Vision-Based Obstacle Detection and Avoidance: Application to Robust Indoor Navigation of Mobile Robots

We propose a more practical and efficient method for obstacle detection and avoidance. In this paper, a robot detects obstacles based on the projective invariants of stereo cameras, fuses this information with two-dimensional scanning sensor data, and finally builds up a more informative and conservative occupancy map. Although this approach is not supposed to recognize the exact shape of the obstacles, this shortcoming is overcome in the actual application by its fast calculation time and robustness against the illumination conditions. To avoid detected obstacles, a new reactive obstacle avoidance strategy is also presented. To evaluate the proposed method, we applied it to the mobile robot iMARO-III. In this test, iMARO-III has succeeded in long-term operation for 7 days continuously without any intervention of engineers and any collision in the real office environment.     

Differential GPS and odometry-based outdoor navigation of a mobile robot

This paper describes outdoor navigation for a mobile robot by using differential GPS (DGPS) and odometry in a campus walkway environment. The robot position is estimated by fusion of DGPS and odometry. The GPS receiver measures its position by radio waves from GPS satellites. The error of GPS measurement data increases near high buildings and trees because of multi-path and forward diffractions. Thus, it is necessary to pick up only accurate DGPS measurement data when the robot position is modified by fusing DGPS and odometry. In this paper, typical DGPS measurement data observed near high buildings and trees are reported. Then, the authors propose a novel position correction method by fusing GPS and odometry. Fusion of DGPS and odometry is realized using an extended Kalman filter framework. Moreover, outdoor navigation for a mobile robot is accomplished by using the proposed correction method.     

Omnidirectional vision-based ego-pose estimation for an autonomous in-pipe mobile robot

This paper describes the omnidirectional vision-based ego-pose estimation method of an in-pipe mobile robot. An in-pipe mobile robot has been developed for inspecting the inner surface of various pipeline configurations, such as the straight pipeline, the elbow and the multiple-branch. Because the proposed in-pipe mobile robot has four individual drive wheels, it has the ability of flexible motions in various pipelines. The ego-pose estimation is indispensable for the autonomous navigation of the proposed in-pipe robot. An omnidirectional camera and four laser modules mounted on the mobile robot are used for ego-pose estimation. An omnidirectional camera is also used for investigating the inner surface of the pipeline. The pose of the in-pipe mobile robot is estimated from the relationship equation between the pose of a robot and the pixel coordinates of four intersection points where light rays that emerge from four laser modules intersect the inside of the pipeline. This relationship equation is derived from the geometry analysis of an omnidirectional camera and four laser modules. In experiments, the performance of the proposed method is evaluated by comparing the result of our algorithm with the measurement value of a specifically designed sensor, which is a kind of a gyroscope.     

Navigation Using an Environmental Magnetic Field for Outdoor Autonomous Mobile Robots

GPS and laser range finders are generally utilized in current robot navigation. However, information from the magnetic field and electronic compass is not, since it is dynamically changing at every position. In this paper, the relationship between the intensity of a magnetic field in the environment and its position is taken into account by utilizing a three-axis magnetic sensor to scan the magnetic field in the environment to build a database. The mobile robot navigates by performing trajectory tracking based on the database. The experimental results show that by applying the proposed method, the mobile robot is able to navigate in an outdoor environment with reliable accuracy.     

Development of a remote fault diagnosis system applicable to autonomous mobile robots

In this paper, the novel idea of using an autonomous mobile robot as a virtual local network is proposed as a system for the remote diagnosis of faults of a mobile robot. There are two primary points in this research. First, an objective of the research is to achieve a system with good expandability, i.e. one in which the diagnosis can be achieved whether there are many or few mobile robots to be diagnosed. To accomplish this goal by simply changing some system settings, a simple network management protocol (SNMP) structure is utilized. Within the developed diagnosis system, a mobile robot is taken as one of the management objects of the network management system (NMS). The SNMP, which is widely used in the NMS, is applied and adapted to the developed system as the communication protocol for exchanging diagnosis information. Moreover, by taking advantage of the active moving and sensing ability of autonomous mobile robots, an effective method of fault inference called a 'run-test' is discussed. By using this method, the accuracy of the diagnoses is improved. In the best-case scenario, the certainty of an accurate diagnosis increased from 20% (without using the 'run-test') to 93%. On the other hand, in some cases, the accuracy of the diagnosis results barely improved. On average, among the cases discussed in this paper, the accuracy of the diagnosis results improved about 2.1 times by the proposed method.     

