Robin Murphy

Thomas Manzini, Priyankari Perali, Raisa Karnik et al. · microsoft-research

This document presents the Center for Robot Assisted Search And Rescue - Uncrewed Aerial Systems - Disaster Response Overhead Inspection Dataset (CRASAR-U-DROIDs) for building damage assessment and spatial alignment collected from small uncrewed aerial systems (sUAS) geospatial imagery. This dataset is motivated by the increasing use of sUAS in disaster response and the lack of previous work in utilizing high-resolution geospatial sUAS imagery for machine learning and computer vision models, the lack of alignment with operational use cases, and with hopes of enabling further investigations between sUAS and satellite imagery. The CRASAR-U-DRIODs dataset consists of fifty-two (52) orthomosaics from ten (10) federally declared disasters (Hurricane Ian, Hurricane Ida, Hurricane Harvey, Hurricane Idalia, Hurricane Laura, Hurricane Michael, Musset Bayou Fire, Mayfield Tornado, Kilauea Eruption, and Champlain Towers Collapse) spanning 67.98 square kilometers (26.245 square miles), containing 21,716 building polygons and damage labels, and 7,880 adjustment annotations. The imagery was tiled and presented in conjunction with overlaid building polygons to a pool of 130 annotators who provided human judgments of damage according to the Joint Damage Scale. These annotations were then reviewed via a two-stage review process in which building polygon damage labels were first reviewed individually and then again by committee. Additionally, the building polygons have been aligned spatially to precisely overlap with the imagery to enable more performant machine learning models to be trained. It appears that CRASAR-U-DRIODs is the largest labeled dataset of sUAS orthomosaic imagery.

Thomas Manzini, Robin Murphy · microsoft-research

This paper details the challenges in applying two computer vision systems, an EfficientDET supervised learning model and the unsupervised RX spectral classifier, to 98.9 GB of drone imagery from the Wu-Murad wilderness search and rescue (WSAR) effort in Japan and identifies 3 directions for future research. There have been at least 19 proposed approaches and 3 datasets aimed at locating missing persons in drone imagery, but only 3 approaches (2 unsupervised and 1 of an unknown structure) are referenced in the literature as having been used in an actual WSAR operation. Of these proposed approaches, the EfficientDET architecture and the unsupervised spectral RX classifier were selected as the most appropriate for this setting. The EfficientDET model was applied to the HERIDAL dataset and despite achieving performance that is statistically equivalent to the state-of-the-art, the model fails to translate to the real world in terms of false positives (e.g., identifying tree limbs and rocks as people), and false negatives (e.g., failing to identify members of the search team). The poor results in practice for algorithms that showed good results on datasets suggest 3 areas of future research: more realistic datasets for wilderness SAR, computer vision models that are capable of seamlessly handling the variety of imagery that can be collected during actual WSAR operations, and better alignment on performance measures.

Robin Murphy, Thomas Manzini · microsoft-research

This paper describes gaps in acquisition of drone imagery that impair the use with computer vision/machine learning (CV/ML) models and makes five recommendations to maximize image suitability for CV/ML post-processing. It describes a notional work process for the use of drones in wilderness search and rescue incidents. The large volume of data from the wide area search phase offers the greatest opportunity for CV/ML techniques because of the large number of images that would otherwise have to be manually inspected. The 2023 Wu-Murad search in Japan, one of the largest missing person searches conducted in that area, serves as a case study. Although drone teams conducting wide area searches may not know in advance if the data they collect is going to be used for CV/ML post-processing, there are data collection procedures that can improve the search in general with automated collection software. If the drone teams do expect to use CV/ML, then they can exploit knowledge about the model to further optimize flights.

Jayesh Tripathi, Robin Murphy

Non-navigable rivers and retention ponds play important roles in buffering communities from flooding, yet emergency planners often have no data as to the volume of water that they can carry before flooding the surrounding. This paper describes a practical approach for using an uncrewed marine surface vehicle (USV) to collect and merge bathymetric maps with digital surface maps of the banks of shallow bodies of water into a unified volumetric model. The below-waterline mesh is developed by applying the Poisson surface reconstruction algorithm to the sparse sonar depth readings of the underwater surface. Dense above-waterline meshes of the banks are created using commercial structure from motion (SfM) packages. Merging is challenging for many reasons, the most significant is gaps in sensor coverage, i.e., the USV cannot collect sonar depth data or visually see sandy beaches leading to a bank thus the two meshes may not intersect. The approach is demonstrated on a Hydronalix EMILY USV with a Humminbird single beam echosounder and Teledyne FLIR camera at Lake ESTI at the Texas A&M Engineering Extension Service Disaster City complex.

