Engineering social awareness in work environments

A growing interest is seen for designing intelligent environments that support personally meaningful, sociable and rich everyday experiences. In this paper we describe an intelligent, large screen display called Panorama that is aimed at supporting and enhancing social awareness within an academic work environment. Panorama is not intended to provide instrumental or other productivity related information. Rather, the goal of Panorama is to enhance social awareness by providing interpersonal and rich information related to co-workers and their everyday interactions in the department. A two-phase assessment of Panorama showed to promote curiosity and interest in exploring different activities in the environment.

Semi-supervised intelligent surveillance system for secure environments

This paper proposes a semi-supervised intelligent visual surveillance system to exploit the information from multi-camera networks for the monitoring of people and vehicles. Modules are proposed to perform critical surveillance tasks including: the management and calibration of cameras within a multi-camera network; tracking of objects across multiple views; recognition of people utilising biometrics and in particular soft-biometrics; the monitoring of crowds; and activity recognition. Recent advances in these computer vision modules and capability gaps in surveillance technology are also highlighted.

Visual localisation in outdoor industrial building environments

This paper presents a vision-based method of vehicle localisation that has been developed and tested on a large forklift type robotic vehicle which operates in a mainly outdoor industrial setting. The localiser uses a sparse 3D edgemap of the environment and a particle filter to estimate the pose of the vehicle. The vehicle operates in dynamic and non-uniform outdoor lighting conditions, an issue that is addressed by using knowledge of the scene to intelligently adjust the camera exposure and hence improve the quality of the information in the image. Results from the industrial vehicle are shown and compared to another laser-based localiser which acts as a ground truth. An improved likelihood metric, using peredge calculation, is presented and has shown to be 40% more accurate in estimating rotation. Visual localization results from the vehicle driving an arbitrary 1.5km path during a bright sunny period show an average position error of 0.44m and rotation error of 0.62deg.

Modeling and exploiting behavior patterns in dynamic environments

This paper presents a new approach to improving the effectiveness of autonomous systems that deal with dynamic environments. The basis of the approach is to find repeating patterns of behavior in the dynamic elements of the system, and then to use predictions of the repeating elements to better plan goal directed behavior. It is a layered approach involving classifying, modeling, predicting and exploiting. Classifying involves using observations to place the moving elements into previously defined classes. Modeling involves recording features of the behavior on a coarse grained grid. Exploitation is achieved by integrating predictions from the model into the behavior selection module to improve the utility of the robot's actions. This is in contrast to typical approaches that use the model to select between different strategies or plays. Three methods of adaptation to the dynamic features of the environment are explored. The effectiveness of each method is determined using statistical tests over a number of repeated experiments. The work is presented in the context of predicting opponent behavior in the highly dynamic and multi-agent robot soccer domain (RoboCup).

Multi-robot control in highly dynamic, competitive environments

The control and coordination of multiple mobile robots is a challenging task; particularly in environments with multiple, rapidly moving obstacles and agents. This paper describes a robust approach to multi-robot control, where robustness is gained from competency at every layer of robot control. The layers are: (i) a central coordination system (MAPS), (ii) an action system (AES), (iii) a navigation module, and (iv) a low level dynamic motion control system. The multi-robot coordination system assigns each robot a role and a sub-goal. Each robots action execution system then assumes the assigned role and attempts to achieve the specified sub-goal. The robots navigation system directs the robot to specific goal locations while ensuring that the robot avoids any obstacles. The motion system maps the heading and speed information from the navigation system to force-constrained motion. This multi-robot system has been extensively tested and applied in the robot soccer domain using both centralized and distributed coordination.

Multi-objective four-dimensional vehicle motion planning in large dynamic environments

This paper presents Multi-Step A* (MSA*), a search algorithm based on A* for multi-objective 4D vehicle motion planning (three spatial and one time dimension). The research is principally motivated by the need for offline and online motion planning for autonomous Unmanned Aerial Vehicles (UAVs). For UAVs operating in large, dynamic and uncertain 4D environments, the motion plan consists of a sequence of connected linear tracks (or trajectory segments). The track angle and velocity are important parameters that are often restricted by assumptions and grid geometry in conventional motion planners. Many existing planners also fail to incorporate multiple decision criteria and constraints such as wind, fuel, dynamic obstacles and the rules of the air. It is shown that MSA* finds a cost optimal solution using variable length, angle and velocity trajectory segments. These segments are approximated with a grid based cell sequence that provides an inherent tolerance to uncertainty. Computational efficiency is achieved by using variable successor operators to create a multi-resolution, memory efficient lattice sampling structure. Simulation studies on the UAV flight planning problem show that MSA* meets the time constraints of online replanning and finds paths of equivalent cost but in a quarter of the time (on average) of vector neighbourhood based A*.

