Volumetric Procedural Models for Shape Representation

This article describes a volumetric approach for procedural shape modeling and a new Procedural Shape Modeling Language (PSML) that facilitates the specification of these models. PSML provides programmers the ability to describe shapes in terms of their 3D elements where each element may be a semantic group of 3D objects, e.g., a brick wall, or an indivisible object, e.g., an individual brick. Modeling shapes in this manner facilitates the creation of models that more closely approximate the organization and structure of their real-world counterparts. As such, users may query these models for volumetric information such as the number, position, orientation and volume of 3D elements which cannot be provided using surface based model-building techniques. PSML also provides a number of new language-specific capabilities that allow for a rich variety of context-sensitive behaviors and post-processing functions. These capabilities include an object-oriented approach for model design, methods for querying the model for component-based information and the ability to access model elements and components to perform Boolean operations on the model parts. PSML is open-source and includes freely available tutorial videos, demonstration code and an integrated development environment to support writing PSML programs

Information-Aware Guidance for Magnetic Anomaly based Navigation

In the absence of an absolute positioning system, such as GPS, autonomous vehicles are subject to accumulation of positional error which can interfere with reliable performance. Improved navigational accuracy without GPS enables vehicles to achieve a higher degree of autonomy and reliability, both in terms of decision making and safety. This paper details the use of two navigation systems for autonomous agents using magnetic field anomalies to localize themselves within a map; both techniques use the information content in the environment in distinct ways and are aimed at reducing the localization uncertainty. The first method is based on a nonlinear observability metric of the vehicle model, while the second is an information theory based technique which minimizes the expected entropy of the system. These conditions are used to design guidance laws that minimize the localization uncertainty and are verified both in simulation and hardware experiments are presented for the observability approach.Comment: 2022 International Conference on Intelligent Robots and Systems October 23 to 27, 2022 Kyoto, Japa

DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability

Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model's training data. DL models that perform well on in-domain test data could struggle on OOD data. Overcoming this discrepancy is essential to the reliable deployment of DL. Proper model calibration decreases the number of spurious connections that are made between model features and class outputs. Hence, calibrated DL can improve OOD generalization by only learning features that are truly indicative of the respective classes. Previous work proposed domain-aware model calibration (DOMINO) to improve DL calibration, but it lacks designs for model generalizability to OOD data. In this work, we propose DOMINO++, a dual-guidance and dynamic domain-aware loss regularization focused on OOD generalizability. DOMINO++ integrates expert-guided and data-guided knowledge in its regularization. Unlike DOMINO which imposed a fixed scaling and regularization rate, DOMINO++ designs a dynamic scaling factor and an adaptive regularization rate. Comprehensive evaluations compare DOMINO++ with DOMINO and the baseline model for head tissue segmentation from magnetic resonance images (MRIs) on OOD data. The OOD data consists of synthetic noisy and rotated datasets, as well as real data using a different MRI scanner from a separate site. DOMINO++'s superior performance demonstrates its potential to improve the trustworthy deployment of DL on real clinical data.Comment: 12 pages, 5 figures, 5 tables, Accepted by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 202

Cooperative control methods for the weapon target assignment problem

Weapon target assignment (WTA) is a combinatorial optimization problem in which a set of weapons must selectively engage a set of targets in order to minimize the expected survival value of the targets. In its distributed form, it is also an important problem in autonomous, multi-agent robotics. In this work, distributed methods are explored for a modified weapon target assignment problem in which weapons seek to achieve a specified probability of kill on each target. Three novel cost functions are proposed which, in cases with low agent-to-target ratios, induce behaviors which may be preferable to the behaviors induced by classical cost functions. The performance of these proposed cost functions is explored in simulation of both homogeneous and heterogeneous engagement scenarios using, as an example, airborne autonomous weapons. Simulation results demonstrate that the proposed cost functions achieve the specified desired behaviors in cases with low agent-to-target ratios where efficient use of weapons is particularly important. Additionally, a multi-objective version of the WTA problem is considered in which the quality of an assignment is dependent on both the total effectiveness of the weapons assigned to each target, and the relative timing of agents' arrival at their targets. Such timing constraints may be important in real-world scenarios where a mission planner wishes to enforce an element of surprise on each target. A fourth cost function is presented which couples weapon effectiveness and timing metrics into a combined cost. In cases where weapon-target closing speeds are limited to a certain range, this combined cost allows the inclusion of arrival time constraints in the assignment decision process. The performance of this new cost function is demonstrated through theoretical analysis and simulation. Results show that the proposed cost function balances the dual goals of optimizing effectiveness and arrival time considerations under closing speed limitations, and that a user-defined tuning parameter can be used to adjust the priority of the dual goals of sequenced arrival and achieving the desired probability of kill.Ph.D

Low-Bandwidth and Compute-Bound RGB-D Planar Semantic SLAM

Visual simultaneous location and mapping (SLAM) using RGB-D cameras has been a necessary capability for intelligent mobile robots. However, when using point-cloud map representations as most RGB-D SLAM systems do, limitations in onboard compute resources, and especially communication bandwidth can significantly limit the quantity of data processed and shared. This article proposes techniques that help address these challenges by mapping point clouds to parametric models in order to reduce computation and bandwidth load on agents. This contribution is coupled with a convolutional neural network (CNN) that extracts semantic information. Semantics provide guidance in object modeling which can reduce the geometric complexity of the environment. Pairing a parametric model with a semantic label allows agents to share the knowledge of the world with much less complexity, opening a door for multi-agent systems to perform complex tasking, and human–robot cooperation. This article takes the first step towards a generalized parametric model by limiting the geometric primitives to a planar surface and providing semantic labels when appropriate. Two novel compression algorithms for depth data and a method to independently fit planes to RGB-D data are provided, so that plane data can be used for real-time odometry estimation and mapping. Additionally, we extend maps with semantic information predicted from sparse geometries (planes) by a CNN. In experiments, the advantages of our approach in terms of computational and bandwidth resources savings are demonstrated and compared with other state-of-the-art SLAM systems