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The intensity and frequency of hurricanes and storms increase due to climate change, leaving destruction in their trail. After a hurricane happens, it is fundamental to respond as quickly as possible, and Search and Rescue operations occur to limit human damage further. The operations take place in hostile environments and extreme situations, where speed and efficiency are essential. Indeed, rescuers must be able to have a shared mental model of the situation and act immediately. This study focuses on visualizing situational awareness in such environments to optimize decision-making.  This study has been conducted in collaboration with Frog design and Sony and is part of the broader project FindMe Tag, a system composed of a wearable device that tracks civilians’ location and biometric data. The tag is connected to an app for civilians to handle which data to share. The data is shared to the rescuers’ dashboard for optimizing Search and Rescue operations, which is the focus of this thesis.  Following a Research through Design approach, this thesis project aims to contribute to the interaction design field by proposing a way to visualize situational awareness during extreme and dynamic situations. The process resulted in a dashboard prototype to support Search and Rescue operations by providing a way to visualize data concerning civilians’ status and rescuers, communicate among teams, and encourage connectedness among civilians.

Unfavorable environmental and (or) human displacement may engender the need for Search and Rescue (SAR). Challenges such as inaccessibility, large search areas, and heavy reliance on available responder count, limited equipment and training makes SAR a challenging problem. Additionally, SAR operations also pose significant risk to involved responders. This opens a remarkable opportunity for robotic systems to assist and augment human understanding of the harsh environments. A large body of work exists on the introduction of ground and aerial robots in visual and temporal inspection of search areas with varying levels of autonomy. Unfortunately, limited autonomy is the norm in such systems, due to the limitations presented by on-board UAV resources and networking capabilities. In this work we propose a new multi-agent approach to SAR and introduce a wearable compute cluster in the form factor of a backpack. The backpack allows offloading compute intensive tasks such as Lost Person Behavior Modelling, Path Planning and Deep Neural Network based computer vision applications away from the UAVs and offers significantly high performance computers to execute them. The backpack also provides for a strong networking backbone and task orchestrators which allow for enhanced coordination and resource sharing among all the agents in the system. On the basis of our benchmarking experiments, we observe that the backpack can significantly boost capabilities and success in modern SAR responses. / Master of Science / Unfavorable environmental and (or) human displacement may engender the need for Search and Rescue (SAR). Challenges such as inaccessibility, large search areas, and heavy reliance on available responder count, limited equipment and training makes SAR a challenging problem. Additionally, SAR operations also pose significant risk to involved responders. This opens a remarkable opportunity for robotic systems to assist and augment human understanding of the harsh environments. A large body of work exists on the introduction of ground and aerial robots in visual and temporal inspection of search areas with varying levels of autonomy. Unfortunately, limited autonomy is the norm in such systems, due to the limitations presented by on-board UAV resources and networking capabilities. In this work we propose a new multi-agent approach to SAR and introduce a wearable compute cluster in the form factor of a backpack. The backpack allows offloading compute intensive tasks such as Lost Person Behavior Modelling, Path Planning and Deep Neural Network based computer vision applications away from the UAVs and offers significantly high performance computers to execute them. The backpack also provides for a strong networking backbone and task orchestrators which allow for enhanced coordination and resource sharing among all the agents in the system. On the basis of our benchmarking experiments, we observe that the backpack can significantly boost capabilities and success in modern SAR responses.

We introduce a framework that would enable using autonomous aerial vehicles in search and rescue scenarios associated with missing person incidents to assist human searchers. We formulate a lost person behavior model and a human searcher model informed by data collected from past search missions. These models are used to generate a probabilistic heatmap of the lost person's position and anticipated searcher trajectories. We use Gaussian processes with a Gibbs' kernel for data fusion to accurately model a limited field-of-view sensor. Our algorithm thereby computes a set of trajectories for a team of aerial vehicles to autonomously navigate, so as to assist and complement human searchers' efforts. / Master of Science / Our goal is to assist human searchers using autonomous aerial vehicles in search and rescue scenarios associated with missing person incidents. We formulate a lost person behavior model and a human searcher model informed by data collected from past search missions. These models are used to generate a probabilistic heatmap of the lost person’s position and anticipated searcher trajectories. We use Gaussian processes for data fusion with Gibbs’ kernel to accurately model a limited field-of-view sensor. Our algorithm thereby computes a set of trajectories for a team of aerial vehicles to autonomously navigate, so as to assist and complement human searchers’ efforts.

