Indoor disaster relief and rescue missions require quadrotors to fully exploit their maneuverability in real-time. However, the computational complexity induced by the underactuated kinodynamics conflicts with the rapid replanning requirement. For agile trajectory planning in cluttered and unknown environments, we propose a real-time optimization-based quadrotor trajectory generation method that integrates kinodynamic constraints in both trajectory search and trajectory optimization phases to fully exploit maneuverability. To further improve efficiency, we introduce a waypoints selection strategy to reduce the computational burden of kinodynamic trajectory optimization by transforming obstacle avoidance constraints into waypoint constraints, thereby enabling safe trajectory optimization in real-time. Specifically, kinodynamic trajectories are searched under kinodynamic constraints, providing reliable initial values for subsequent numerical optimization. Nextly, a waypoints selection algorithm, based on an estimation of trajectory variation during optimization, is introduced to preserve the obstacle-avoidance properties obtained during the search phase by limiting the variation with waypoint constraints. Finally, trajectory is segmented by waypoints with fixed time intervals each segment and then optimized under kinodynamic constraints, ensuring real-time optimization at the cost of time allocation optimality. We evaluated our method through simulation and experimentally validate its performance in cluttered and unknown environments. The competence of proposed method is also validated in real-world experiments.

With the prevailing development of the internet and sensors, various streaming raw data are generated continually. However, traditional clustering algorithms are unfavorable for discovering the underlying patterns of incremental data in time; clustering accuracy cannot be assured if fixed parameters clustering algorithms are used to handle incremental data. In this paper, an Incremental-Density-Micro-Clustering (IDMC) framework is proposed to address this concern. To reduce the succeeding clustering computation, we design the Dynamic-microlocal-clustering method to merge samples from streaming data into dynamic microlocal clusters. Beyond that, the Density-center-based neighborhood search method is proposed for periodically merging microlocal clusters to global clusters automatically; at the same time, these global clusters are updated by the Dynamic-cluster-increasing method with data streaming in each period. In this way, IDMC processes sensor data with less computational time and memory, improves the clustering performance, and simplifies the parameter choosing in conventional and stream data clustering. Finally, experiments are conducted to validate the proposed clustering framework on UCI datasets and streaming data generated by IoT sensors. As a result, this work advances the state-of-the-art of incremental clustering algorithms in the field of sensors’ streaming data analysis.

Planetary gearboxes (PGs) are significant transmission chains in industrial applications. Scientifically perceiving its operational security domain (OSD) is crucial for reliability analysis and preventive maintenance. Unfortunately, most security domain analysis methods discard the abundant information from the fault diagnosis indicator and measured data. To this end, this article proposes a frequency energy ratio (FER) based strategy to intelligently diagnose gear faults, identify the OSD and perform a reliability analysis of the OSD. The FER cell is constructed to consider multiple sideband numbers and bandwidth conditions. The optimal parameter pair among FER cells is determined by a novel distance measurement strategy, namely intra-inter class distance metric, which combines deep autoencoder and relatively max–minimum distance metric. The optimal FER can be conveniently applied to diagnose the remaining data. More importantly, the optimal FER with different fault-induced frequencies is picked to identify the OSD via minor training data. The failure probability of a large number of vibration data is calculated to verify the effectiveness of the OSD. Experimental results demonstrate that FER owns stronger robustness and diagnostic accuracy than the traditional sideband energy ratio. The proposed methods bridge the fault diagnosis and reliability analysis, which have a bright perspective in perceiving operational conditions from research and engineering fields.

The vast amount of data captured by robots in large-scale environments brings the computing and storage bottlenecks to the typical methods of modeling the spaces the robots travel in. In order to efficiently construct a compact terrain model from uncertain, incomplete point cloud data of large-scale environments, in this paper, we first propose a novel feature mapping method, named random mapping, based on the fast random construction of base functions, which can efficiently project the messy points in the low-dimensional space into the high-dimensional space where the points are approximately linearly distributed. Then, in this mapped space, we propose to learn a continuous linear regression model to represent the terrain. We show that this method can model the environments in much less computation time, memory consumption, and access time, with high accuracy. Furthermore, the models possess the generalization capabilities comparable to the performances on the training set, and its inference accuracy gradually increases as the random mapping dimension increases. To better solve the large-scale environmental modeling problem, we adopt the idea of parallel computing to train the models. This strategy greatly reduces the wall-clock time of calculation without losing much accuracy. Experiments show the effectiveness of the random mapping method and the effects of some important parameters on its performance. Moreover, we evaluate the proposed terrain modeling method based on the random mapping method and compare its performances with popular typical methods and state-of-art methods.

