An information centric approach to heterogeneous multi-sensor integration for robotic applications

An autonomous robot's ability to respond intelligently to an unstructured non-static environment is constrained by its ability to sense and interpret its world. The prospective benefits of employing multiple diverse sensors outweigh the potential limitations. Herein, the two most important issues of what to fuse and how to fuse are developed in detail. The former is known as the registration problem, and the latter the fusion problem. The registration procedure is addressed by a comprehensive three-stage process. It is a goal of this work to provide a viable framework towards the real-time interpretation of heterogeneous sensor data for robots.     

A knowledge-based neural network for fusing edge maps of multi-sensor images

With the goal of fusion prescribed as building an edge map that contains as many edges as possible from the given multi-spectral/sensor images, a new fusion scheme, called the knowledge-based neural network fusion (KBNNF), is proposed to fuse edge maps of these images in order to generate a combined edge map that has more complete and reliable edge information than what one can obtain from any single image.The KBNNF is used to fuse edge maps of images having mutually complementary edge information in the following sense: (i) the edges in the images are compatible, i.e., can be interpreted together; and (ii) the edges in the different images reveal different parts of the scene. More complete edge contours of the same object are obtained by linking the edge sections obtained from different images together. The resulting edge map can be used for subsequent study (like object recognition).The proposed scheme bases its confidence and reliability on the analysis of variance (ANOVA)-based edge detector that can address two important issues of edge based image fusion well: (i) the difference in edge position among the images because of the different characteristics of the images and the error in the image registration process; and (ii) the variance existing among the edge test values calculated from different images. The KBNNF has been applied to fuse: (i) radar (SAR)–optical (SPOT), (ii) optical–optical, (iii) infrared–infrared, and (iv) optical–infrared (satellite) image combinations. Comparisons are made with the relevant existing techniques in the literature. The paper concludes with some examples to illustrate the efficacy of the proposed scheme.     

基于多传感器信息融合的移动机器人快速精确自定位

通过分析全向视觉、电子罗盘和里程计等传感器的感知模型,设计并实现了一种给定环境模型下移动机器人全局自定位算法.该算法利用蒙特卡罗粒子滤波,融合多个传感器在不同观测点获取的观测数据完成机器人自定位.与传统的、采用单一传感器自定位的方法相比,它把多个同质或异质传感器所提供的不完整测量及相关联数据库中的信息加以综合,降低单个...     

A variational approach to multi-sensor fusion of images

Past research into multi-modality sensor data fusion has given rise to approaches that are generally heuristic and ad hoc. In this paper we utilize the calculus of variations as the underlying framework for fusing registered images of different modalities when models relating these modalities are available. The result is a mathematically rigorous method for improving the accuracy with which parameters can be estimated. Using both dense and sparse simulated range and intensity data, the proposed approach is demonstrated on the problem of estimating the surface representing the three dimensional structure of a scene. The results indicate that a four to five-fold increase in surface estimation accuracy with respect to the original input data can be realized. Furthermore, an 8%–250% increase in accuracy over surface estimation from each sensing modality alone (i.e., via shape from shading or surface reconstruction) can be realized.H. Pien is supported by Draper Laboratory under IR&D No. 451; J. Gauch is partially supported by the National Science Foundation under Grant IRI-9109431.     

基于特征点的机器人快速自定位研究及应用

提出一种在已知的结构化环境中，利用激光传感器信息进行移动机器人快速自定位的算法．该算法从传感器信息中快速提取环境特征点，根据特征点的坐标映射模型，确定机器人的坐标和方向角．它克服了传统算法运算量大、实时性差的问题且定位精度高，将其应用于机器人足球赛中，取得了较好的定位效果．     

A convergent dynamic window approach to obstacle avoidance

The dynamic window approach (DWA) is a well-known navigation scheme developed by Fox et al. and extended by Brock and Khatib. It is safe by construction, and has been shown to perform very efficiently in experimental setups. However, one can construct examples where the proposed scheme fails to attain the goal configuration. What has been lacking is a theoretical treatment of the algorithm's convergence properties. Here we present such a treatment by merging the ideas of the DWA with the convergent, but less performance-oriented, scheme suggested by Rimon and Koditschek. Viewing the DWA as a model predictive control (MPC) method and using the control Lyapunov function (CLF) framework of Rimon and Koditschek, we draw inspiration from an MPC/CLF framework put forth by Primbs to propose a version of the DWA that is tractable and convergent.     

