SmartWheels: Detecting urban features for wheelchair users’ navigation

People with mobility impairments have heterogeneous needs and abilities while moving in an urban environment and hence they require personalized navigation instructions. Providing these instructions requires the knowledge of urban features like curb ramps, steps or other obstacles along the way. Since these urban features are not available from maps and change in time, crowdsourcing this information from end-users is a scalable and promising solution. However, it is inconvenient for wheelchair users to input data while on the move. Hence, an automatic crowdsourcing mechanism is needed. In this contribution we present SmartWheels, a solution to detect urban features by analyzing inertial sensors data produced by wheelchair movements. Activity recognition techniques are used to process the sensors data stream. SmartWheels is evaluated on data collected from 17 real wheelchair users navigating in a controlled environment (10 users) and in-the-wild (7 users). Experimental results show that SmartWheels is a viable solution to detect urban features, in particular by applying specific strategies based on the confidence assigned to predictions by the classifier

Indoor And Outdoor Real Time Information Collection in Disaster Scenario

A disaster usually severely harms human health and property. After a disaster, great amount of information of a disaster area is needed urgently. The information not only indicates the severity of the disaster, but also is crucial for an efficient search and rescue process. In order to quickly and accurately collect real time information in a disaster scenario, a mobile platform is developed for an outdoor scenario and a localization and navigation system for responders is introduced for an indoor scenario. The mobile platform has been integrated to the DIORAMA system. It is built with a 6-wheel robot chassis along with an Arduino microcontroller. Controlled by a mounted Android smartphone, the mobile platform can receive commands from incident commanders and quickly respond to the commands. While patrolling in a disaster area, a constant RFID signal is collected to improve the localization accuracy of victims. Pictures and videos are also captured in order to enhance the situational awareness of rescuers. The design of the indoor information collection is focused on the responder side. During a disaster scenario, it is hard to track responders’ locations in an indoor environment. In this thesis, an indoor localization and navigation system based on Bluetooth low energy and Android is developed for helping responders report current location and quickly find the right path in the environment. Different localization algorithms are investigated and implemented. A navigation system based on A­* is also proposed

Exponentially weighted particle filter for simultaneous localization and mapping based on magnetic field measurements

This paper presents a simultaneous localization and mapping (SLAM) method that utilizes the measurement of ambient magnetic fields present in all indoor environments. In this paper, an improved exponentially weighted particle filter was proposed to estimate the pose distribution of the object and a Kriging interpolation method was introduced to update the map of the magnetic fields. The performance and effectiveness of the proposed algorithms were evaluated by simulations on MATLAB based on a map with magnetic fields measured manually in an indoor environment and also by tests on the mobile devices in the same area. From the tests, two interesting phenomena have been discovered; one is the shift of location estimation after sharp turning and the other is the accumulated errors. While the latter has been confirmed and investigated by a few researchers, the reason for the first one still remains unknown. The tests also confirm that the interpolated map by using the proposed method improves the localization accuracy

Investigation on the mobile robot navigation in an unknown environment

Mobile robots could be used to search, find, and relocate objects in many types of manufacturing operations and environments. In this scenario, the target objects might reside with equal probability at any location in the environment and, therefore, the robot must navigate and search the whole area autonomously, and be equipped with specific sensors to detect objects. Novel challenges exist in developing a control system, which helps a mobile robot achieve such tasks, including constructing enhanced systems for navigation, and vision-based object recognition. The latter is important for undertaking the exploration task that requires an optimal object recognition technique. In this thesis, these challenges, for an indoor environment, were divided into three sub-problems. In the first, the navigation task involved discovering an appropriate exploration path for the entire environment, with minimal sensing requirements. The Bug algorithm strategies were adapted for modelling the environment and implementing the exploration path. The second was a visual-search process, which consisted of employing appropriate image-processing techniques, and choosing a suitable viewpoint field for the camera. This study placed more emphasis on colour segmentation, template matching and Speeded-Up Robust Features (SURF) for object detection. The third problem was the relocating process, which involved using a robot’s gripper to grasp the detected, desired object and then move it to the assigned, final location. This also included approaching both the target and the delivery site, using a visual tracking technique. All codes were developed using C++ and C programming, and some libraries that included OpenCV and OpenSURF were utilized for image processing. Each control system function was tested both separately, and then in combination as a whole control program. The system performance was evaluated using two types of mobile robots: legged and wheeled. In this study, it was necessary to develop a wheeled search robot with a high performance processor. The experimental results demonstrated that the methodology used for the search robots was highly efficient provided the processor was adequate. It was concluded that it is possible to implement a navigation system within a minimum number of sensors if they are located and used effectively on the robot’s body. The main challenge within a visual-search process is that the environmental conditions are difficult to control, because the search robot executes its tasks in dynamic environments. The additional challenges of scaling these small robots up to useful industrial capabilities were also explored

Use of Emerging Technology as Part of the Experiential Learning Process in Ultradistance Cycling: A Phenomenological Study