To address the problem of building accurate and coherent models for large-scale terrains from incomplete and noisy sensor data, this paper proposes a novel framework that can efficiently infer terrain structures by divisionally providing the best linear unbiased estimates for the elevation values. To avoid data ambiguity caused by the uncertainty of sensor data, the proposed method introduces elevation filtering to extract the terrain surfaces, which reduces the amount of data greatly while the contained terrain information is basically unchanged. Then, for the large-scale terrains, the Gaussian mixture model is used to divide the interested regions, which remarkably improves the prediction accuracy and speed. Finally, for each subregion, a gaussian process regression model based on the static kernel is used to create a multiresolution terrain representation, which can deal with incompleteness of sensor data by considering the spatial correlations of the terrain. Evaluations of the proposed technique were conducted on diverse large-scale field terrains, including the quarry, planetary emulation terrain and highland, showing that the proposed method outperforms the state-of-art terrain modeling techniques in terms of the prediction accuracy, computation speed and memory consumption. As a practical application, the path planning problem was explored based on this terrain modeling technique to produce a better path.

Pedestrian trajectories and actions prediction in complex environment is challenging due to the complexity of human behavior and a variety of internal and external stimuli. Much works has gone towards predicting trajectories and actions separately without mining the coupling relationships between them, which is an important information for our humans to reason and predict. Inspired by this, we propose an end-to-end joint context information iterative reasoning network (CIR-Net). Specifically, a novel heterogeneous spatiotemporal graph module (HST-Graph) is proposed to encode and aggregate multiple types of context information of the motion pattern and the scene. And an action-trajectory hybrid guidance module is proposed to enhance the ability of long-time prediction by utilizing the internal coupling between actions and trajectory. Moreover, an iterative reasoning structure is designed to iteratively correcting the trajectory and actions prediction error. Experimental results on the ETH&UCY and VIRAT datasets demonstrate the favorable performance of the framework.

Generating meaningful spatial models of physical environments is a crucial ability for autonomous navigation of mobile robots. This paper considers the problem of building continuous occupancy maps from sparse and noisy sensor data. To this end, we propose a new method named random mapping maps that advances the popular methods in two aspects. Firstly, it can represent environment models in a memory-saving and time-saving manner by randomly mapping a low-dimensional feature space to a high-dimensional one where a linear model is learnt. Secondly, it can rapidly obtain accurate inferences of the occupancy states of the spatial locations. This technique is based on the random mapping that projects the measurement data into a random feature space in which a discriminative model is learnt by the available data. It can asymptotically represent the complexity of the real world as the mapping dimension increases. Evaluations of the proposed method were conducted on various environments to verify its availability to environment modeling. Its performances in terms of time and memory consumptions were evaluated quantitatively. Finally, as a practical application, experiments about path planning were conducted based on the gradients of the proposed representation of environment model.

In this paper, a robust recurrent neural network is presented in a Bayesian framework based on echo state mechanisms. Since the new model is capable of handling outliers in the training data set, it is termed as a robust echo state network (RESN). The RESN inherits the basic idea of ESN learning in a Bayesian framework, but replaces the commonly used Gaussian distribution with a Laplace one, which is more robust to outliers, as the likelihood function of the model output. Moreover, the training of the RESN is facilitated by employing a bound optimization algorithm, based on which, a proper surrogate function is derived and the Laplace likelihood function is approximated by a Gaussian one, while remaining robust to outliers. It leads to an efficient method for estimating model parameters, which can be solved by using a Bayesian evidence procedure in a fully autonomous way. Experimental results show that the proposed method is robust in the presence of outliers and is superior to existing methods.

This paper proposes an improved nonlinear canonical correlation analysis algorithm named radial basis function canonical correlation analysis (RBFCCA) for multivariate chaotic time series analysis and prediction. This algorithm follows the key idea of kernel canonical correlation analysis (KCCA) method to make a nonlinear mapping of the original data sets firstly with a RBF network and a linear neural network. Then linear CCA is performed using the transformed nonlinear data sets, which corresponds to make nonlinear CCA of the original data. A modified cost function of the neural network with Lagrange multipliers and a joint learning rule based on gradient ascent algorithm which maximizes the correlation coefficient of the network outputs is used to extract the maximal correlation pattern between the input and output of a prediction model. Finally, a regression model is constructed to implement the prediction problem. The performance of RBFCCA prediction algorithm is demonstrated via the prediction problem of Lorenz time series and some practical observed time series. The results compared with the traditional neural network method and the KCCA method indicate that the RBFCCA algorithm proposed in this paper is able to capture the dynamics of complex systems and give reliable prediction accuracy.