Nonholonomic motion planning: steering using sinusoids

Methods for steering systems with nonholonomic c·onstraints between arbitrary configurations are investigated. Suboptimal trajectories are derived for systems that are not in canonical form. Systems in which it takes more than one level of bracketing to achieve controllability are considered. The trajectories use sinusoids at integrally related frequencies to achieve motion at a given bracketing level. A class of systems that can be steered using sinusoids (claimed systems) is defined. Conditions under which a class of two-input systems can be converted into this form are given     

Resumption strategies for interrupted programming tasks

Interrupted and blocked tasks are a daily reality for professional programmers. Unfortunately, the strategies programmers use to recover lost knowledge and rebuild context when resuming work have not yet been well studied. In this paper, we describe an exploratory analysis performed on 10,000 recorded sessions of 86 programmers and a survey of 414 programmers to understand the various strategies and coping mechanisms developers use to manage interrupted programming tasks. Based on the analysis, we propose a framework for understanding these strategies and suggest how task resumption might be better supported in future development tools. The results suggest that task resumption is a frequent and persistent problem for developers. For example, we find that only 10% of the sessions have programming activity resume in less than 1 min after an interruption, only 7% of the programming sessions involve no navigation to other locations prior to editing. We also found that programmers use multiple coping mechanisms to recover task context when resuming work.     

Bang-bang control strategy using a tabular adaptive model

An adaptive-predictive model for nonlinear, finite settling-time processes has been described by Roy et al., This paper utilizes this model for the trajectory following control of both finite and infinite settling-time nonlinear systems with switched two-level input.     

New recursive estimators from correlated interrupted observations using covariance information

Least-squares linear and quadratic filtering and fixed-point smoothing algorithms are derived to estimate a signal from uncertain observations perturbed by an additive white noise. The random variables describing the uncertainty are correlated only at consecutive time instants, and this correlation, as well as the probability that the signal exists in each observation, is known. Recursive algorithms are obtained without requiring the state-space model generating the signal, but just some moments of both the signal and the additive noise in the observation equation. For the linear estimation algorithms, only the second-order moments are required, and the autocovariance function of the signal must be expressed in a semi-degenerate kernel form. The quadratic estimation algorithms use, in addition, the moments up to the fourth one, and they require the autocovariance and cross-covariance functions of the signal and their second-order powers in a semi-degenerate kernel form. This form for expressing autocovariance functions is not very restrictive, since it covers many general stochastic processes, including stationary and non-stationary processes.     

Visual strategies for the control of steering toward a goal

We have shown that people steer toward a target by aligning their heading with the target when target egocentric direction is not available for steering [24]. Here we examined what visual strategies people use to steer toward a target when target egocentric direction is available for steering. The display simulated a participant walking over a ground plane with a target placed off to one side. The participant’s simulated heading in the display was displaced 10° away from the participant’s straight ahead. A textured ground display that provided dense global optic flow and an empty ground display that provided nearly no flow were tested. Participants were instructed to use a joystick to control their simulated self-motion in the display to (a) steer toward the target, (b) center the target at their straight ahead, or (c) minimize the target drift on the screen. We found that participants produced similar heading error profiles when they were instructed to steer toward the target or to center the target straight ahead, but not when they were instructed to minimize the target movement on the screen. Furthermore, regardless of the instructions received, final heading errors were about 5° smaller with the textured than with the empty ground display, indicating the effect of optic flow on the control performance. We conclude that when target egocentric direction is available for steering, people do not steer toward the target by canceling its optical drift. Optic flow contributes to steering toward a target even when control could be based on egocentric direction alone.     

On identification and adaptive estimation for systems with interrupted observations

A linear discrete-time system with interrupted observations is considered. The interrupted observation mechanism is expressed in terms of a finite-state ergodic Markov chain. The transition probability matrix of the Markov chain and some system parameters may be unknown, but constant, and are assumed to belong to a compact set. A novel scheme, called truncated maximum likelihood estimation, is proposed for consistent estimation of the unknown parameters. Sufficient conditions for strong consistency are investigated. The truncated maximum likelihood procedure is computationally feasible whereas the standard maximum likelihood procedure is not, given large observation records. Finally, using the truncated ML algorithm, a suboptimal adaptive state estimator is proposed and its asymptotic behavior is analyzed.     

Optimal control of stochastic systems with interrupted observations

An optimal control problem is considered for a nonlinear stochastic system with an interrupted observation mechanism that is characterized in terms of a jump Markov process taking on the values 0 or 1. The state of the system is described by a diffusion process, but the observation has components modulated by the jump process. The admissible control laws are smooth functions of the observation. Using the calculus of variations, necessary conditions on optimal controls are derived. These conditions amount to solving a set of four coupled nonlinear partial differential equations. A numerical procedure for solving these equations is suggested and an example dealt with numerically.     

