In recent years, there have been a rise in the prevalence of Anti-satellite (ASAT) incidents. These events range from temporary disruptions via sensor dazzling, to destructive tests and demonstrations. The increased relevance of ASAT technologies is brought about by the growth of the space industry as a whole in combination with the development of ASAT related technologies. Satellite rendezvous and proximity operation technologies developed for satellite-tosatellite maintenance may readily be adapted for ASAT attacks. Similarly, LIDAR (Light Detection and Ranging) and laser scanning technologies are akin to various laser-based ASAT attacks such as dazzling and spoofing.

This paper overviews the development and operator testing of a shared autonomy system for small unmanned ground vehicles operating in indoor environments. The project focused on creating driving assistance technologies to reduce the burden of performing low-level tasks when operating in cluttered or difficult areas by sharing control between the operator and the autonomous software. The system also provides a safety layer to prevent the robot from becoming disabled due to operator error or environmental hazards. Examples of developed behaviours include obstacle proximity warning, centering the vehicle through narrow doorways, wall following during long traversals, tip-over indicator, stair climbing aid, and retreat from communications loss. The hardware and software were integrated on a QinetiQ Talon IV robot and tested by military operators in a relevant environment.

This paper presents a new obstacle avoidance method that provides unobtrusive assistance to tele-operation of unmanned ground vehicles. Different from existing obstacle avoidance methods, the present method can determine whether the driving commands from an operator are safe in the presence of obstacles and can automatically adjust unsafe commands to help the operator avoid proximate obstacles. The command adjustment is done in an unobtrusive manner and conforms to the dynamic and kinematic constraints of the vehicle in order to minimize its interference to the operator. Due to its assistive and unobtrusive nature, this method can quietly share some control authority with an operator in tele-operating a vehicle, and hence it has the potential to make tele-operation of ground vehicles in challenging environments significantly easier and safer. The effectiveness of this method is demonstrated in extensive experiments in cluttered environments using military-grade tracked robots.

Tele-operation of a Lunar rover from a control station on Earth involves a latency of several seconds due primarily to the finite speed (light-speed) of command and sensor signals, and this latency creates a difficult control task for the human operator. Two predictive displays, which seek to aid viewer perception of present events, were designed and evaluated for the specific task of driving a rover with multi-second latency. These displays provided visual information to the human operator on the rover’s real-time locomotion, as predicted from control inputs executed by the operator. A human-subject experiment with 12 participants was conducted in which the participants navigated an actual rover through obstacle courses. There were four experimental conditions repeated by each participant: (1) delayed video feed only, (2, 3) two predictive displays based on delayed video feed, and (4) a reference condition of video feed with no delay. Inferential statistics show that both predictive displays significantly improved performance in terms of time taken to complete the courses, and one of the displays facilitated performance approaching that with no delay. No trends were observed in terms of collisions with or encroachments near obstacles.

A key component in the emerging localization and mapping paradigm is an appearance-based place recognition algorithm that detects when a place has been revisited. This algorithm can run in the background at a low frame rate and be used to signal a global geometric mapping algorithm when a loop is detected. An optimization technique can then be used to correct the map by 'closing the loop'. This allows an autonomous unmanned ground vehicle to improve localization and map accuracy and successfully navigate large environments. Image-based place recognition techniques lack robustness to sensor orientation and varying lighting conditions. Additionally, the quality of range estimates from monocular or stereo imagery can decrease the loop closure accuracy. Here, we present a lidar-based place recognition system that is robust to these challenges. This probabilistic framework learns a generative model of place appearance and determines whether a new observation comes from a new or previously seen place. Highly descriptive features called the Variable Dimensional Local Shape Descriptors are extracted from lidar range data to encode environment features. The range data processing has been implemented on a graphics processing unit to optimize performance. The system runs in real-time on a military research vehicle equipped with a highly accurate, 360 degree field of view lidar and can detect loops regardless of the sensor orientation. Promising experimental results are presented for both rural and urban scenes in large outdoor environments.

Automatic detection of photosynthetic life underwater when combined with photorealistic 3D models of observed sites offers a powerful tool for scientific investigations. This paper describes underwater Scene Modeler (uSM) and presents results of experiments in mapping and modelling microbialites colonies conducted as part of the Pavilion Lake Research Project (PLRP). uSM can be also used for monitoring health of marine plants, such as coral and algae, and inspection of underwater structures.