Experimental studies of wireless communication and GNSS kinematic positioning performance in high-mobility vehicle environments

In order to support intelligent transportation system (ITS) road safety applications such as collision avoidance, lane departure warnings and lane keeping, Global Navigation Satellite Systems (GNSS) based vehicle positioning system has to provide lane-level (0.5 to 1 m) or even in-lane-level (0.1 to 0.3 m) accurate and reliable positioning information to vehicle users. However, current vehicle navigation systems equipped with a single frequency GPS receiver can only provide road-level accuracy at 5-10 meters. The positioning accuracy can be improved to sub-meter or higher with the augmented GNSS techniques such as Real Time Kinematic (RTK) and Precise Point Positioning (PPP) which have been traditionally used in land surveying and or in slowly moving environment. In these techniques, GNSS corrections data generated from a local or regional or global network of GNSS ground stations are broadcast to the users via various communication data links, mostly 3G cellular networks and communication satellites. This research aimed to investigate the precise positioning system performances when operating in the high mobility environments. This involves evaluation of the performances of both RTK and PPP techniques using: i) the state-of-art dual frequency GPS receiver; and ii) low-cost single frequency GNSS receiver. Additionally, this research evaluates the effectiveness of several operational strategies in reducing the load on data communication networks due to correction data transmission, which may be problematic for the future wide-area ITS services deployment. These strategies include the use of different data transmission protocols, different correction data format standards, and correction data transmission at the less-frequent interval. A series of field experiments were designed and conducted for each research task. Firstly, the performances of RTK and PPP techniques were evaluated in both static and kinematic (highway with speed exceed 80km) experiments. RTK solutions achieved the RMS precision of 0.09 to 0.2 meter accuracy in static and 0.2 to 0.3 meter in kinematic tests, while PPP reported 0.5 to 1.5 meters in static and 1 to 1.8 meter in kinematic tests by using the RTKlib software. These RMS precision values could be further improved if the better RTK and PPP algorithms are adopted. The tests results also showed that RTK may be more suitable in the lane-level accuracy vehicle positioning. The professional grade (dual frequency) and mass-market grade (single frequency) GNSS receivers were tested for their performance using RTK in static and kinematic modes. The analysis has shown that mass-market grade receivers provide the good solution continuity, although the overall positioning accuracy is worse than the professional grade receivers. In an attempt to reduce the load on data communication network, we firstly evaluate the use of different correction data format standards, namely RTCM version 2.x and RTCM version 3.0 format. A 24 hours transmission test was conducted to compare the network throughput. The results have shown that 66% of network throughput reduction can be achieved by using the newer RTCM version 3.0, comparing to the older RTCM version 2.x format. Secondly, experiments were conducted to examine the use of two data transmission protocols, TCP and UDP, for correction data transmission through the Telstra 3G cellular network. The performance of each transmission method was analysed in terms of packet transmission latency, packet dropout, packet throughput, packet retransmission rate etc. The overall network throughput and latency of UDP data transmission are 76.5% and 83.6% of TCP data transmission, while the overall accuracy of positioning solutions remains in the same level. Additionally, due to the nature of UDP transmission, it is also found that 0.17% of UDP packets were lost during the kinematic tests, but this loss doesn't lead to significant reduction of the quality of positioning results. The experimental results from the static and the kinematic field tests have also shown that the mobile network communication may be blocked for a couple of seconds, but the positioning solutions can be kept at the required accuracy level by setting of the Age of Differential. Finally, we investigate the effects of using less-frequent correction data (transmitted at 1, 5, 10, 15, 20, 30 and 60 seconds interval) on the precise positioning system. As the time interval increasing, the percentage of ambiguity fixed solutions gradually decreases, while the positioning error increases from 0.1 to 0.5 meter. The results showed the position accuracy could still be kept at the in-lane-level (0.1 to 0.3 m) when using up to 20 seconds interval correction data transmission.

Nonlinear H_infinity control of UAVs for collision avoidance in gusty environments

This paper proposes a nonlinear H_infinity controller for stabilization of velocities, attitudes and angular rates of a fixed-wing unmanned aerial vehicle (UAV) in a windy environment. The suggested controller aims to achieve a steady-state flight condition in the presence of wind gusts such that the host UAV can be maneuvered to avoid collision with other UAVs during cruise flight with safety guarantees. This paper begins with building a proper model capturing flight aerodynamics of UAVs. Then a nonlinear controller is developed with gust attenuation and rapid response properties. Simulations are conducted for the Shadow UAV to verify performance of the proposed con- troller. Comparative studies with the proportional-integral-derivative (PID) controllers demonstrate that the proposed controller exhibits great performance improvement in a gusty environment, making it suitable for integration into the design of flight control systems for cruise flight of UAVs.