Thomas Manzini, Priyankari Perali, Jayesh Tripathi et al. · microsoft-research

This paper audits damage labels derived from coincident satellite and drone aerial imagery for 15,814 buildings across Hurricanes Ian, Michael, and Harvey, finding 29.02% label disagreement and significantly different distributions between the two sources, which presents risks and potential harms during the deployment of machine learning damage assessment systems. Currently, there is no known study of label agreement between drone and satellite imagery for building damage assessment. The only prior work that could be used to infer if such imagery-derived labels agree is limited by differing damage label schemas, misaligned building locations, and low data quantities. This work overcomes these limitations by comparing damage labels using the same damage label schemas and building locations from three hurricanes, with the 15,814 buildings representing 19.05 times more buildings considered than the most relevant prior work. The analysis finds satellite-derived labels significantly under-report damage by at least 20.43% compared to drone-derived labels (p<1.2x10^-117), and satellite- and drone-derived labels represent significantly different distributions (p<5.1x10^-175). This indicates that computer vision and machine learning (CV/ML) models trained on at least one of these distributions will misrepresent actual conditions, as the differing satellite and drone-derived distributions cannot simultaneously represent the distribution of actual conditions in a scene. This potential misrepresentation poses ethical risks and potential societal harm if not managed. To reduce the risk of future societal harms, this paper offers four recommendations to improve reliability and transparency to decisio-makers when deploying CV/ML damage assessment systems in practice

Thomas Manzini, Priyankari Perali, Raisa Karnik et al. · microsoft-research

This work presents the first quantitative study of alignment errors between small uncrewed aerial systems (sUAS) georectified imagery and a priori building polygons and finds that alignment errors are non-uniform and irregular, which negatively impacts field robotics systems and human-robot interfaces that rely on geospatial information. There are no efforts that have considered the alignment of a priori spatial data with georectified sUAS imagery, possibly because straight-forward linear transformations often remedy any misalignment in satellite imagery. However, an attempt to develop machine learning models for an sUAS field robotics system for disaster response from nine wide-area disasters using the CRASAR-U-DROIDs dataset uncovered serious translational alignment errors. The analysis considered 21,608 building polygons in 51 orthomosaic images, covering 16787.2 Acres (26.23 square miles), and 7,880 adjustment annotations, averaging 75.36 pixels and an average intersection over union of 0.65. Further analysis found no uniformity among the angle and distance metrics of the building polygon alignments, presenting an average circular variance of 0.28 and an average distance variance of 0.45 pixels2, making it impossible to use the linear transform used to align satellite imagery. The study's primary contribution is alerting field robotics and human-robot interaction (HRI) communities to the problem of spatial alignment and that a new method will be needed to automate and communicate the alignment of spatial data in sUAS georectified imagery. This paper also contributes a description of the updated CRASAR-U-DROIDs dataset of sUAS imagery, which contains building polygons and human-curated corrections to spatial misalignment for further research in field robotics and HRI.

Maryam Rahnemoonfar, Tashnim Chowdhury, Robin Murphy

Recent advancements in computer vision and deep learning techniques have facilitated notable progress in scene understanding, thereby assisting rescue teams in achieving precise damage assessment. In this paper, we present RescueNet, a meticulously curated high-resolution post-disaster dataset that includes detailed classification and semantic segmentation annotations. This dataset aims to facilitate comprehensive scene understanding in the aftermath of natural disasters. RescueNet comprises post-disaster images collected after Hurricane Michael, obtained using Unmanned Aerial Vehicles (UAVs) from multiple impacted regions. The uniqueness of RescueNet lies in its provision of high-resolution post-disaster imagery, accompanied by comprehensive annotations for each image. Unlike existing datasets that offer annotations limited to specific scene elements such as buildings, RescueNet provides pixel-level annotations for all classes, including buildings, roads, pools, trees, and more. Furthermore, we evaluate the utility of the dataset by implementing state-of-the-art segmentation models on RescueNet, demonstrating its value in enhancing existing methodologies for natural disaster damage assessment.

Maryam Rahnemoonfar, Tashnim Chowdhury, Argho Sarkar et al.

Visual scene understanding is the core task in making any crucial decision in any computer vision system. Although popular computer vision datasets like Cityscapes, MS-COCO, PASCAL provide good benchmarks for several tasks (e.g. image classification, segmentation, object detection), these datasets are hardly suitable for post disaster damage assessments. On the other hand, existing natural disaster datasets include mainly satellite imagery which have low spatial resolution and a high revisit period. Therefore, they do not have a scope to provide quick and efficient damage assessment tasks. Unmanned Aerial Vehicle(UAV) can effortlessly access difficult places during any disaster and collect high resolution imagery that is required for aforementioned tasks of computer vision. To address these issues we present a high resolution UAV imagery, FloodNet, captured after the hurricane Harvey. This dataset demonstrates the post flooded damages of the affected areas. The images are labeled pixel-wise for semantic segmentation task and questions are produced for the task of visual question answering. FloodNet poses several challenges including detection of flooded roads and buildings and distinguishing between natural water and flooded water. With the advancement of deep learning algorithms, we can analyze the impact of any disaster which can make a precise understanding of the affected areas. In this paper, we compare and contrast the performances of baseline methods for image classification, semantic segmentation, and visual question answering on our dataset.

