Robot navigation with markov models: A framework for path planning and learning with limited computational resources

This chapter presents two artificial intelligence tools: multi-agent systems (MAS) and fuzzy logic to design and implement the control architecture for autonomous navigation of a mobile robot, taking into account the changing nature of the environment. It provides the key methods used for mobile robot navigation as related works. Navigation methods for mobile robots have two classes: classical approaches and advanced approaches. The chapter introduces the problem position, describes the control architecture based agent, and also provides a discussion on the methods for evaluating an agent system. A robot is a complex system that must meet varied and sometimes contradictory requirements. The global architecture developed is a fully distributed hybrid architecture and does not involve any supervisor. The system is designed around four agents (perception, locomotion, fuzzy controller and feasibility). The chapter presents the elements essential for understanding the principle of control by fuzzy logic. These are: fuzzy variables, and inference rules.

To address the environmental concern and improve the economic efficiency, the wind power is rapidly integrated into smart grids. However, the inherent uncertainty of wind energy raises operational challenges. To ensure the cost-efficient, reliable and robust operation, it is critically important to find the optimal decision that can correctly and rigorously hedge against all sources of uncertainty. In this paper, we propose data-driven stochastic unit commitment (SUC) to guide the power grids scheduling. Specifically, given the finite historical data, the posterior predictive distribution is developed to quantify the wind power prediction uncertainty accounting for both inherent stochastic uncertainty of wind power generation and input model estimation error. For complex power grid systems, a finite number of scenarios is used to estimate the expected cost in the planning horizon. To further control the impact of finite sampling error induced by using the sample average approximation (SAA), we propose a parallel computing based optimization solution methodology, which can quickly find the reliable optimal unit commitment decision hedging against various sources of uncertainty. The empirical study over six-bus and 118-bus systems demonstrates that our approach can provide more cost-efficient and robust performance than the existing deterministic and stochastic unit commitment approaches.

We address in this paper the problem of the autonomous online learning of a sensory-motor task, demonstrated by an operator guiding the robot. For the last decade, we have developed a vision-based architecture for mobile robot navigation. Our bio-inspired model of the navigation has already proved to achieve sensory-motor tasks in real time both in unknown indoor and outdoor environments. We propose to bootstrap the underlying PerAc architecture in order to control the sensory-motor learning. The interaction leads the robot to autonomously build a precise sensory-motor dynamic approximating the behavior of the teacher. A real dialog based on actions imposed by the teacher and those proposed by the robot emerges, which catalyzes the learning of the robot. The architecture is finally tested in real indoor and outdoor environments.

A high-level state model of the national airspace system (NAS) has been developed and refined. That model provides guidance on the effect and interactions of sources of uncertainty in the NAS. The model also provides guidance on the possible effect of NextGen concepts and technologies on the NAS. The model is being coded as a high-level, abstract simulation that, when provided with specific uncertainty distributions, can also identify the emergent effect of those changes in the uncertainty distributions. The results of that analysis and simulation provide guidance for prioritizing the development and adoption of NextGen concepts and technologies for the NAS. I. Introduction he National Airspace System (NAS) is a highly complex, socio-technical system. At its lowest level, it consists of many thousands of entities, including aircraft and other sophisticated technology, passengers, pilots, and air traffic controllers. Modeling and simulation of such a system is difficult due to the large number of entities to be modeled, the infinite (or nearly so) number of degrees of freedom within the system, and the high degree of possible interactivity in the system. As a result, existing models/simulations must simplify the system by eliminating from consideration certain aspects, and therefore cannot be completely accurate in determining the effect of uncertainties on the performance of the NAS. To try to fill this gap in capability, a model of the NAS was developed, with the purpose of determining the effect of the various sources of uncertainty on the system performance of the NAS. This model is state-based, and models the system from a high level (Landry and Archer, 2013). The model is believed to be complete. Given this model, one can reason about the effects of uncertainty on the NAS, as well as the interactions of these sources of uncertainty (Landry and Archer, 2013). In addition, if one characterizes the effect of different operational concepts and technologies being considered under NextGen in terms of their effect on those sources of uncertainty, one can then reason about the relative benefits of those different concepts and technologies. In this work, we focus on the effect of concepts and technologies on the performance of the NAS. Following a brief discussion of the model, an enumeration of the technologies and concepts under consideration for NextGen will be presented, including their anticipated degree of effect on various sources of uncertainty. Lastly, the results of simulation exercises to test the model will be presented. II. Model of the NAS: Framework

Load and resistance factor design (LRFD) of foundations is an emerging design philosophy in the United States. Reliability analyses are the preferred tools to establish resistance factors, but the needed probabilistic information regarding design method uncertainty is difficult to obtain. This paper illustrates an approach to uncertainty assessment that seeks to isolate the various sources of uncertainty. Using this approach, reliability analysis is used to develop resistance factors for the design of driven pipe piles in sand. The resistance factor results are used to highlight some of the differences between design methods that are exposed by the proposed uncertainty assessment technique. This approach is key to helping engineers manage risk and assess the relative value of differing degrees and types of design knowledge.