3D visual perception for self-driving cars using a multi-camera system: Calibration,mapping, localization,and obstacle detection

Cameras are a crucial exteroceptive sensor for self-driving cars as they are low-cost and small, provide appearance information about the environment, and work in various weather conditions. They can be used for multiple purposes such as visual navigation and obstacle detection. We can use a surround multi-camera system to cover the full 360-degree field-of-view around the car. In this way, we avoid blind spots which can otherwise lead to accidents. To minimize the number of cameras needed for surround perception, we utilize fisheye cameras. Consequently, standard vision pipelines for 3D mapping, visual localization, obstacle detection, etc. need to be adapted to take full advantage of the availability of multiple cameras rather than treat each camera individually. In addition, processing of fisheye images has to be supported. In this paper, we describe the camera calibration and subsequent processing pipeline for multi-fisheye-camera systems developed as part of the V-Charge project. This project seeks to enable automated valet parking for self-driving cars. Our pipeline is able to precisely calibrate multi-camera systems, build sparse 3D maps for visual navigation, visually localize the car with respect to these maps, generate accurate dense maps, as well as detect obstacles based on real-time depth map extraction.     

A dynamical system approach to realtime obstacle avoidance

This paper presents a novel approach to real-time obstacle avoidance based on Dynamical Systems (DS) that ensures impenetrability of multiple convex shaped objects. The proposed method can be applied to perform obstacle avoidance in Cartesian and Joint spaces and using both autonomous and non-autonomous DS-based controllers. Obstacle avoidance proceeds by modulating the original dynamics of the controller. The modulation is parameterizable and allows to determine a safety margin and to increase the robot’s reactiveness in the face of uncertainty in the localization of the obstacle. The method is validated in simulation on different types of DS including locally and globally asymptotically stable DS, autonomous and non-autonomous DS, limit cycles, and unstable DS. Further, we verify it in several robot experiments on the 7 degrees of freedom Barrett WAM arm.     

A grammatical approach to self-organizing robotic systems

In this paper, we define a class of graph grammars that can be used to model and direct concurrent robotic self-assembly and similar self-organizing processes. We give several detailed examples of the formalism and then focus on the problem of synthesizing a grammar so that it generates a given, prespecified assembly. In particular, to generate an acyclic graph we synthesize a binary grammar (rules involve at most two parts), and for a general graph we synthesize a ternary grammar (rules involve at most three parts). In both cases, we characterize the number of concurrent steps required to achieve the assembly. We also show a general result that implies that no binary grammar can generate a unique stable assembly. We conclude the paper with a discussion of how graph grammars can be used to direct the self-assembly of robotic parts.     

A Bayesian approach to fusing uncertain,imprecise and conflicting information

The Dezert–Smarandache theory (DSmT) and transferable belief model (TBM) both address concerns with the Bayesian methodology as applied to applications involving the fusion of uncertain, imprecise and conflicting information. In this paper, we revisit these concerns regarding the Bayesian methodology in the light of recent developments in the context of the DSmT and TBM. We show that, by exploiting recent advances in the Bayesian research arena, one can devise and analyse Bayesian models that have the same emergent properties as DSmT and TBM. Specifically, we define Bayesian models that articulate uncertainty over the value of probabilities (including multimodal distributions that result from conflicting information) and we use a minimum expected cost criterion to facilitate making decisions that involve hypotheses that are not mutually exclusive. We outline our motivation for using the Bayesian methodology and also show that the DSmT and TBM models are computationally expedient approaches to achieving the same endpoint. Our aim is to provide a conduit between these two communities such that an objective view can be shared by advocates of all the techniques.     

除冰机器人的多传感器融合障碍测距方法研究

障碍物测距是高压输电线路自主除冰机器人的关键技术之一。针对220 kV输电线路除冰机器人的结构特点,提出了一种基于扩展卡尔曼滤波的障碍物距离信息融合检测方法。首先根据障碍物分布情况设计了除冰机器人多传感器检测系统的结构,建立了障碍物信息融合系统模型。然后根据障碍物信息状态模型的非线性特点,对传感器获取的异步测量数据进行同步处理,再应用改进的扩展卡尔曼滤波对多传感器信息进行滤波和融合,并与单个传感器的结果相比较,实验结果研究表明：该方法能有效地融合不同传感器的信息,具有更高的测距精度和更快的收敛速度。     

A neuro-fuzzy approach to real-time trajectory generation for robotic rehabilitation

This paper proposes a method for the design of a real-time neuro-fuzzy trajectory generator for the robotic rehabilitation of patients with upper limb dysfunction due to neurological diseases. The primary objective of the methodology is to assist therapists by allowing them to delegate repetitive therapy tasks to a mechatronic system. The trajectory generator is packaged as a platform-independent solution to facilitate the rehabilitation of patients using multiple manipulator configurations. The system utilizes a fuzzy-logic schema to introduce compliance into the human–robot interaction, and to allow the emulation of a wide variety of therapy techniques. This approach also allows for the fine-tuning of patient specific behaviour using linguistic variables. The rule base for the system is trained using a fuzzy clustering algorithm and applied to the experimental data gathered during traditional therapy sessions. The compliance rule base is combined with a hybrid neuro-fuzzy compensator to automatically tune the dynamics of the robot–patient interaction. Preliminary results indicate that the approach can accurately reproduce a prescribed patient/therapist interaction, validating the proposed approach.     