Mobile Robot Control Architecture for Reflexive Avoidance of Moving Obstacles

In this paper, a three-layer (deliberative, sequencing, reflexive) architecture is adopted and the structure of the reflexive layer is discussed. The objective of this architecture is to extract the basic actions that require hard-real-time execution from non-real-time-allowed behaviors by separating them into the reflexive and sequencing layers, respectively. The reflexive layer consists of resources, actions, an action coordinator and a motion controller. To guarantee the hard-real-time execution, a set of simple actions and an action coordinator are designed using the functions provided in the RTAI (Real-Time Application Interface for Linux) environment. Also, an obstacle avoidance algorithm based upon data from a laser range scanner is developed. For the purpose of avoiding a moving obstacle, which is treated as a moving circle through segmentation and circularization processes, a Kalman filter is developed to estimate the distance and the heading of the center of the moving circle. The effectiveness and real-time characteristics of the proposed reflexive layer and the developed algorithms are examined through experiments using scattered stand-still obstacles as well as a moving human.     

室外自主移动机器人AMOR的导航技术

在非结构化环境,移动机器人行驶运动规划和自主导航是非常挑战性的问题。基于实时的动态栅格地图,提出了一个快速的而又实效的轨迹规划算法,实现机器人在室外环境的无碰撞运动导航。AMOR是自主研发的室外运动移动机器人,它在2007年欧洲C-ELROB大赛中赢得了野外自主侦察比赛的冠军。它装备了SICK的激光雷达,用来获取机器人运动前方的障碍物体信息,建立实时动态的环境地图。以A*框架为基础的改造算法,能够在众多的路径中快速地找到最佳的安全行驶路径,实现可靠的自主导航。所有的测试和比赛结果表明所提方案是可行的、有效的。     

Characteristics of obstacle avoidance behavior in a linear cluster robotic system

Robotic systems are used to perform maintenance functions in hazardous environments usually with the fundamental objective of reducing, or eliminating, human worker exposure to dangers. An obvious thought is how might Internet capabilities be used in such operations. This paper considers this general idea in terms of current research accomplishments, and the practical needs, constraints and concepts that are associated with the notion of using the Internet to reduce costs and enhance overall performance in robotic maintenance in hazardous environments.     

Localization and guidance with an embarked camera on a mobile robot

In this paper, a new method using the Hough transform to correct the drift of the mobile robot CESA is presented. The corrections are made by direct observation. As an illustration, an algorithm implemented is detailed, and experiment results for CESA navigation in our laboratory's corridor and trajectory generation are given.     

Hybrid autonomous control for multi mobile robots

Reinforcement learning can be an adaptive and flexible control method for autonomous system. It does not need a priori knowledge; behaviors to accomplish given tasks are obtained automatically by repeating trial and error. However, with increasing complexity of the system, the learning costs are increased exponentially. Thus, application to complex systems, like a many redundant d.o.f. robot and multi-agent system, is very difficult. In the previous works in this field, applications were restricted to simple robots and small multi-agent systems, and because of restricted functions of the simple systems that have less redundancy, effectiveness of reinforcement learning is restricted. In our previous works, we had taken these problems into consideration and had proposed new reinforcement learning algorithm, 'Q-learning with dynamic structuring of exploration space based on GA (QDSEGA)'. Effectiveness of QDSEGA for redundant robots has been demonstrated using a 12-legged robot and a 50-link manipulator. However, previous works on QDSEGA were restricted to redundant robots and it was impossible to apply it to multi mobile robots. In this paper, we extend our previous work on QDSEGA by combining a rule-based distributed control and propose a hybrid autonomous control method for multi mobile robots. To demonstrate the effectiveness of the proposed method, simulations of a transportation task by 10 mobile robots are carried out. As a result, effective behaviors have been obtained.     

