Jimmy Tran

Initial experiments on 3D modeling of complex disaster environments using unmanned aerial vehicles

The use of unmanned aerial vehicles (UAVs) has the potential to significantly improve the situation awareness of emergency first responders working at urban disaster sites. Having the characteristics of being small, light-weight and quickly deployable, UAVs offer the ability to fly over an urban disaster and provide intelligence to Urban Search and Rescue (USAR) task force efforts before precious operational resources are committed on the ground. In this paper we discuss our experience with using a small UAV to perform the task of creating a 3D model of a rubble piles surface using commercial off the shelf (COTS) components in the form of an available UAV equipped with a modified video game sensing package.

Low-cost 3D scene reconstruction for response robots in real-time

In this paper we discuss several methods for the creation of 3D models that can provide additional information to robot operators in order to improve their situation awareness of the robot being teleoperated. We derive the 3D models from spatial data gathered from an inexpensive, readily available, video game sensor. In addition, the paper introduces a new method for feature extraction as part of image registration in feature-sparse environments that operates in real-time.

Dog and snake marsupial cooperation for urban search and rescue deployment

One of the many challenges in developing ground response robots for Urban Search and Rescue (USAR) is endowing them with mobility that allows traversal of challenging terrain. In a preliminary study we introduced a new approach to the mobility problem that utilizes USAR dogs to deliver robots close to human victims in rubble. The results indicated that some search dogs are able to carry a small robot to a victim. This paper extends the original work - this time employing a more capable snake robot. Snake robots have much better maneuverability within rubble than wheeled or tracked robots. Unfortunately they are very slow - making timely rubble traversal a moot point. The premise of this work is that our hybrid system exhibits the advantages of rapid canine mobility with the flexibility and sensing capability of a snake robot.

Continuing progress in augmenting urban search and rescue dogs

Canine Augmentation Technology (CAT) is a telepresence system worn by search canines to be used in Urban Search and Rescue (US&R) operations. The intended purpose of CAT is as a tool for search teams and emergency managers to sense the situation when the dog finds a survivor in a collapsed structure. Data about the environment is transmitted to searchers and managers from the dog who may be able to penetrate further into a rubble pile than humans. Certain critical information can help the rescue team by allowing them to understand the situation around the victim before they actually attempt the rescue. This paper describes the latest developments in the CAT prototypes as well as discusses the improvements from previous versions and makes comparisons to other telepresence systems used in US&R operations.

Toward the automatic detection of access holes in disaster rubble

The collapse of buildings and other structures in heavily populated areas often result in multiple human victims becoming trapped within the resulting rubble. This rubble is often unstable, difficult to traverse and dangerous for first responders who are tasked with finding and extricating victims through access holes in the rubble. Recent work in scene mapping and reconstruction using RGB-D data collected by unmanned aerial vehicles (UAVs) suggest the possibility of automatically identifying potential access holes into the interior of rubble. This capability would allow critical limited search capacity to be concentrated in areas where potential access holes can be verified as useful entry points. In this paper, we present a system to automatically identify access holes in rubble. Our investigation begins with defining a hole in terms of its functionality as a potential means for accessing the interior of rubble. From this definition, we propose a set of discriminative geometric and photometric features to detect “access holes”. We conducted experiments using RGB-D data collected over several disaster training facilities using a UAV. Our empirical evaluation indicates the potential of the proposed approach for successfully identifying access holes in disaster rubble scenes.

Wireless Mesh Network Performance for Urban Search and Rescue Missions

In this paper we demonstrate that the Canine Pose Estimation (CPE) system can provide a reliable estimate for some poses and when coupled with effective wireless transmission over a mesh network. Pose estimates are time sensitive, thus it is important that pose data arrives at its destination quickly. Propagation delay and packet delivery ratio measuring algorithms were developed and used to appraise Wireless Mesh Network (WMN) performance as a means of carriage for this time-critical data. The experiments were conducted in the rooms of a building where the radio characteristics closely resembled those of a partially collapsed building-a typical US&R environment. This paper presents the results of the experiments, which demonstrate that it is possible to receive the canine pose estimation data in realtime although accuracy of the results depend on the network size and the deployment environment.

A hybrid lossless and lossy compression scheme for streaming RGB-D data in real time

Mobile and aerial robots used in urban search and rescue (USAR) operations have shown the potential for allowing us to explore, survey and assess collapsed structures effectively at a safe distance. RGB-D cameras, such as the Microsoft Kinect, allow us to capture 3D depth data in addition to RGB images, providing a significantly richer user experience than flat video, which may provide improved situational awareness for first responders. However, the richer data comes at a higher cost in terms of data throughput and computing power requirements. In this paper we consider the problem of live streaming RGB-D data over wired and wireless communication channels, using low-power, embedded computing equipment. When assessing a disaster environment, a range camera is typically mounted on a ground or aerial robot along with the onboard computer system. Ground robots can use both wireless radio and tethers for communications, whereas aerial robots can only use wireless communication. We propose a hybrid lossless and lossy streaming compression format designed specifically for RGB-D data and investigate the feasibility and usefulness of live-streaming this data in disaster situations.

3D Disaster Scene Reconstruction Using a Canine-Mounted RGB-D Sensor

A 3D map of the interior of a disaster site that pinpoints the location of trapped victims would greatly aid search and rescue efforts. We propose using a canine-mounted RGB-D sensor; a trained rescue dog can carry an image sensor through the site to build a 3D model useful for rescuers. However, the registration of the data provides challenges beyond those typically faced in scene reconstruction due to the rapid motion and sudden pose changes. We provide a solution whereby a pre-processing step identifies good frames to combine from a stream of RGB-D image frames. These selected images are then combined into the larger model by calculating a relative pose using the 3D location of key points matched in the visible images. Results are presented of 3D models constructed using data collected from the canine platform.

DEX - A design for Canine-Delivered Marsupial Robot

This paper presents the work on Drop and EXplore (DEX), a small rescue robot to be used in Urban Search and Rescue (USAR) operations. Unlike other rescue robots, DEX was designed to be used in tandem with trained USAR canines. The development of DEX was part of a new concept called Canine Assisted Robot Deployment (CARD). CARD utilizes search canines to deliver robots close to the casualties trapped under rubble. A small robot is attached to a search dog. After the dog uses its agility and sense of smell to find a casualty, the robot is deployed when the dog gives its bark indication. This method circumvents the current problems of response robots, their inability to traverse rubble. As DEX was constructed in order to test the concept of CARD, its designs are described in this paper along with the experiments conducted.

Canine Assisted Robot Deployment for Urban Search and Rescue

In Urban Search and Rescue (USAR) operations the search for survivors must occur before rescue operations can proceed. Two methods that can be used to search in rubble are trained search dogs and specialized response robots (sometimes called rescue robots). Rescue robots are used to collect information about trapped people within a disaster like a collapsed building. Information from them can help first responders plan and execute a rescue effort. The main challenge for these robots is the restrictions placed on their mobility by challenging rubble surfaces. While current research in this area attacks this challenge through mechanical design, good solutions remain elusive. This paper presents a new method for dispersing response robots called Canine Assisted Robot Deployment (CARD). CARDs approach utilizes USAR dogs to deliver robots close to a trapped human detected by the dog. This method exploits the canine ability to find survivors using their olfactory sensors and agility. Once a dog carrying a small robot has found a casualty, the robot can be dropped and begin exploring. Initial experiments and results are described in this paper.