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 scalable solution to the nearest neighbor search problem through local-search methods on neighbor graphs

Pattern Analysis and Applications - Nearest neighbor search is a powerful abstraction for data access; however, data indexing is troublesome even for approximate indexes. For intrinsically...     

Development of a pit filling algorithm for LiDAR canopy height models

LiDAR canopy height models (CHMs) can exhibit unnatural looking holes or pits, i.e., pixels with a much lower digital number than their immediate neighbors. These artifacts may be caused by a combination of factors, from data acquisition to post-processing, that not only result in a noisy appearance to the CHM but may also limit semi-automated tree-crown delineation and lead to errors in biomass estimates. We present a highly effective semi-automated pit filling algorithm that interactively detects data pits based on a simple user-defined threshold, and then fills them with a value derived from their neighborhood. We briefly describe this algorithm and its graphical user interface, and show its result in a LiDAR CHM populated with data pits. This method can be rapidly applied to any CHM with minimal user interaction. Visualization confirms that our method effectively and quickly removes data pits.     

First experiences of teaching quantum computing

The Journal of Supercomputing - Quantum computing is a reality that presents challenges to computer engineering students and practitioners. It has been claimed that it is possible to effectively...     

New Results for Prediction of Chaotic Systems Using Deep Recurrent Neural Networks

Neural Processing Letters - Prediction of nonlinear and dynamic systems is a challenging task, however with the aid of machine learning techniques, particularly neural networks, is now possible to...     

An Active Disturbance Rejection Approach to Leader‐Follower Controlled Formation

This article describes the design of a linearizing, observer‐based, robust dynamic feedback control scheme for output reference trajectory tracking tasks in a leader‐follower non‐holonomic car formation problem. The approach is based on the cars' kinematic models. A radical simplification in the form of a global ultra‐model is proposed on the follower's exact open loop position tracking error dynamics obtained via flatness considerations. This results in a system described by an additively disturbed set of two, second order integrators with non‐linear velocity dependent control input gain matrix. The unknown additive disturbances are modeled as absolutely uniformly bounded time signals which may be locally approximated by arbitrary elements of a sufficiently high degree family of Taylor polynomials. Linear high‐gain Luenberger observers of the generalized proportional integral (GPI) type may be readily designed. These observers include the self updating internal model of the unknown disturbance input vector components in the form of generic, instantaneous, time‐polynomial models. The proposed (GPI) observers, which are the dual counterpart of GPI controllers [17], achieve a simultaneous disturbance estimation and tracking error phase variables estimation. This on‐line gathered information is used to advantage on the follower's feedback controller thus allowing for a simple, yet efficient, disturbance and control input gain cancelation effort. The results are applied to have the follower track a time‐delayed version of the actual leader's trajectory. Experimental results are presented which illustrate the robustness and viability of the proposed approach.     

A system-on-chip development of a neuro-fuzzy embedded agent for ambient-intelligence environments

This paper presents the development of a neuro-fuzzy agent for ambient-intelligence environments. The agent has been implemented as a system-on-chip (SoC) on a reconfigurable device, i.e., a field-programmable gate array. It is a hardware/software (HW/SW) architecture developed around a MicroBlaze processor (SW partition) and a set of parallel intellectual property cores for neuro-fuzzy modeling (HW partition). The SoC is an autonomous electronic device able to perform real-time control of the environment in a personalized and adaptive way, anticipating the desires and needs of its inhabitants. The scheme used to model the intelligent agent is a particular class of an adaptive neuro-fuzzy inference system with piecewise multilinear behavior. The main characteristics of our model are computational efficiency, scalability, and universal approximation capability. Several online experiments have been performed with data obtained in a real ubiquitous computing environment test bed. Results obtained show that the SoC is able to provide high-performance control and adaptation in a life-long mode while retaining the modeling capabilities of similar agent-based approaches implemented on larger computing machines.     

Estimating residual variance in random forest regression

Random forest, a data-mining technique which uses multiple classification or regression trees, is a popular algorithm used for prediction. Inference and goodness-of-fit assessment, however, may require an estimator of variability; in many applications the residual variance is of primary interest. This paper proposes two estimators of residual variance for random forest regression that take advantage of byproducts of the algorithm. The first estimator is based on the residual sum of squares from a random forest fit and uses a bootstrap bias correction. The second estimator is a difference-based estimator that uses proximity measures as weights. The estimators are evaluated through Monte Carlo simulations. Applications of the methods to the problem of assessing the relative variability of males and females on cognitive and achievement tests are discussed, and the methods are applied to estimate the residual variance in test scores for male and female students on the mathematics portion of the 2007 Arizona Instrument to Measure Standards.     

Correction to: EKLT: Asynchronous Photometric Feature Tracking Using Events and Frames

International Journal of Computer Vision - The original version of this article was unfortunately omitted to publish the footnote “The best result per row is highlighted in bold” in...     

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.