Path-following control of a mobile robot in the presence of actuator constraints

In this paper, we present a control law for a non-holonomic mobile robot that achieves path following. In the path-following problem, the objective is to control the angular velocity of the robot so that the robot tracks a given reference trajectory. In this paper, we propose a control law that achieves path following in the presence of a constraint on the angular velocity. By applying the proposed control law, the robot can track the reference trajectory even if the distance from the initial position of the robot and the reference trajectory is arbitrary large. Further, we extend the control law so that the linear velocity of the robot becomes small when the robot passes through corners. By using the control algorithm, we can prevent the angular velocity of the robot becoming extremely large when the robot passes through corners. Numerical examples are provided to illustrate the effectiveness of the proposed methods.     

A 10-gram vision-based flying robot

We aim at developing ultralight autonomous microflyers capable of freely flying within houses or small built environments while avoiding collisions. Our latest prototype is a fixed-wing aircraft weighing a mere 10 g, flying around 1.5 m/s, and carrying the necessary electronics for airspeed regulation and lateral collision avoidance. This microflyer is equipped with two tiny camera modules, two rate gyroscopes, an anemometer, a small microcontroller and a Bluetooth radio module. In-flight tests were carried out in a new experimentation room specifically designed for easy changing of surrounding textures.     

自主移动机器人自适应室外道路检测

目的 为降低室外自主移动机器人视觉导航中遇到的阴影、裂纹及道路边界不规则造成的道路检测算法不鲁棒性,提出一种每帧灰度阈值可调的快速自适应道路检测方法。方法 先采用2维离散小波进行道路图像分解与重构,比较各级小波重构后的近似道路图像,确定出不影响“路-非路”灰度二分类的最佳分辨率等级;在低分辨率尺度空间中,用灰度类间最大方差和类内最小方差共同构造适应度函数,采用改进的遗传算法对各帧道路图像进行阈值自适应分割,找到准确的道路边界,最近两边界中心位置即机器人行驶方向。采用小型陆地自主车作为研究平台,并在卡耐基梅隆大学（CMU）提供的室外移动机器人道路视频中进行算法测试。结果 本文方法能够在具有阴影、裂纹、光照度变化的道路条件下鲁棒分割出道路边界,机器人可以平均30 km/h的速度在有较严重阴影干扰的校园道路上行驶,视觉系统的处理速度平均可达到20 ms/帧。结论 本文方法比传统的灰度直方图分割法表现出更强的环境自适应性,可实现较为鲁棒的室外道路检测,并可作为室外自主移动机器人非结构化道路检测的一种鲁棒性较强的方法加以推广。     

An investigation of climbing up stairs for an inchworm robot

In this research, an inchworm-type robot is being developed for the purpose of rescue missions. This robot has the ability to traverse obstacles such as stairs with fewer joints than legged robots. A self-standing inchworm robot, which has five links and four joints, is produced for trial purposes. In order for this robot to be able to climb up stairs, the kinematical analysis and the development of the program were investigated. As a result, the effectiveness of this robot is confirmed experimentally.     

Vision-based hybrid control scheme for autonomous parking of a mobile robot

This paper presents a novel vision-based hybrid controller for parking of mobile robots. Parking or docking is an essential behavioral unit for autonomous robots. The proposed hybrid controller comprises a discrete event controller to change the direction of travel and a pixel error-driven proportional controller to generate motion commands to achieve the continuous motion. At the velocity control level, the robot is driven using a built-in PID control system. The feedback system uses image plane measurements in pixel units to perform image-based visual servoing (IBVS). The constraints imposed due to the non-holonomic nature of the robot and the limited field of view of the camera are taken into account in designing the IBVS-based controller. The controller continuously compares the current view of the parking station against the reference view until the desired parking condition is achieved. A comprehensive analysis is provided to prove the convergence of the proposed method. Once the parking behavior is invoked, the robot has the ability to start from any arbitrary position to achieve successful parking given that initially the parking station is in the robot's field of view. As the method is purely based on vision the hybrid controller does not require any position information (or localization) of the robot. Using the Pioneer 3AT robot, several experiments are carried out to authenticate the method. The experimental system has the ability to achieve the parking state and align laterally within ±0.5 cm of the target pose.     