A sonar approach to obstacle detection for a vision-based autonomous wheelchair

An advanced prototype Computer Controlled Power Wheelchair Navigation System or CCPWNS has been developed to provide autonomy for highly disabled users, whose mix of disabilities makes it difficult or impossible to control their own power chairs in their homes. The working paradigm is “teach and repeat” a mode of control for typical industrial holonomic robots. Ultrasound sensors, which during subsequent autonomous tracking will be used to detect obstacles, also are active during teaching. Based upon post-processed data collected during this teaching event, elaborate trajectories–which may involve multiple direction changes, pivoting and so on, depending upon the requirements of the typically restricted spaces within which the chair must operate–will later be called upon by the disabled rider. An off-line postprocessor assigns an ultrasound profile to the sequence of poses of any taught trajectory. Use of this profile during tracking obviates most of the inherent problems of using ultrasound to avoid obstacles while retaining the ability to near solid objects, such as when passing through a narrow doorway, where required by the environment and trajectory objectives. The work in this article describes a procedure to obtain consistent maps of sonar boundaries during the teaching process, and a preliminary approach to use this information during the tracking phase. The approach is illustrated by results obtained by using the CCPWNS prototype.     

A bayesian approach to real-time obstacle avoidance for a mobile robot

Real-time obstacle avoidance is essential for the safe operation of mobile robots in a dynamically changing environment. This paper investigates how an industrial mobile robot can respond to unexpected static obstacles while following a path planned by a global path planner. The obstacle avoidance problem is formulated using decision theory to determine an optimal response based on inaccurate sensor data. The optimal decision rule minimises the Bayes risk by trading between a sidestep maneuver and backtracking to follow an alternative path. Real-time implementation is emphasised here as part of a framework for real world applications. It has been successfully implemented both in simulation and in reality using a mobile robot.     

A robotic approach to fault-tolerant, precision pointing

Future spaceborne optical interferometers and laser satellite communication systems are two space applications that require a precision pointing function in order to meet mission goals. Spaceborne interferometers provide a promising means to discover Earth-like planets in other solar-like systems. The laser communication systems provide a low-power, low-cost, lightweight means of data relay between ground and space and for deep-space communications to interplanetary probes. Both applications share the need to acquire and track a target. The interferometry application requires pointing errors to be submicroradian while the laser communication application requires microradian-level errors. In order to meet the precision pointing requirements found in these applications, a precision pointing strategy has been developed. The strategy employs a hexapod as the pointing platform to reject vibrations from a noisy spacecraft bus over all frequencies: at low frequency using 2- or 3-axis pointing and at high frequency using 6-axis vibration isolation. The benefits include broadband pointing stability without a high-bandwidth pointing sensor or destabilizing excitation of high-frequency structural modes, as well as tolerance to failures. This article outlines this approach to pointing     

A geometric approach to robotic unfolding of garments

This work presents a novel approach to autonomous unfolding of garments by means of a dual arm robotic manipulator. The proposed approach is based on the observation that a garment can be brought to an approximately planar configuration if it is held by two points on its outline. This step facilitates the detection of another set of points that when grasped the garment will naturally unfold. A robust method for successively detecting such boundary points on images of garments hanging from a single point was developed. The manipulated garment is then laid on a flat surface and matched to a set of foldable templates using shape analysis techniques. Using the established correspondences with the template’s landmark points the garment is re-grasped by such two points that it will naturally unfold in a spread out configuration. The adopted framework has been experimentally evaluated using a dual industrial manipulator and a variety of garments. The produced results indicate the feasibility and robustness of the proposed approach.     

A two-phase analytic approach to robotic system design

A two-phase analytic approach to robotic system design is presented. The first phase evaluates the robotic technological classes according to their functional adequacy; the next phase specifies the desired robotic configuration. The methodology developed here is demonstrated for the case of installing a robot in an automated investment casting shelling production line.     

Rendezvous-guidance trajectory planning for robotic dynamic obstacle avoidance and interception.

This correspondence presents a novel online trajectory-planning method for the autonomous robotic interception of moving targets in the presence of dynamic obstacles, i.e., position and velocity matching (also referred to as rendezvous). The proposed time-optimal interception method is a hybrid algorithm that augments a novel rendezvous-guidance (RG) technique with the velocity-obstacle approach, for obstacle avoidance, first reported by Fiorini and Shiller. The obstacle-avoidance algorithm itself could not be used in its original form and had to be modified to ensure that the online planned path deviates minimally from the one generated by the RG algorithm. Extensive simulation and experimental analyses, some of which are reported in this correspondence, have clearly demonstrated the tangible time efficiency of the proposed interception method.