Raymond Sheh

Effective user interface design for rescue robotics

Until robots are able to autonomously navigate, carry out a mission and report back to base, effective human-robot interfaces will be an integral part of any practical mobile robot system. This is especially the case for robot-assisted Urban Search and Rescue (USAR). Unfamiliar and unstructured environments, unreliable communications and many sensors combine to make the job of a human operator, and hence the interface designer challenging.This paper presents the design, implementation and deployment of a human-robot interface for the teleoperated USAR research robot, textsfCASTER. Proven HCI-based user interface design principles were adopted in order to produce an interface that was intuitive and minimised learning time while maximising effectiveness.The human-robot interface was deployed by Team CASualty in the 2005 RoboCup Rescue Robot League competition. This competition allows a wide variety of approaches to USAR research to be evaluated in a realistic environment. Despite the operator having less than one month of experience, Team CASualty came 3rd, beating teams that had far longer to train their operators. In particular, the ease with which the robot could be driven and high quality information gathered played a crucial part in Team CASualtys success. Further empirical evaluations of the system on a group of twelve users as well as members of the public further reinforce our belief that this interface is quick to learn, easy to use and effective.

Extracting Terrain Features from Range Images for Autonomous Random Stepfield Traversal

One of the challenges of rescue robotics is to create robots that can autonomously traverse rough, unstructured terrain. Although mechanical engineering can produce very capable robots, mechanical engineering alone will not drive them. In this paper, we present a terrain feature extractor that can be taught to find significant features in range images of terrain around a robot from a human expert. This novel approach has the advantage that it potentially allows the human experts knowledge to be captured rapidly. A terrain model is generated from the many points in the range sensor data. Techniques from the field of knowledge acquisition are then used to find patterns in the terrain model. A knowledge acquisition system can then be taught to drive a robot in unstructured terrain based on these features. We evaluate the performance of the initial stages of the feature extractor on a real robot, traversing NIST specification red stepfields.

Community-driven development of standard software modules for search and rescue robots

The main goal of the paper is to continuously enlarge the set of software building blocks that can be reused in the search and rescue domain.

Controlling Heterogeneous Semi-autonomous Rescue Robot Teams

Robot-assisted Urban Search and Rescue (USAR) operations benefit from having multiple robots search an area, especially if doing so does not require additional operators. However, designing a user interface that facilitates a single operator controlling many robots is challenging. In particular, the problems of situation awareness and cognitive load are amplified. This is especially the case when the robots concerned have a large number of degrees of freedom. We present a preliminary design and implementation of a user interface for a team of heterogeneous, potentially autonomous USAR robots with many degrees of freedom for both sequential and parallel operation. It extends our earlier design for a successfully deployed single-robot interface. Our design is inspired by Real-Time Strategy computer games, which must address many similar issues. The design seeks to maximise situational awareness and reduce cognitive load while allowing the operator to monitor and, if necessary, control all of the robots. Our user interface was deployed during the 2006 RoboCup Rescue Robot League where it played an important role in achieving the highest single-run scores in the preliminary rounds of the competition.

The RoboCupRescue robot league: Guiding robots towards fieldable capabilities

The RoboCupRescue Robot League is an international competition where teams from all over the world compete against an arena that allows them to demonstrate their advanced robotic capabilities for emergency response applications. The league is also a community that works together to advance the state-of-the-art towards improving performance and the standards that help quantify this performance. In this paper, we present the current state of the competition, its links to the wider standardization process and how it is guiding robots towards fieldable capabilities.

Behavioural cloning for driving robots over rough terrain

Controllers for autonomous mobile robots that operate in rough terrain must consider the shape of the surrounding terrain and its impact on the robots movements. For complex terrain, these interactions are extremely difficult to model in a way that allows traditional controllers to be built. We have used Behavioural Cloning, a type of learning by imitation that produces rules that clone the skills of an expert human operator. We have also developed an autonomous instructor in simulation and used it to generate training data from which we have cloned controllers. The resulting controllers perform at a level comparable to that of a human expert. The controllers behave similarly both in simulation, where they were developed, and on the physical robot without the need for further modification or training.

Emergency response robot evaluation exercise

More than 60 robot test methods are being developed by a team led by the National Institute of Standards and Technology (NIST) with the sponsorship of U.S. Department of Homeland Security (DHS). These test methods are being specified and standardized under the standards development organization ASTM International. These standards are developed for the purposes of identifying the capabilities of mobile robots to help emergency response organizations assess the applicability of the robots.n The test methods are developed using an iterative process during which they are prototyped and validated by the participating researchers, developers, emergency response users, and robot manufacturers. We have conducted a series of evaluation exercises based on the test method implementations. These events were participated by representatives from all the different segments of the community. As such, these events present a unique opportunity for advancing the test methods, collecting capability data, and identifying robotic technology focusing issues. This paper describes an exercise event that this effort recently conducted.

The 2012 Safety, Security, and Rescue Robotics Summer School

The 2012 IEEE Robotics and Automation Society Safety, Security, and Rescue Robotics Summer School was held from the 9th to the 15th of September in Alanya, Turkey, and brought together a select group of students, researchers, responders, manufacturers, and standards organization representatives for a week of learning, dissemination of challenges and best-in-class solutions, and networking. This event builds on a series of summer schools that started in Rome in 2004 and were significant in their combination of lectures, hands-on practicals, and networking opportunities, all carried out in one all-inclusive venue. In this paper, we report on the outcomes and novel aspects of the latest incarnation of this event.

Simulating range cameras for complex terrain robot mobility

We have developed three simple simulations for range cameras (also called 3D cameras or flash LIDARs) that may be integrated into existing 3D robot visualisation and simulation frameworks. They are able to generate data at better than video framerates with minimal additional processing by leveraging the OpenGL graphics engine. We have used these simulations in our work on applying machine learning to the task of autonomous complex terrain robot mobility and 3D SLAM. We found that with a simple noise model it is possible to obtain realistic results.