A network of reusable paths (NRP) allows for a new approach to planetary surface exploration using a mobile robot. NRP gives the robot the ability to accurately return to any previously visited point. This allows mission-level improvements by enabling parallel exploration of scientific targets. NRP would be particularly useful for sample-return missions to the Moon or Mars. The approach was tested in a mock Lunar sample-return mission near the impact crater located in Sudbury, Ontario, Canada. There, NRP enabled nearly twice as many sites to be investigated as compared to a serial approach to exploration. In this mock mission, the robot drove more than 3.9 km, allowing for in situ analysis and sample collection and return at many sites.

A. E. Pickersgill, G. R. Osinski, M. Beauchamp, C. Marion, M. M. Mader, R. Francis, E. McCullough, B. Shankar, T. Barfoot, M. Bondy, A. Chanou, M. Daly, H. Dong, P. Furgale, J. Gammell, N. Ghafoor, M. Husse in, P. Jasiobedzki, A. Lambert, K. Leung, C. McManus, H. K. Ng, A. Pontefract, B. Stenning, L. L. Tornabene, J. Tripp, and the ILSR Team. Centre for Planetary Science and Exploration, University of Western Ontario, London, ON, Canada. Institute for Aerospace Studies, University of Toronto, Toronto, ON, Canada. MDA Space Robotics, Brampton, ON, Canada. Dept. of Earth and Space Science & Engineering, York University, Toronto, ON, Canada. Optech Inc., Vaughan, ON, Canada. See [1]. (apickers@uwo.ca; gosinski@uwo.ca).

Situational awareness of CBRN robot operators is quite limited, as they rely on images and measurements from on-board detectors. This paper describes a novel framework that enables a uniform and intuitive access to live and recent data via 2D and 3D representations of visited sites. These representations are created automatically and augmented with images, models and CBRNE measurements. This framework has been developed for CBRNE Crime Scene Modeler (C2SM), a mobile CBRNE mapping system. The system creates representations (2D floor plans and 3D photorealistic models) of the visited sites, which are then automatically augmented with CBRNE detector measurements. The data stored in a database is accessed using a variety of user interfaces providing different perspectives and increasing operators' situational awareness.

A critical problem in the deployment of commercial teleoperated robots is the development of effective training tools and methodologies. This paper describes our approach to the problem of providing such training to robot operators tasked to contaminated environments. The Vanguard MK2 and C2SMFast Sensor Simulator – a virtual reality based training system – provides an accurate simulation of the C2SM Crime Scene Modeler, an autonomous robotic system developed for the investigation of contaminated crime scenes. The training system provides a simulation of the underlying robotic platform and the C2SMFast sensor suite, and allows training on the system without physically deploying the robot or CBRNE contaminants. Here we describe the basic structure of the simulator and the software components that were used to construct it.

With the continued success of the Mars Exploration Rovers and the return of humans to the Moon within the next decade, a considerable amount of research is being done on the technologies required to provide surface mobility and the tools required to provide scientific capability. Here, we explore the utility of lidar and the mobile Scene Modeler (mSM) – which is based on a stereo camera system – as scientific tools. Both of these technologies have been, or are being considered for, technological applications such as autonomous satellite rendezvous and rover navigation. We carried out a series of field tests at the 23km diameter, 39Ma, Haughton impact structure located on Devon Island in the Canadian Arctic. Several sites of geological interest were investigated, including polygonal terrain, gullies and channels, slump/collapse features, impact melt breccia hills, and a site of impact-associated hydrothermal mineralization. These field tests show that lidar and mSM provide a superior visual record of the terrain, from the regional (km) to outcrop (m to cm) scale and in 3-D, as compared to standard digital photography. Thus, a key strength of these technologies is in situ reconnaissance and documentation. Lidar scans also provide a wealth of geometric and structural information about a site, accomplishing the equivalent of weeks to months of manual surveying and with much greater accuracy than traditional tools, making this extremely useful for planetary exploration missions. An unexpected result of these field tests is the potential for lidar and mSM to provide qualitative, and potentially quantitative, composition information about a site. Given the high probability of lidar and mSM being used on future lunar missions, we suggest that it would be beneficial to further investigate the potential for these technologies to be used as science tools.