Real-time zero moment point compensation method using null motion for mobile manipulators

This paper presents a method to deal with the dynamic stability for a mobile manipulator. Although the system has static stability, manipulation on the moving base or mobile locomotion with a manipulator may cause the system to turn over due to dynamics, so the controller of a mobile manipulator is carefully designed. In this paper, to define the dynamic stability for a mobile manipulator, the zero moment point (ZMP) is used. ZMP is a very useful measure of the dynamic stability. However, if the degrees of freedom of the system are large, the calculation algorithm is very complicated. So, to simplify the calculation algorithm, we define ZMP using the iterative Newton–Euler formulation. Next, a unified approach for the two subsystems, i.e., mobile and manipulator, is formulated using a redundant scheme. To conserve the dynamic stability of the system in real-time, we define the performance index for the redundant system using ZMP. Then, the redundancy resolution problem for optimizing the proposed performance index is solved using the null motion optimization. Finally, the performance of the proposed method is demonstrated by simulation.     

A digital driving method using pulse-density modulation with a random dither matrix for higher motion image quality

We propose a new digital driving technique using pulse-density modulation (PDM) with a random dither matrix for evenness of luminance uniformity and higher motion image quality. As a digital driving technique, pulse-width modulation (PWM) is conventionally used for active-matrix organic light-emitting diode (AMOLED) displays in order to improve luminance uniformity. However, in PWM driving, it is difficult to realize high-frame-rate driving for improving motion image quality. On the other hand, it is considered that PDM driving method is effective to obtain high motion image quality because of non-frame-refreshing property. In the present paper, we proposed a PDM driving technique using a random dither matrix and evaluated the relationship between the display image quality and the driving frequency in the proposed method through simulations. As a result, it is confirmed that good image quality can be obtained by using a blue-noise mask as a random dither matrix at a practical driving frequency. Moreover, we demonstrated the proposed technique using an actual AMOLED panel.     

Optimal sequential estimation of discrete processes with Markov interrupted observations

This short paper considers the problem of sequential estimation of discrete-time processes corrupted by additive noise when there is time-varying uncertainty regarding the presence of the process at each stage of the observation sequence. A recursive Bayes' optimal solution is derived that does not require a growing amount of memory and computation for its implementation but that, however, requires recursion on continuous functions to be performed. Digital computer implementation of the proposed algorithms is discussed and some simulation results are presented.     

Mossar: motion segmentation by using splitting and remerging strategies

This paper presents a novel approach for motion segmentation by using strategies of splitting and remerging. The presented approach, Mossar, hybridizes two existing ones to obtain their potential advantages while covering weaknesses: (1) velocity-based, one of the most widely used approaches that has fairly low accuracy but provides computational simplicity and (2) graph-based, a state-of-the-art approach that provides outstanding accuracy, yet bears high computational complexity and a burden in setting of thresholds. An initial set of key frames is generated by a velocity-based splitting process and then fed into a graph-based remerging process for refinement. We present mechanisms that improve key-frames capturing in the velocity-based approach as well as details on how the graph-based approach is modified and later applied to remerging. The proposed approach also allows users to interactively add or reduce the number of key frames to control segmentation hierarchy without the need to change threshold values and re-run segmentation, as usually done in existing approaches. Our experimental results show that the presented hybrid approach, compared to both velocity-based and graph-based, demonstrates superior performance in terms of accuracy and in comparison to graph-based, our approach has not only less complexity but also a lesser number of thresholds, the values of which can be much more simply determined.     

Estimating a state-space model from point process observations

A widely used signal processing paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, we develop an algorithm to estimate a state-space model observed through point process measurements. We represent the latent process modulating the neural spiking activity as a gaussian autoregressive model driven by an external stimulus. Given the latent process, neural spiking activity is characterized as a general point process defined by its conditional intensity function. We develop an approximate expectation-maximization (EM) algorithm to estimate the unobservable state-space process, its parameters, and the parameters of the point process. The EM algorithm combines a point process recursive nonlinear filter algorithm, the fixed interval smoothing algorithm, and the state-space covariance algorithm to compute the complete data log likelihood efficiently. We use a Kolmogorov-Smirnov test based on the time-rescaling theorem to evaluate agreement between the model and point process data. We illustrate the model with two simulated data examples: an ensemble of Poisson neurons driven by a common stimulus and a single neuron whose conditional intensity function is approximated as a local Bernoulli process.     

Comments on: Optimal stochastic control for discrete-time linear systems with interrupted observations

In a recent paper [1] a control law was found, which was claimed to be optimal. Here we point out an error in the proof and give a counterexample. In fact, the control law in [1] can be seen as a “passive open loop approximation”. We suggest also a control law which actively identifies the random variables in the loop.     