Improved spatiotemporal monitoring of soil salinity using filtered kriging with measurement errors: An application to the West Urmia Lake, Iran

In order to solve the problem of autonomous localization and navigation caused by the loss of the position information of the mobile robot in a complex environment, a method for autonomous navigation of a mobile robot based on ROS is proposed in this paper. The ROS platform is used to avoid the problems of code redundancy, poor portability and so on. In this method, firstly, monte carlo algorithm is used to realize the autonomous localization of the robot, raster map is constructed with lidar data, and the concept of raster occupancy is introduced to reduce the influence of sensor noise. Then an improved ant colony algorithm is proposed for global path planning to realize autonomous navigation of the robot. At the end of this paper, the robot control system is combined with ROS navigation system for map construction and autonomous navigation experiments. The experimental results show that the improved ant colony algorithm can effectively shorten the number of iterations, reduce the number of iterations by 38%, improve the adaptability to the environment, facilitate the fast finding of the shortest path, and accurately realize the autonomous navigation task of the robot.

This paper presents a new obstacle negotiation method for mobile robot navigation on rough terrain. The proposed method first transforms a local terrain map surrounding the robot's momentary position into a grid-type traversability map by extracting the slope and roughness of a terrain patch through least-squares plane-fitting. It then computes so-called "polar obstacle densities" for the cells in the traversability map and transforms the traversability map into a traversability field histogram, from which the velocity and the steering command for the robot are determined. Simulation results show that the algorithm is able to navigate the robot to the target with a finite-length path.

SUMMARYThis paper presents a computationally simple stereo vision method for detecting useful features of obstacles on the ground relevant to a mobile robot's navigation in an indoor environment. Enhanced time efficiency and reliability is achieved by introducing a geometrical image transformation and a frame-wise iterative edge image comparison scheme. The image transformation used in this paper relates each constant disparity value to an oblique plane at an elevation from the floor. The stereo correspondence method is devised by implementing a frame-wise AND operation based upon edge orientation and a subsequent pixel wise intensity correlation checking. This method is efficient in terms of computation time and capable of identifying isodisparity points, so that the height information of all the obstacle points above the floor can be determined. The obtained disparities are recorded on a local map to give complete obstacle features for mobile robot's path planning. Through a series of experiments performed under various environmental conditions, it is found that the proposed method can effectively be applied for locating obstacles of various heights in indoor navigation of mobile robots.

The North Sea is affected by eutrophication problems despite the decreasing riverine nutrient fluxes since the late 1980s. Formally, assessment of the eutrophication state of European marine environments is based on their historical state. Model estimates are increasingly used to support monitoring data that often do not encompass such pre-eutrophic conditions. However, various sources of uncertainties emerge when producing these estimates. In this study, we systematically quantify various sources of uncertainties in terms of variability, and assess their importance for the North Sea. For the reconstruction of the historical state, we use two coupled physical-biogeochemical model systems: ECOHAM on a 20-km grid for the European shelf and GPM on a high-resolution (1.5–4.5 km) grid for the Southern North Sea. To gain insights into the impacts due to the uncertainty in riverine loadings, we consider the historical nutrient inputs from two alternative watershed-models (MONERIS and E-HYPE). Overall, the modeled historic state based on E-HYPE shows higher nutrient concentrations compared to the state based on MONERIS, especially in the coastal regions. Assessing the degree of methodological uncertainties by an inter-comparison of different sources and against natural variabilities provides insight into the reliability of the model-based reconstruction of the historical state. We find that in regions influenced by freshwater from major rivers uncertainties owed to riverine loading scenarios exceed the natural sources of variability. For the offshore regions, natural sources of variability dominate over those caused by model- and scenario-related uncertainties. These findings are expected to assist decision makers and researchers in gaining insight into the degree of confidence in evaluating the model results, and prioritizing the need for refinement of models and scenarios for the production of reliable projections.

This article describes a novel qualitative navigation method for mobile robots in indoor environments. The approach is based on qualitative representations of variations in sensor behavior between adjacent regions in space. These representations are used to localize and guide planning and reaction. Off-line, the system accepts as input a line-based diagram of the environment and generates a map based on a simple qualitative model of sensor behavior. During execution, the robot controller integrates this map into a reaction module. This architecture has been tested both in simulation and on a real mobile robot. Results from both trials are provided.

