These results are compared to the ones obtained applying the EKF algorithm with the standard pose state-vector. For the experiments, an omnidirectional robotic platform with omnidirectional wheels is used.     

Development of a stair-climbing mobile robot with legs and wheels

This paper deals with the development of a stair-climbing mobile robot with legs and wheels. The main technical issues in developing this type of robot are the stability and speed of the robot while climbing stairs. The robot has two wheels in the front of the body to support its weight when it moves on flat terrain, and it also has arms between the wheels to hook onto the tread of stairs. There are two pairs of legs in the rear of the body. Using not only the rorational torque of the arms and the wheels, but also the force of the legs, the robot goes up and down stairs. It measures the size of stairs when going up and down the first step, and therefore the measurement process does not cause this robot to lose any time. The computer which controls the motion of the robot needs no complicated calculations as other legged robots do. The mechanism of this robot and the control algorithm are described in this paper. This robot will be developed as a wheelchair with a stair climbing mechanism for disabled and elderly people in the near future. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

Localization of a wheeled mobile robot by sensor data fusion based on a fuzzy logic adapted Kalman filter

Accurate estimates of mobile robot location, if available, can be used to improve the performance of a vehicle dynamics control system. To this purpose, the data provided by odometric and sonar sensors are here fused together by means of an extended Kalman filter, providing robot position and orientation estimates at each sampling instant. To cope with the tracking of long trajectories, the performance of the filter is improved by introducing an on-line fuzzy-rule-based adaptation scheme.     

Motion planning for a landmine-detection robot

This article describes a landmine-detection system that contains a landmine-detection mobile robot and a following mobile robot. In this system, the landmine-detection robot goes ahead, and uses a landmine detector and a GPS module to find a landmine, records the coordinates of its location, and transmits these coordinates to the following mobile robot via a wireless RF interface. The following robot can record the location and orientation of the landmine-detection robot and all the landmines in the region. The following robot moves close to the landmine, and programs a path to avoid obstacles and landmines automatically. The driving system of the landmine-detection mobile robot uses a microprocessor dsPIC 30F4011 as the core, and controls two DC servomotors to program the motion path. The user interface of the landmine-detection robot and the following robot uses Borland C₊₊ Builder language to receive the location data. In the experimental results, the landmine-detection robot records the location of landmines using a GPS module, and transmits the locations to the following robot via a wireless RF interface. The following robot avoids the landmines, and improves the safety of people or materials being carried through the landmine area.     

基于机器人运动模型的EKF-SLAM算法改进

针对两轮驱动机器人运动模型定向误差的累积问题,提出改进的三轮驱动机器人运动模型,对EKF-SLAM算法的一致性进行改进;该模型通过对机器人车轮线速度的解耦运算,将机器人运动过程中的旋转角速度提取出来并作为系统的控制输入之一,从而可以直接得到各个控制时间间隔内的机器人姿态角变化,很好地避免了机器人定向误差的累积;最后,基于归一化估计方差的检验标准进行实验,验证了三轮驱动机器人运动模型有效提高了EKF-SLAM算法的一致性。     

基于GPS定位的太阳能板自动追光系统设计

为了使太阳能电池板更好地吸收太阳光,提出了一种基于GPS定位的太阳能电池板自动追光系统设计。通过使用GPS定位功能为跟踪装置所在位置提供精确的经纬度和时间,通过视日运动轨迹法计算当前时间太阳的高度角和方位角,以及利用高度/方位传感器获得当前太阳能电池板转动的仰角和旋转角度,根据间歇跟踪法计算出当前跟踪偏差值,并通过主控制器输出相应的电脉冲信号。以控制步进电机的跟踪装置驱动太阳能电池板。综上所述,通过实现主要参数包括地理位置与授时,跟踪偏差以及太阳的位置,从而能够准确提高太阳光的高精度自动跟踪,以及也增加了太阳能电池板自动追光系统的稳定性,且实用性更强。     

Smooth control of an articulated mobile robot with switching constraints

The paper describes a smooth controller of an articulated mobile robot with switching constraints. The use of switching constraints associated with grounded/lifted wheels is an effective method of controlling various motions; e.g. the avoidance of a moving obstacle. A model of an articulated mobile robot that has active and passive wheels and active joints with switching constraints is derived. A controller that accomplishes the trajectory tracking of the robot’s head and subtasks using smooth joint input is proposed on the basis of the model. Simulations and experiments are presented to show the effectiveness of the proposed controller.     

无人机协助下基于SR-CKF的无线传感器网络节点定位研究

针对无线传感器网络（WSN）节点的实际应用场合大多数分布在复杂的三维地形，并且当无线传感器网络分布规模达到一定程度时，对每一个传感器节点装载GPS模块来实现节点定位不切实际的情况，提出了一种无人机（UAV）协助下利用极大似然估计法（MLE）对未知节点进行初步定位，引入平方根容积卡尔曼滤波（SR-CKF）算法对未知节点进行精确定位，采用阈值选择的更新策略来减小非线性因素的影响。仿真结果表明:所提出的UAV-WSN-MLE-SRCKF协作定位方式实现了三维地形中未知传感器节点的定位估计，大量减少了装载GPS模块所带来的成本，同时也提高了定位精度和稳定性。