Technology is well entrenched as part of our everyday lives and formal learning settings. The role technology plays as part of informal learning of sports and physical activities has not been explored as thoroughly. This study examined the use of technology by ultradistance cyclists as part of their experiential learning process. Data collection was through semi-structured interviews of 10 cyclists who routinely utilized technology in preparing for and participating in ultradistance events. Emerging themes were organized utilizing NVIVO software. While identified themes were similar to the phases of the Kolb (2014) experiential learning model, there was also a strong temporal component. Technology usage themes prior to an event included Abstract Conceptualization, Route Planning, and Training. Technology usage themes during an event included Active Experimentation, Concrete Experience, and Coping with Equipment, Mental, or Physical Challenges. A technology usage theme after an event included Reflective Observations. Participants also expressed preferences in technology characteristics; themes included Record and Display information, Easy to Use, Syncing Between Devices, and Reliability. Kolb and Kolb (2005) identified a number of features that enhanced informal experiential learning spaces in higher education. Technology could replicate these features to enhance the experiential learning process in ultradistance cycling

Towards Natural Human Control and Navigation of Autonomous Wheelchairs

Approximately 2.2 million people in the United States depend on a wheelchair to assist with their mobility. Often times, the wheelchair user can maneuver around using a conventional joystick. Visually impaired or wheelchair patients with restricted hand mobility, such as stroke, arthritis, limb injury, Parkinson’s, cerebral palsy or multiple sclerosis, prevent them from using traditional joystick controls. The resulting mobility limitations force these patients to rely on caretakers to perform everyday tasks. This minimizes the independence of the wheelchair user. Modern day speech recognition systems can be used to enhance user experiences when using electronic devices. By expanding the motorized wheelchair control interface to include the detection of user speech commands, the independence is given back to the mobility impaired. A speech recognition interface was developed for a smart wheelchair. By integrating navigation commands with a map of the wheelchair’s surroundings, the wheelchair interface is more natural and intuitive to use. Complex speech patterns are interpreted for users to command the smart wheelchair to navigate to specified locations within the map. Pocketsphinx, a speech toolkit, is used to interpret the vocal commands. A language model and dictionary were generated based on a set of possible commands and locations supplied to the speech recognition interface. The commands fall under the categories of speed, directional, or destination commands. Speed commands modify the relative speed of the wheelchair. Directional commands modify the relative direction of the wheelchair. Destination commands require a known location on a map to navigate to. The completion of the speech input processer and the connection between wheelchair components via the Robot Operating System make map navigation possible

Unmanned Ground Vehicles for Smart Farms

Forecasts of world population increases in the coming decades demand new production processes that are more efficient, safer, and less destructive to the environment. Industries are working to fulfill this mission by developing the smart factory concept. The agriculture world should follow industry leadership and develop approaches to implement the smart farm concept. One of the most vital elements that must be configured to meet the requirements of the new smart farms is the unmanned ground vehicles (UGV). Thus, this chapter focuses on the characteristics that the UGVs must have to function efficiently in this type of future farm. Two main approaches are discussed: automating conventional vehicles and developing specifically designed mobile platforms. The latter includes both wheeled and wheel-legged robots and an analysis of their adaptability to terrain and crops

Indoor Localisation of Scooters from Ubiquitous Cost-Effective Sensors: Combining Wi-Fi, Smartphone and Wheel Encoders

Indoor localisation of people and objects has been a focus of research studies for several decades because of its great advantage to several applications. Accuracy has always been a challenge because of the uncertainty of the employed sensors. Several technologies have been proposed and researched, however, accuracy still represents an issue. Today, several sensor technologies can be found in indoor environments, some of which are economical and powerful, such as Wi-Fi. Meanwhile, Smartphones are typically present indoors because of the people that carry them along, while moving about within rooms and buildings. Furthermore, vehicles such as mobility scooters can also be present indoor to support people with mobility impairments, which may be equipped with low-cost sensors, such as wheel encoders. This thesis investigates the localisation of mobility scooters operating indoor. This represents a specific topic as most of today's indoor localisation systems are for pedestrians. Furthermore, accurate indoor localisation of those scooters is challenging because of the type of motion and specific behaviour. The thesis focuses on improving localisation accuracy for mobility scooters and on the use of already available indoor sensors. It proposes a combined use of Wi-Fi, Smartphone IMU and wheel encoders, which represents a cost-effective energy-efficient solution. A method has been devised and a system has been developed, which has been experimented on different environment settings. The outcome of the experiments are presented and carefully analysed in the thesis. The outcome of several trials demonstrates the potential of the proposed solutions in reducing positional errors significantly when compared to the state-of-the-art in the same area. The proposed combination demonstrated an error range of 0.35m - 1.35m, which can be acceptable in several applications, such as some related to assisted living. 3 As the proposed system capitalizes on the use of ubiquitous technologies, it opens up to a potential quick take up from the market, therefore being of great benefit for the target audience