Gregory Hager, Vijay Kumar, Robin Murphy et al.

The recent coronavirus pandemic has highlighted the many challenges faced by the healthcare, public safety, and economic systems when confronted with a surge in patients that require intensive treatment and a population that must be quarantined or shelter in place. The most obvious and pressing challenge is taking care of acutely ill patients while managing spread of infection within the care facility, but this is just the tip of the iceberg if we consider what could be done to prepare in advance for future pandemics. Beyond the obvious need for strengthening medical knowledge and preparedness, there is a complementary need to anticipate and address the engineering challenges associated with infectious disease emergencies. Robotic technologies are inherently programmable, and robotic systems have been adapted and deployed, to some extent, in the current crisis for such purposes as transport, logistics, and disinfection. As technical capabilities advance and as the installed base of robotic systems increases in the future, they could play a much more significant role in future crises. This report is the outcome of a virtual workshop co-hosted by the National Academy of Engineering (NAE) and the Computing Community Consortium (CCC) held on July 9-10, 2020. The workshop consisted of over forty participants including representatives from the engineering/robotics community, clinicians, critical care workers, public health and safety experts, and emergency responders. It identifies key challenges faced by healthcare responders and the general population and then identifies robotic/technological responses to these challenges. Then it identifies the key research/knowledge barriers that need to be addressed in developing effective, scalable solutions. Finally, the report ends with the following recommendations on how to implement this strategy.

Maryam Rahnemoonfar, Tashnim Chowdhury, Robin Murphy et al.

In this paper, we present a large-scale hurricane Michael dataset for visual perception in disaster scenarios, and analyze state-of-the-art deep neural network models for semantic segmentation. The dataset consists of around 2000 high-resolution aerial images, with annotated ground-truth data for semantic segmentation. We discuss the challenges of the dataset and train the state-of-the-art methods on this dataset to evaluate how well these methods can recognize the disaster situations. Finally, we discuss challenges for future research.

Xuesu Xiao, Jan Dufek, Robin Murphy

This paper proposes a formal robot motion risk reasoning framework and develops a risk-aware path planner that minimizes the proposed risk. While robots locomoting in unstructured or confined environments face a variety of risk, existing risk only focuses on collision with obstacles. Such risk is currently only addressed in ad hoc manners. Without a formal definition, ill-supported properties, e.g. additive or Markovian, are simply assumed. Relied on an incomplete and inaccurate representation of risk, risk-aware planners use ad hoc risk functions or chance constraints to minimize risk. The former inevitably has low fidelity when modeling risk, while the latter conservatively generates feasible path within a probability bound. Using propositional logic and probability theory, the proposed motion risk reasoning framework is formal. Building upon a universe of risk elements of interest, three major risk categories, i.e. locale-, action-, and traverse-dependent, are introduced. A risk-aware planner is also developed to plan minimum risk path based on the newly proposed risk framework. Results of the risk reasoning and planning are validated in physical experiments in real-world unstructured or confined environments. With the proposed fundamental risk reasoning framework, safety of robot locomotion could be explicitly reasoned, quantified, and compared. The risk-aware planner finds safe path in terms of the newly proposed risk framework and enables more risk-aware robot behavior in unstructured or confined environments.

Xuesu Xiao, Jan Dufek, Robin Murphy

This paper presents a formal definition and explicit representation of robot motion risk. Currently, robot motion risk has not been formally defined, but has already been used in motion and path planning. Risk is either implicitly represented as model uncertainty using probabilistic approaches, where the definition of risk is somewhat avoided, or explicitly modeled as a simple function of states, without a formal definition. In this work, we provide formal reasoning behind what risk is for robot motion and propose a formal definition of risk in terms of a sequence of motion, namely path. Mathematical approaches to represent motion risk are also presented, which is in accordance with our risk definition and properties. The definition and representation of risk provide a meaningful way to evaluate or construct robot motion or path plans. The understanding of risk is even of greater interest for the search and rescue community: the deconstructed environments cast extra risk onto the robot, since they are working under extreme conditions. A proper risk representation has the potential to reduce robot failure in the field.

