Autonomous robots can survey and monitor large environments. However, these robots often have limited compu-tational and power resources, making it crucial to develop an ef-ficient and adaptive informative path planning (IPP) algorithm. Such an algorithm must quickly adapt to environmental data to maximize the information collected while accommodating path constraints, such as distance budgets and boundary limitations. Current approaches to this problem often rely on maximizing mutual information using methods such as greedy algorithms, Bayesian optimization, and genetic algorithms. These methods can be slow and do not scale well to large or 3D environments. We present an adaptive IPP approach that is fully differentiable, significantly faster than previous methods, and scalable to 3D spaces. Our approach also supports continuous sensing robots, which collect data continuously along the entire path, by leveraging streaming sparse Gaussian processes. Benchmark results on two real-world datasets demonstrate that our approach yields solutions that are on par with or better than baseline methods while being up to two orders of magnitude faster. Additionally, we showcase our adaptive IPP approach in a 3D space using a system-on-chip embedded computer with minimal computational resources. Our code is available in the SGP- Tools Python library with a companion ROS 2 package for deployment on ArduPilot-based robots.

This paper addresses multi-robot informative path planning (IPP) for environmental monitoring. The problem involves determining informative regions in the environment that should be visited by robots to gather the most information about the environment. We propose an efficient sparse Gaussian process-based approach that uses gradient descent to optimize paths in continuous environments. Our approach efficiently scales to both spatially and spatio-temporally correlated environments. Moreover, our approach can simultaneously optimize the informative paths while accounting for routing constraints, such as a distance budget and limits on the robot’s velocity and acceleration. Our approach can be used for IPP with both discrete and continuous sensing robots, with point and non-point field-of-view sensing shapes, and for both single and multi-robot IPP. We demonstrate that the proposed approach is fast and accurate on real-world data.

This paper proposes NeuralWave, an intelligent and non-intrusive user identification system based on human gait biometrics extracted from WiFi signals. In particular, the channel state information (CSI)measurements are first collected from commodity WiFi devices. Then, a collection of data preprocessing schemes are applied to sanitize and calibrate the noisy and erroneous CSI data samples to manifest and augment the gait-induced radio-frequency (RF)signatures. Next, a 23-layer deep convolutional neural network, namely RadioNet, is developed to automatically learn the salient features from the preprocessed CSI data samples. The extracted features constitute a latent representation for the gait biometric that is discriminative enough to distinguish one person from another. Using the latent biometric representation, a softmax multi-class classifier is adopted to achieve accurate user identification. Extensive experiments in a typical indoor environment are conducted to show the effectiveness of our system. In particular, NeuralWave can achieve 87.76 ± 2.14% user identification accuracy for a group of 24 people. To the best of our knowledge, NeuralWave is the first in the literature to exploit deep learning for feature extraction and classification of physiological and behavioral gait biometrics embedded in CSI signals from commodity WiFi.