运动图形方面的10个成功策略

让自己成为视频导演的关键就是要创作出自己独特的风格并发展自己的品牌。 01将重点放在讲故事上 确保你很了解自己将要讲述的故事。无论你使用什么媒体,故事对于一部成功的电影来说都是很重要的。无论你使用了多少技巧来取悦观众,一旦故事不管用的话,电影就会失败。     

Derandomizing random walks in undirected graphs using locally fair exploration strategies

We consider the problem of exploring an anonymous undirected graph using an oblivious robot. The studied exploration strategies are designed so that the next edge in the robot’s walk is chosen using only local information, and so that some local equity (fairness) criterion is satisfied for the adjacent undirected edges. Such strategies can be seen as an attempt to derandomize random walks, and are natural counterparts for undirected graphs of the rotor-router model for symmetric directed graphs. The first of the studied strategies, known as Oldest-First, always chooses the neighboring edge for which the most time has elapsed since its last traversal. Unlike in the case of symmetric directed graphs, we show that such a strategy in some cases leads to exponential cover time. We then consider another strategy called Least-Used-First which always uses adjacent edges which have been traversed the smallest number of times. We show that any Least-Used-First exploration covers a graph G = (V, E) of diameter D within time O(D|E|), and in the long run traverses all edges of G with the same frequency.     

3D point cloud registration denoising method for human motion image using deep learning algorithm

Aiming at the problem of 3D point cloud noise affecting the efficiency and precision of human body 3D reconstruction in complex scenes, a 3D point cloud registration denoising method for human motion image using depth learning algorithm is proposed. First, two Kinect sensors are used to collect the three-dimensional data of the human body in the scene, and the spatial alignment under the Bursa linear model is used to pre-process the background point cloud data. The depth image of the point cloud is calculated, and the depth image pair is extracted by the convolutional neural network. Furthermore, the feature difference of the depth image pair is taken as the input of the fully connected network and the point cloud registration parameter is calculated, and the above operation is performed iteratively until the registration error is less than the acceptable threshold. Then, the improved C-means algorithm is used to remove the outlier, the noise is clustered, and the large-scale outlier noise is removed. Finally, the high-frequency information is processed by the depth data bilateral filtering method. The experimental results show that compared with the traditional bilateral filtering algorithm and fuzzy C-means algorithm, the proposed method can effectively remove noise of different scales and maintain good performance on the basis of maintaining human body features. In the point cloud model of A, B, and C, the average error of the proposed method is lower than that of the traditional bilateral filtering algorithm with 15.7%, 15.9%, and 19.8%, respectively, and it is lower than that of the fuzzy C-means algorithm with 25.8%, 26.9%, and 30.2%, respectively.     

Sequential estimation of a threshold crossing time for a Gaussian random walk through correlated observations

Given a Gaussian random walk X with drift, we consider the problem of estimating its first-passage time ?? _A for a given level A from an observation process Y correlated to X. Estimators may be any stopping times ?? with respect to the observation process Y. Two cases of the process Y are considered: a noisy version of X and a process X with delay d. For a given loss function f(x), in both cases we find exact asymptotics of the minimal possible risk E f((?? ? ?? _A )/r) as A, d ?? ??, where r is a normalizing coefficient. The results are extended to the corresponding continuous-time setting where X and Y are Brownian motions with drift.     

Remarks on “optimal stochastic control for discrete-time linear system with interrupted observations”

In the paper by Fujita and Fukao [1], a proof of the separation theorem for discrete-time linear systems with interrupted observations was presented. The theorem is based heavily on [1, Lemma]. This note points out errors of the proof of [1, Lemma] and disproves the separation theorem.     

A random forest-based framework for crop mapping using temporal,spectral, textural and polarimetric observations

Combining optical and polarimetric synthetic aperture radar (PolSAR) earth observations offers a complementary data set with a significant number of spectral, textural, and polarimetric features for crop mapping and monitoring. Moreover, a temporal combination of both sources of information may lead to obtaining more reliable results compared to the use of single-time observations. In this paper, an operational framework based on the stacked generalization of random forest (RF), which efficiently employed bi-temporal observations of optical and radar data, was proposed for crop mapping. In the first step, various spectral, vegetation index, textural, and polarimetric features were extracted from both data sources and placed into several groups. Each group was classified separately using a single RF classifier. Then, several additional classification tasks were accomplished by another RF classifier. The earth observations used in this paper were collected by RapidEye satellites and the Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system over an agricultural region near Winnipeg, Manitoba, Canada. The results confirmed that the proposed methodology was able to provide a higher overall accuracy and kappa coefficient than traditional stacking method, and also than all the individual RFs using each group. These accuracy metrics were also better than those of the RFs using the stacked features. Moreover, only the proposed methodology could achieve standard accuracy (F-score ≥85%) for all crop types in the study area. The visual comparison also demonstrated that the crop maps produced by the proposed methodology had more homogeneous, uniform appearances. Moreover, the mixed pixels of crop types, which abundantly existed in the traditional stacking and individual RFs? maps, were significantly eliminated.