Design approaches for promoting beneficial indoor environments in healthcare facilities : a review

As a result of growing evidence regarding the effects of environmental characteristics on the health and wellbeing of people in healthcare facilities (HCFs), more emphasis is being placed on, and more attention being paid to, the consequences of design choices in HCFs. Therefore, we have critically reviewed the implications of key indoor physical design parameters, in relation to their potential impact on human health and wellbeing. In addition, we discussed these findings within the context of the relevant guidelines and standards for the design of HCFs. A total of 810 abstracts, which met the inclusion criteria, were identified through a Pubmed search, and these covered journal articles, guidelines, books, reports and monographs in the studied area. Of these, 231 full publications were selected for this review. According to the literature, the most beneficial design elements were: single-bed patient rooms, safe and easily cleaned surface materials, sound-absorbing ceiling tiles, adequate and sufficient ventilation, thermal comfort, natural daylight, control over temperature and lighting, views, exposure and access to nature, and appropriate equipment, tools and furniture. The effects of some design elements, such as lighting (e.g. artificial lighting levels) and layout (e.g. decentralized versus centralized nurses’ stations), on staff and patients vary, and “the best design practice” for each HCF should always be formulated in co-operation with different user groups and a multi-professional design team. The relevant guidelines and standards should also be considered in future design, construction and renovations, in order to produce more favourable physical indoor environments in HCFs.

Monocular vision based autonomous navigation for a cost-effective MAV in GPS-denied environments

In this paper, we present a monocular vision based autonomous navigation system for Micro Aerial Vehicles (MAVs) in GPS-denied environments. The major drawback of monocular systems is that the depth scale of the scene can not be determined without prior knowledge or other sensors. To address this problem, we minimize a cost function consisting of a drift-free altitude measurement and up-to-scale position estimate obtained using the visual sensor. We evaluate the scale estimator, state estimator and controller performance by comparing with ground truth data acquired using a motion capture system. All resources including source code, tutorial documentation and system models are available online.

Augmenting traversability maps with ultra-wideband radar to enhance obstacle detection in vegetated environments

Operating in vegetated environments is a major challenge for autonomous robots. Obstacle detection based only on geometric features causes the robot to consider foliage, for example, small grass tussocks that could be easily driven through, as obstacles. Classifying vegetation does not solve this problem since there might be an obstacle hidden behind the vegetation. In addition, dense vegetation typically needs to be considered as an obstacle. This paper addresses this problem by augmenting probabilistic traversability map constructed from laser data with ultra-wideband radar measurements. An adaptive detection threshold and a probabilistic sensor model are developed to convert the radar data to occupancy probabilities. The resulting map captures the fine resolution of the laser map but clears areas from the traversability map that are induced by obstacle-free foliage. Experimental results validate that this method is able to improve the accuracy of traversability maps in vegetated environments.

Being social @ work: designing for playfully mediated social awareness in work environments

Awareness within work environments should not be seen limited to important work-related information, activities and relationships. Mediating somewhat casual and engaging encounters related to non-work issues could also lead to meaningful and pleasurable experiences. This paper explores a design approach to support playfully mediated social awareness within an academic environment. Using ethnographic exploration and understanding the current and aspired practices, we provide details of two broad (and some times overlapping) categories of interaction for supporting and enhancing playfully mediated social awareness amongst staff members: 1) Self-Reflections and 2) Casual Encounters. We implement these two categories of interaction in an intelligent, asynchronous, large screen display called Panorama, for the staff room of our computer science department. Panorama attempts to mediate non-critical, non-work related information about the staff-members in an engaging manner to enhance social awareness within the department. We particularly emphasize on the soft design issues like reflections, belonging, care, pleasure and playfulness utilized in our design approach. The result of a two-phase assessment study suggests that our conceptualization of social awareness and the Panorama application has the potential to be easily incorporated into our academic environment.

Multiple map hypotheses for planning and navigating in non-stationary environments

This paper presents a method to enable a mobile robot working in non-stationary environments to plan its path and localize within multiple map hypotheses simultaneously. The maps are generated using a long-term and short-term memory mechanism that ensures only persistent configurations in the environment are selected to create the maps. In order to evaluate the proposed method, experimentation is conducted in an office environment. Compared to navigation systems that use only one map, our system produces superior path planning and navigation in a non-stationary environment where paths can be blocked periodically, a common scenario which poses significant challenges for typical planners.

Security issues for future intelligent transport systems

Cooperative Intelligent Transportation Systems (C-ITS) allow in-vehicle systems, and ultimately the driver, to enhance their awareness of their surroundings by enabling communication between vehicles and road infrastructure. C-ITS are widely considered as the next major step in driving assistance systems, aiming at increasing safety, comfort and mobility for drivers. However, any communicating systems are subjected to security threats. A key component for providing secure communications at a large scale is a Public Key Infrastructure (PKI). Due to the safety-critical nature of Vehicle-to-Vehicle (V2V) communications, a C-ITS PKI has functional, performance and scalability requirements that differ from traditional non-automotive environments. This paper identifies and defines the key functional and security requirements for C-ITS PKI systems and analyses proposed C-ITS PKI standards against these requirements. In particular, the proposed US and European C-ITS PKI systems are identified as being too complex and not scalable. The paper also highlights various privacy, security and scalability concerns that should be considered for a secure C-ITS PKI solution in the Australian transport landscape.