GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search

Robotic solutions for quick disaster response are essential to ensure minimal loss of life, especially when the search area is too dangerous or too vast for human rescuers. We model this problem as an asynchronous multi-agent active-search task where each robot aims to efficiently seek objects of interest (OOIs) in an unknown environment. This formulation addresses the requirement that search missions should focus on quick recovery of OOIs rather than full coverage of the search region. Previous approaches fail to accurately model sensing uncertainty, account for occlusions due to foliage or terrain, or consider the requirement for heterogeneous search teams and robustness to hardware and communication failures. We present the Generalized Uncertainty-aware Thompson Sampling (GUTS) algorithm, which addresses these issues and is suitable for deployment on heterogeneous multi-robot systems for active search in large unstructured environments. We show through simulation experiments that GUTS consistently outperforms existing methods such as parallelized Thompson Sampling and exhaustive search, recovering all OOIs in 80% of all runs. In contrast, existing approaches recover all OOIs in less than 40% of all runs. We conduct field tests using our multi-robot system in an unstructured environment with a search area of approximately 75,000 sq. m. Our system demonstrates robustness to various failure modes, achieving full recovery of OOIs (where feasible) in every field run, and significantly outperforming our baseline.Comment: 7 pages, 5 figures, 1 table, for associated video see: https://youtu.be/K0jkzdQ_j2E , to appear in International Conference on Robotics and Automation (ICRA) 202

Object Counting and Localization: A Statistical Approach

Scene understanding is fundamental to many computer vision applications such as autonomous driving, robot navigation and human-machine interaction; visual object counting and localization are important building blocks of scene understanding. In this dissertation, we present: (1) a framework that employs doubly stochastic Poisson (Cox) processes to estimate the number of instances of an object in an image and (2) a Bayesian model that localizes multiple instances of an object using counts from image sub-regions. Poisson processes are well-suited for modeling events that occur randomly in space, such as the location of objects in an image or the enumeration of objects in a scene. The proposed algorithm selects a subset of bounding boxes in the image domain, then queries them for the presence of the object of interest by running a pre-trained convolutional neural net (CNN) classifier. The resulting observations are then aggregated, and a posterior distribution over the intensity of a Cox process is computed. This intensity function is summed up, providing an estimator of the number of instances of the object over the entire image. Despite the flexibility and versatility of Poisson processes, their application to large datasets is limited, as their computational complexity and storage requirements do not easily scale with image size, typically requiring $O(n^3)$ computation time and $O(n^2)$ storage, where $n$ is the number of observations. To mitigate this problem, we employ the Kronecker algebra, which takes advantage of the tensor product structure of covariance matrices. As the likelihood is non-Gaussian, the Laplace approximation is used for inference, employing the conjugate gradient and Newton's method. Our approach has then close to linear performance, requiring only $O(n^{3/2})$ computation time and $O(n)$ memory. We demonstrate the counting results on both simulated data and real-world datasets, comparing the results with state-of-the-art counting methods. We then extend this framework by noting that most object detection and classification systems rely upon the use of region proposal networks or upon classifying the ``objectness'' of specific sub-windows to help detect potential object locations within an image. We use our Cox model to convert such region proposals to a well-defined Poisson intensity. This output can be used as-is to directly estimate object counts, or can be plugged into pre-existing object detection frameworks to improve their counting and detection performance. This remapping does not require the original network to be re-trained: the parameters of the model can be estimated analytically from the training data. Furthermore, we consider the problem of quickly localizing multiple instances of an object by asking questions of the form ``How many instances are there in this set?", while obtaining noisy answers. We evaluate the performance of the partitioning \textit{policy} using the expected entropy of the posterior distribution after a fixed number of questions with noisy answers. We derive a lower bound for the value of this problem and study a specific policy, named the \textit{dyadic policy}. We show that this policy achieves a value which is no more than twice this lower bound when answers are noise-free, and show a more general constant factor approximation guarantee for the noisy setting. We present an empirical evaluation of this policy on simulated data for the problem of detecting multiple instances of the same object in an image. Finally, we present experiments on localizing multiple objects simultaneously on real images

Models and algorithms for multi-agent search problems

The problem of searching for objects of interest occurs in important applications ranging from rescue, security, transportation, to medicine. With the increasing use of autonomous vehicles as search platforms, there is a need for fast algorithms that can generate search plans for multiple agents in response to new information. In this dissertation, we develop new techniques for automated generation of search plans for different classes of search problems. First, we study the problem of searching for a stationary object in a discrete search space with multiple agents where each agent can access only a subset of the search space. In these problems, agents can fail to detect an object when inspecting a location. We show that when the probabilities of detection only depend on the locations, this problem can be reformulated as a minimum cost network optimization problem, and develop a fast specialized algorithm for the solution. We prove that our algorithm finds the optimal solution in finite time, and has worst-case computation performance that is faster than general minimum cost flow algorithms. We then generalize it to the case where the probabilities of detection depend on the agents and the locations, and propose a greedy algorithm that is 1/2-approximate. Second, we study the problem of searching for a moving object in a discrete search space with multiple agents where each agent can access only a subset of a discrete search space at any time and agents can fail to detect objects when searching a location at a given time. We provide necessary conditions for an optimal search plan, extending prior results in search theory. For the case where the probabilities of detection depend on the locations and the time periods, we develop a forward-backward iterative algorithm based on coordinate descent techniques to obtain solutions. To avoid local optimum, we derive a convex relaxation of the dynamic search problem and show this can be solved optimally using coordinate descent techniques. The solutions of the relaxed problem are used to provide random starting conditions for the iterative algorithm. We also address the problem where the probabilities of detection depend on the agents as well as the locations and the time periods, and show that a greedy-style algorithm is 1/2-approximate. Third, we study problems when multiple objects of interest being searched are physically scattered among locations on a graph and the agents are subject to motion constraints captured by the graph edges as well as budget constraints. We model such problem as an orienteering problem, when searching with a single agent, or a team orienteering problem, when searching with multiple agents. We develop novel real-time efficient algorithms for both problems. Fourth, we investigate classes of continuous-region multi-agent adaptive search problems as stochastic control problems with imperfect information. We allow the agent measurement errors to be either correlated or independent across agents. The structure of these problems, with objectives related to information entropy, allows for a complete characterization of the optimal strategies and the optimal cost. We derive a lower bound on the performance of the minimum mean-square error estimator, and provide upper bounds on the estimation error for special cases. For agents with independent errors, we show that the optimal sensing strategies can be obtained in terms of the solution of decoupled scalar convex optimization problems, followed by a joint region selection procedure. We further consider search of multiple objects and provide an explicit construction for adaptively determining the sensing actions