The current study evaluates the impacts of various sources of uncertainty involved in hydrologic modeling on parameter behavior and regionalization utilizing different Bayesian likelihood functions and the Differential Evolution Adaptive Metropolis (DREAM) algorithm. The developed likelihood functions differ in their underlying assumptions and treatment of error sources. We apply the developed method to a snow accumulation and ablation model (National Weather Service SNOW17) and generate parameter ensembles to predict snow water equivalent (SWE). Observational data include precipitation and air temperature forcing along with SWE measurements from 24 sites with diverse hydroclimatic characteristics. A multiple linear regression model is used to construct regionalization relationships between model parameters and site characteristics. Results indicate that model structural uncertainty has the largest influence on SNOW17 parameter behavior. Precipitation uncertainty is the second largest source of uncertainty, showing greater impact at wetter sites. Measurement uncertainty in SWE tends to have little impact on the final model parameters and resulting SWE predictions. Considering all sources of uncertainty, parameters related to air temperature and snowfall fraction exhibit the strongest correlations to site characteristics. Parameters related to the length of the melting period also show high correlation to site characteristics. Finally, model structural uncertainty and precipitation uncertainty dramatically alter parameter regionalization relationships in comparison to cases where only uncertainty in model parameters or output measurements is considered. Our results demonstrate that accurate treatment of forcing, parameter, model structural, and calibration data errors is critical for deriving robust regionalization relationships.

Mark–recapture distance sampling (MRDS) surveys with two independent observers are widely used to estimate wildlife population abundance. The analysis relies on accurate identification of duplicate sightings common to both observers, and correct judgements of group size, both of which are hard to achieve for species that exhibit complex grouping patterns.In this paper, we examine the impact of these sources of uncertainty on bias and precision of abundance estimates, using a case study of 22 aerial surveys of common dolphins (Delphinus delphis) in the Hauraki Gulf, New Zealand. We develop a novel probabilistic method to identify duplicate observations, and account for various sources of uncertainty using a simulation‐intensive approach.For our case study, identifying duplicates using reasonable but arbitrary thresholds of time and angle discrepancies created a range of abundance estimates differing by up to 20%, whereas our novel threshold‐free probabilistic analysis returned an estimate roughly central to this range. Uncertainty in group size made a smaller impact of up to 5% on abundance estimates. All analysis choices returned similar values for the coefficient of variation, from 20 to 23%.Generating robust estimates of abundance, and accounting for all associated sources of uncertainty, is critical for informing conservation management. Our novel approach provides a way to eliminate arbitrary decisions associated with MRDS, and account for a wider range of uncertainties. Our method allows for the reliable application of MRDS to a wider range of terrestrial and marine species, and will be a useful tool for producing robust abundance estimates for species that exhibit complex grouping patterns.

This paper presents a computational framework for uncertainty quantification in prognostics in the context of condition-based monitoring of aerospace systems. The different sources of uncertainty and the various uncertainty quantification activities in condition-based prognostics are outlined in detail, and it is demonstrated that the Bayesian subjective approach is suitable for interpreting uncertainty in online monitoring. A state-space model-based framework for prognostics, that can rigorously account for the various sources of uncertainty, is presented. Prognostics consists of two important steps. First, the state of the system is estimated using Bayesian tracking, and then, the future states of the system are predicted until failure, thereby computing the remaining useful life of the system. The proposed framework is illustrated using the power system of a planetary rover test-bed, which is being developed and studied at NASA Ames Research Center.

The automotive industry is experiencing a period of transition from traditional internal combustion engine (ICE) vehicles to electric vehicles. Although electric machines have always been used in many applications, they are generally designed neglecting the sources of uncertainty, even such uncertainty can lead to significant deterioration of the motor performance. The aim of this paper is to compare the results obtained from the multi-objective optimization of an interior permanent magnet synchronous motor (IPMSM) using a robust approach versus a deterministic one. Unlike other studies in the literature, this research simultaneously considers different sources of uncertainty, such as geometric parameters, magnet properties, and operating temperature, to assess the variability of electric motor performance. Different designs of a 48 slot–8 pole motor are simulated with finite element analysis, then the outputs are used to train artificial neural networks that are employed to find the optimal design with different approaches. The method incorporates an innovative use of the neural network-based variance estimation (NNVE) technique to efficiently calculate the standard deviation of the objective functions. Finally, the results of the robust optimization are compared with those of the deterministic optimization. Due to the small margin of improvement in robustness, both methods lead to similar results.