An adaptive appearance-based map for long-term topological localization of mobile robots

"This work considers a mobile service robot which uses an appearance-based representation of its workplace as a map, where the current view and the map are used to estimate the current position in the environment. Due to the nature of real-world environments such as houses and offices, where the appearance keeps changing, the internal representation may become out of date after some time. To solve this problem the robot needs to be able to adapt its internal representation continually to the changes in the environment. This paper presents a method for creating an adaptive map for long-term appearance-based localization of a mobile robot using long-term and short-term memory concepts, with omni-directional vision as the external sensor."--publisher website

An adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In previous work we introduced a method to update the reference views in a topological map so that a mobile robot could continue to localize itself in a changing environment using omni-directional vision. In this work we extend this longterm updating mechanism to incorporate a spherical metric representation of the observed visual features for each node in the topological map. Using multi-view geometry we are then able to estimate the heading of the robot, in order to enable navigation between the nodes of the map, and to simultaneously adapt the spherical view representation in response to environmental changes. The results demonstrate the persistent performance of the proposed system in a long-term experiment.

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metrictopological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability.

A frequency adaptive single-phase Phase-Locked Loop with harmonic rejection

An improved Phase-Locked Loop (PLL) for extracting phase and frequency of the fundamental component of a highly distorted grid voltage is presented. The structure of the single-phase PLL is based on the Synchronous Reference Frame (SRF) PLL and uses an All Pass Filter (APF) to generate the quadrature component from the single phase input voltage. In order to filter the harmonic content, a Moving Average Filter (MAF) is used, and performance is improved by designing a lead compensator and also a feed-forward compensator. The simulation results are compared to show the improved performance with feed-forward. In addition, the frequency dependency of MAF is dealt with by a proposed method for adaption to the frequency. This method changes the window size based on the frequency on a sample-by-sample basis. By using this method, the speed of resizing can be reduced in order to decrease the output ripples caused by window size variations.

Can social resilience inform SA/SIA for adaptive planning for climate change in vulnerable regions?

Social resilience concepts are gaining momentum in environmental planning through an emerging understanding of the socio-ecological nature of biophysical systems. There is a disconnect, however, between these concepts and the sociological and psychological literature related to social resilience. Further still, both schools of thought are not well connected to the concepts of social assessment (SA) and social impact assessment (SIA) that are the more standard tools supporting planning and decision-making. This raises questions as to how emerging social resilience concepts can translate into improved SA/SIA practices to inform regional-scale adaptation. Through a review of the literature, this paper suggests that more cross-disciplinary integration is needed if social resilience concepts are to have a genuine impact in helping vulnerable regions tackle climate change.

Trait resilience fosters adaptive coping when control opportunities are high : implications for the motivating potential of active work

Purpose: We examine the interaction between trait resilience and control in predicting coping and performance. Drawing on a person–environment fit perspective, we hypothesized resilient individuals would cope and perform better in demanding work situations when control was high. In contrast, those low in resilience would cope and perform better when control was low. Recognizing the relationship between trait resilience and performance also could be indirect, adaptive coping was examined as a mediating mechanism through which high control enables resilient individuals to demonstrate better performance. Methodology: In Study 1 (N = 78) and Study 2 (N = 94), participants completed a demanding inbox task in which trait resilience was measured and high and low control was manipulated. Study 3 involved surveying 368 employees on their trait resilience, control, and demand at work (at Time 1), and coping and performance 1 month later at Time 2. Findings: For more resilient individuals, high control facilitated problem-focused coping (Study 1, 2, and 3), which was indirectly associated with higher subjective performance (Study 1), mastery (Study 2), adaptive, and proficient performance (Study 3). For more resilient individuals, high control also facilitated positive reappraisal (Study 2 and 3), which was indirectly associated with higher adaptive and proficient performance (Study 3). Implications: Individuals higher in resilience benefit from high control because it enables adaptive coping. Originality/value: This research makes two contributions: (1) an experimental investigation into the interaction of trait resilience and control, and (2) investigation of coping as the mechanism explaining better performance.

Adaptive estimation of HMM transition probabilities

This paper presents new schemes for recursive estimation of the state transition probabilities for hidden Markov models (HMM's) via extended least squares (ELS) and recursive state prediction error (RSPE) methods. Local convergence analysis for the proposed RSPE algorithm is shown using the ordinary differential equation (ODE) approach developed for the more familiar recursive output prediction error (RPE) methods. The presented scheme converges and is relatively well conditioned compared with the ...

On adaptive HMM state estimation

In this paper new online adaptive hidden Markov model (HMM) state estimation schemes are developed, based on extended least squares (ELS) concepts and recursive prediction error (RPE) methods. The best of the new schemes exploit the idempotent nature of Markov chains and work with a least squares prediction error index, using a posterior estimates, more suited to Markov models then traditionally used in identification of linear systems.

Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

Adaptive finite element analysis of elliptic problems based on bubble-type local mesh generation

A new mesh adaptivity algorithm that combines a posteriori error estimation with bubble-type local mesh generation (BLMG) strategy for elliptic differential equations is proposed. The size function used in the BLMG is defined on each vertex during the adaptive process based on the obtained error estimator. In order to avoid the excessive coarsening and refining in each iterative step, two factor thresholds are introduced in the size function. The advantages of the BLMG-based adaptive finite element method, compared with other known methods, are given as follows: the refining and coarsening are obtained fluently in the same framework; the local a posteriori error estimation is easy to implement through the adjacency list of the BLMG method; at all levels of refinement, the updated triangles remain very well shaped, even if the mesh size at any particular refinement level varies by several orders of magnitude. Several numerical examples with singularities for the elliptic problems, where the explicit error estimators are used, verify the efficiency of the algorithm. The analysis for the parameters introduced in the size function shows that the algorithm has good flexibility.

Scale adaptive tracking using mean shift and efficient feature matching

The mean shift tracker has achieved great success in visual object tracking due to its efficiency being nonparametric. However, it is still difficult for the tracker to handle scale changes of the object. In this paper, we associate a scale adaptive approach with the mean shift tracker. Firstly, the target in the current frame is located by the mean shift tracker. Then, a feature point matching procedure is employed to get the matched pairs of the feature point between target regions in the current frame and the previous frame. We employ FAST-9 corner detector and HOG descriptor for the feature matching. Finally, with the acquired matched pairs of the feature point, the affine transformation between target regions in the two frames is solved to obtain the current scale of the target. Experimental results show that the proposed tracker gives satisfying results when the scale of the target changes, with a good performance of efficiency.

Adaptive online gradient descent

We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates between [square root T] and [log T]. Furthermore, we show strong optimality of the algorithm. Finally, we provide an extension of our results to general norms.

Toward the adaptive generation of bespoke game content

In this chapter, we explore methods for automatically generating game content—and games themselves—adapted to individual players in order to improve their playing experience or achieve a desired effect. This goes beyond notions of mere replayability and involves modeling player needs to maximize their enjoyment, involvement, and interest in the game being played. We identify three main aspects of this process: generation of new content and rule sets, measurement of this content and the player, and adaptation of the game to change player experience. This process forms a feedback loop of constant refinement, as games are continually improved while being played. Framed within this methodology, we present an overview of our recent and ongoing research in this area. This is illustrated by a number of case studies that demonstrate these ideas in action over a variety of game types, including 3D action games, arcade games, platformers, board games, puzzles, and open-world games. We draw together some of the lessons learned from these projects to comment on the difficulties, the benefits, and the potential for personalized gaming via adaptive game design.