Design and realization of a mobile wheelchair robot for all terrains

A novel design of a mobile wheelchair robot for all terrains, especially for staircases and inclines, is proposed. Toachieve the required locomotion, a pair of multi-limbed structures, comprised of a lift coxae, rotation femurs and support tibiae, are pivotally mounted on the opposite sides of the body and actuated to rotate through epicyclic gear trains. A dual-footing mode with a set of wheel and crawler tractors in each support tibia permits the locomotion mode depending on various terrains, and hence the mobile wheelchair robot can navigate on a flat surface, and climb up and down stairs or inclines with its body kept horizontal. The implemented design is verified experimentally using our first manufactured prototype mobile wheelchair robot and it is shown that it could be suitable for applications to wheelchairs or others.     

Fuzzy-differential evolution algorithm for planning time-optimal trajectories of a unicycle mobile robot on a predefined path

An evolutionary technique with a Fuzzy Inference System (FIS) is offered for planning time-optimal trajectories on a predefined Visibility Graph Method Dijkstra (VGM-D) path of a Nomad 200 mobile robot (MR). First of all, the segmented trajectory is generated by the VGM-D algorithm. Line and curve segments are the components of the trajectory. The number of intersections of the segmented VGM-D path determines the curve segments number. It is assumed that, at each curve segment, translation velocity v _t is taken as constant. The Differential Evolution (DE) algorithm finds v _t values of all the curve segments, which minimize the trajectory tracking time. Line segments lengths are used to calculate the constraints of the problem according to the Nomad 200's limitations on the translation velocity and acceleration/deceleration. The structures of the curve segments are modeled by FIS to decrease the DE's execution time. Another FIS model is used to define the upper bound of the translation velocities on the curve segments for the same purpose. Both FIS models are trained by the adapted-network-based fuzzy inference system (ANFIS). Experiments are successfully implemented on the Nomad 200 MR.     

Position estimation of a mobile robot using images of a moving target in intelligent space with distributed sensors

Latest advances in hardware technology and state-of-the-art of mobile robots and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. A mobile service robot requires the perception of its present position to co-exist with humans and support humans effectively in populated environments. To realize this, a robot needs to keep track of relevant changes in the environment. This paper proposes localization of a mobile robot using images recognized by distributed intelligent networked devices in intelligent space (ISpace) in order to achieve these goals. This scheme combines data from the observed position, using dead-reckoning sensors, and the estimated position, using images of moving objects, such as a walking human captured by a camera system, to determine the location of a mobile robot. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the ISpace. Using the a priori known path of a moving object and a perspective camera model, the geometric constraint equations that represent the relation between image frame coordinates for a moving object and the estimated robot's position are derived. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot, and the Kalman filtering scheme is used for the estimation of the mobile robot location. The proposed approach is applied for a mobile robot in ISpace to show the reduction of uncertainty in determining the location of a mobile robot, and its performance is verified by computer simulation and experiment.     

Sensor-based fuzzy navigation of an autonomous mobile robot in an indoor environment

The work presented in this paper deals with the problem of navigating a mobile robot either in an unknown indoor environment or in a partially known one. A navigation method based on the combination of elementary behaviors has been developed for an unknown environment. Most of these behaviors are achieved by means of fuzzy inference systems. The proposed navigator combines two types of obstacle avoidance behaviors, one for the convex obstacles and one for the concave ones. In the case of a partially known environment, a hybrid method is used to exploit the advantages of global and local navigation strategies. The coordination of these strategies is based on a fuzzy inference system that involves an on-line comparison between the real scene and a memorized one. Both methods have been implemented on the miniature mobile robot Khepera^® which is equipped with rough sensors. The good results obtained illustrate the robustness of a fuzzy logic approach with regard to sensor imperfections.