Xuesu Xiao, Jan Dufek, Robin Murphy

This paper proposes and benchmarks two tether-based motion primitives for tethered UAVs to execute autonomous flight with proprioception only. Tethered UAVs have been studied mainly due to power and safety considerations. Tether is either not included in the UAV motion (treated same as free-flying UAV) or only in terms of station-keeping and high-speed steady flight. However, feedback from and control over the tether configuration could be utilized as a set of navigational tools for autonomous flight, especially in GPS-denied environments and without vision-based exteroception. In this work, two tether-based motion primitives are proposed, which can enable autonomous flight of a tethered UAV. The proposed motion primitives are implemented on a physical tethered UAV for autonomous path execution with motion capture ground truth. The navigational performance is quantified and compared. The proposed motion primitives make tethered UAV a mobile and safe autonomous robot platform. The benchmarking results suggest appropriate usage of the two motion primitives for tethered UAVs with different path plans.

Xuesu Xiao, Jan Dufek, Robin Murphy

This paper develops a path planner that minimizes risk (e.g. motion execution) while maximizing accumulated reward (e.g., quality of sensor viewpoint) motivated by visual assistance or tracking scenarios in unstructured or confined environments. In these scenarios, the robot should maintain the best viewpoint as it moves to the goal. However, in unstructured or confined environments, some paths may increase the risk of collision; therefore there is a tradeoff between risk and reward. Conventional state-dependent risk or probabilistic uncertainty modeling do not consider path-level risk or is difficult to acquire. This risk-reward planner explicitly represents risk as a function of motion plans, i.e., paths. Without manual assignment of the negative impact to the planner caused by risk, this planner takes in a pre-established viewpoint quality map and plans target location and path leading to it simultaneously, in order to maximize overall reward along the entire path while minimizing risk. Exact and approximate algorithms are presented, whose solution is further demonstrated on a physical tethered aerial vehicle. Other than the visual assistance problem, the proposed framework also provides a new planning paradigm to address minimum-risk planning under dynamical risk and absence of substructure optimality and to balance the trade-off between reward and risk.

Xuesu Xiao, Jan Dufek, Mohamed Suhail et al.

This paper develops and compares two motion planning algorithms for a tethered UAV with and without the possibility of the tether contacting the confined and cluttered environment. Tethered aerial vehicles have been studied due to their advantages such as power duration, stability, and safety. However, the disadvantages brought in by the extra tether have not been well investigated by the robotic locomotion community, especially when the tethered agent is locomoting in a non-free space occupied with obstacles. In this work, we propose two motion planning frameworks that (1) reduce the reachable configuration space by taking into account the tether and (2) deliberately plan (and relax) the contact point(s) of the tether with the environment and enable an equivalent reachable configuration space as the non-tethered counterpart would have. Both methods are tested on a physical robot, Fotokite Pro. With our approaches, tethered aerial vehicles could find their applications in confined and cluttered environments with obstacles as opposed to ideal free space, while still maintaining the advantages from the usage of a tether. The motion planning strategies are particularly suitable for marsupial heterogeneous robotic teams, such as visual servoing/assisting for another mobile, tele-operated primary robot.

Haresh Karnan, Aritra Biswas, Pranav Vaidik Dhulipala et al.

This paper presents an algorithm and the implementation of a motor schema to aid the visual localization subsystem of the ongoing EMILY project at Texas A and M University. The EMILY project aims to team an Unmanned Surface Vehicle (USV) with an Unmanned Aerial Vehicle (UAV) to augment the search and rescue of marine casualties during an emergency response phase. The USV is designed to serve as a flotation device once it reaches the victims. A live video feed from the UAV is provided to the casuality responders giving them a visual estimate of the USVs orientation and position to help with its navigation. One of the challenges involved with casualty response using a USV UAV team is to simultaneously control the USV and track it. In this paper, we present an implemented solution to automate the UAV camera movements to keep the USV in view at all times. The motor schema proposed, uses the USVs coordinates from the visual localization subsystem to control the UAVs camera movements and track the USV with minimal camera movements such that the USV is always in the cameras field of view.

Nicolas Kourtellis, Adriana Iamnitchi, Cristian Borcea et al.

Mobile multi-robot teams deployed for monitoring or search-and-rescue missions in urban disaster areas can greatly improve the quality of vital data collected on-site. Analysis of such data can identify hazards and save lives. Unfortunately, such real deployments at scale are cost prohibitive and robot failures lead to data loss. Moreover, scaled-down deployments do not capture significant levels of interaction and communication complexity. To tackle this problem, we propose novel mobility and failure generation frameworks that allow realistic simulations of mobile robot networks for large scale disaster scenarios. Furthermore, since data replication techniques can improve the survivability of data collected during the operation, we propose an adaptive, scalable data replication technique that achieves high data survivability with low overhead. Our technique considers the anticipated robot failures and robot heterogeneity to decide how aggressively to replicate data. In addition, it considers survivability priorities, with some data requiring more effort to be saved than others. Using our novel simulation generation frameworks, we compare our adaptive technique with flooding and broadcast-based replication techniques and show that for failure rates of up to 60% it ensures better data survivability with lower communication costs.