2,771 research outputs found
Multistep predictions for adaptive sampling in mobile robotic sensor networks using proximal ADMM
This paper presents a novel approach, using multi-step predictions, to the adaptive sampling problem for efficient monitoring of environmental spatial phenomena in a mobile sensor network. We employ a Gaussian process to represent the spatial field of interest, which is then used to predict the field at unmeasured locations. The adaptive sampling problem aims to drive the mobile sensors to optimally navigate the environment while the sensors adaptively take measurements of the spatial phenomena at each sampling step. To this end, an optimal sampling criterion based on conditional entropy is proposed, which minimizes the prediction uncertainty of the Gaussian process model. By predicting the measurements the mobile sensors potentially take in a finite horizon of multiple future sampling steps and exploiting the chain rule of the conditional entropy, a multi-step-ahead adaptive sampling optimization problem is formulated. Its objective is to find the optimal sampling paths for the mobile sensors in multiple sampling steps ahead. Robot-robot and robot-obstacle collision avoidance is formulated as mixed-integer constraints. Compared with the single-step-ahead approach typically adopted in the literature, our approach provides better navigation, deployment, and data collection with more informative sensor readings. However, the resulting mixed-integer nonlinear program is highly complex and intractable. We propose to employ the proximal alternating direction method of multipliers to efficiently solve this problem. More importantly, the solution obtained by the proposed algorithm is theoretically guaranteed to converge to a stationary value. The effectiveness of our proposed approach was extensively validated by simulation using a real-world dataset, which showed highly promising results. © 2013 IEEE
Secure Multi-Robot Adaptive Information Sampling with Continuous, Periodic and Opportunistic Connectivity
Multi-robot teams are an increasingly popular approach for information gathering in large geographic areas, with applications in precision agriculture, natural disaster aftermath surveying, and pollution tracking. In a coordinated multi-robot information sampling scenario, robots share their collected information amongst one another to form better predictions. These robot teams are often assembled from untrusted devices, making the verification of the integrity of the collected samples an important challenge. Furthermore, such robots often operate under conditions of continuous, periodic, or opportunistic connectivity and are limited in their energy budget and computational power. In this thesis, we study how to secure the information being shared in a multi-robot network against integrity attacks and the cost of integrating such techniques. We propose a blockchain-based information sharing protocol that allows robots to reject fake data injection by a malicious entity. However, optimal information sampling is a resource-intensive technique, as are the popular blockchain-based consensus protocols. Therefore, we also study its impact on the execution time of the sampling algorithm, which affects the energy spent. We propose algorithms that build on blockchain technology to address the data integrity problem, but also take into account the limitations of the robots’ resources and communication. We evaluate the proposed algorithms along the perspective of the trade-offs between data integrity, model accuracy, and time consumption under continuous, periodic, and opportunistic connectivity
Decentralized Learning With Limited Communications for Multi-robot Coverage of Unknown Spatial Fields
This paper presents an algorithm for a team of mobile robots to
simultaneously learn a spatial field over a domain and spatially distribute
themselves to optimally cover it. Drawing from previous approaches that
estimate the spatial field through a centralized Gaussian process, this work
leverages the spatial structure of the coverage problem and presents a
decentralized strategy where samples are aggregated locally by establishing
communications through the boundaries of a Voronoi partition. We present an
algorithm whereby each robot runs a local Gaussian process calculated from its
own measurements and those provided by its Voronoi neighbors, which are
incorporated into the individual robot's Gaussian process only if they provide
sufficiently novel information. The performance of the algorithm is evaluated
in simulation and compared with centralized approaches.Comment: Accepted IROS 202
Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes
Robotic Information Gathering (RIG) is a foundational research topic that
answers how a robot (team) collects informative data to efficiently build an
accurate model of an unknown target function under robot embodiment
constraints. RIG has many applications, including but not limited to autonomous
exploration and mapping, 3D reconstruction or inspection, search and rescue,
and environmental monitoring. A RIG system relies on a probabilistic model's
prediction uncertainty to identify critical areas for informative data
collection. Gaussian Processes (GPs) with stationary kernels have been widely
adopted for spatial modeling. However, real-world spatial data is typically
non-stationary -- different locations do not have the same degree of
variability. As a result, the prediction uncertainty does not accurately reveal
prediction error, limiting the success of RIG algorithms. We propose a family
of non-stationary kernels named Attentive Kernel (AK), which is simple, robust,
and can extend any existing kernel to a non-stationary one. We evaluate the new
kernel in elevation mapping tasks, where AK provides better accuracy and
uncertainty quantification over the commonly used stationary kernels and the
leading non-stationary kernels. The improved uncertainty quantification guides
the downstream informative planner to collect more valuable data around the
high-error area, further increasing prediction accuracy. A field experiment
demonstrates that the proposed method can guide an Autonomous Surface Vehicle
(ASV) to prioritize data collection in locations with significant spatial
variations, enabling the model to characterize salient environmental features.Comment: International Journal of Robotics Research (IJRR). arXiv admin note:
text overlap with arXiv:2205.0642
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