M. Tech. Computer System Engineering / Autonomous robots are extensively used equipment in industries and in our daily lives; they assist in manufacturing and production but are used for exploration in dangerous or unknown environments. However for a successful exploration, manufacturing and production, navigation plays an important role. Road detection is a vital factor that assists autonomous robots in perfect navigation. Different methods using camera-vision technique have been developed by various researchers with outstanding results, but their systems are still vulnerable to environmental risks. The frequent weather change in various countries such as South Africa, Nigeria and Zimbabwe where shadow, light intensity and other environmental noises occur on daily basis, can cause autonomous robot to encounter failure in navigation. Therefore, the main research question is: How to enhance the road region detection system to enable an effective and efficient maneuvering of the robot in any weather condition.

In urban search and rescue (USAR) operations, there is a considerable amount of danger faced by rescuers. The use of mobile robots can alleviate this issue. Coordinating the search effort is made more difficult by the communication issues typically faced in these environments, such that communication is often restricted. With small numbers of robots, it is necessary to break communication links in order to explore the entire environment. The robots can be viewed as a broken ad hoc network, relying on opportunistic contact in order to share data. In order to minimise overheads when exchanging data, a novel algorithm for data exchange has been created which maintains the propagation speed of flooding while reducing overheads. Since the rescue workers outside of the structure need to know the location of any victims, the task of finding their locations is two parted: 1) to locate the victims (Search Time), and 2) to get this data outside the structure (Delay Time). Communication with the outside is assumed to be performed by a static robot designated as the Command Station. Since it is unlikely that there will be sufficient robots to provide full communications coverage of the area, robots that discover victims are faced with the difficult decision of whether they should continue searching or return with the victim data. We investigate a variety of search techniques and see how the application of biological foraging models can help to streamline the search process, while we have also implemented an opportunistic network to ensure that data are shared whenever robots come within line of sight of each other or the Command Station. We examine this trade-off between performing a search and communicating the results.

The area of Human-Robot Interaction deals with problems not only related to robots interacting with humans, but also with problems related to humans interacting and controlling robots. This dissertation focuses on the latter and evaluates multi-sensory (vision, hearing, touch, smell) feedback interfaces as a means to improve robot-operator cognition and performance. A set of four empirical studies using both simulated and real robotic systems evaluated a set of multi-sensory feedback interfaces with various levels of complexity. The task scenario for the robot in these studies involved the search for victims in a debris-filled environment after a fictitious catastrophic event (e.g., earthquake) took place. The results show that, if well-designed, multi-sensory feedback interfaces can indeed improve the robot operator data perception and performance. Improvements in operator performance were detected for navigation and search tasks despite minor increases in workload. In fact, some of the multi-sensory interfaces evaluated even led to a reduction in workload. The results also point out that redundant feedback is not always beneficial to the operator. While introducing the concept of operator omni-directional perception, that is, the operator’s capability of perceiving data or events coming from all senses and in all directions, this work explains that feedback redundancy is only beneficial when it enhances the operator omni-directional perception of data relevant to the task at hand. Last, the comprehensive methodology employed and refined over the course of the four studies is suggested as a starting point for the design of future HRI user studies. In summary, this work sheds some light on the benefits and challenges multi-sensory feedback interfaces bring, specifically on teleoperated robotics. It adds to our current understanding of these kinds of interfaces and provides a few insights to assist the continuation of research in the area.

Semi-autonomous control schemes can address the limitations of both teleoperation and fully autonomous robotic control of rescue robots in disaster environments by allowing cooperation and task sharing between a human operator and a robot with respect to tasks such as navigation, exploration and victim identification. Herein, a unique hierarchical reinforcement learning (HRL) -based semi-autonomous control architecture is presented for rescue robots operating in unknown and cluttered urban search and rescue (USAR) environments. The aim of the controller is to allow a rescue robot to continuously learn from its own experiences in an environment in order to improve its overall performance in exploration of unknown disaster scenes. A new direction-based exploration technique and a rubble pile categorization technique are integrated into the control architecture for exploration of unknown rubble filled environments. Both simulations and physical experiments in USAR-like environments verify the robustness of the proposed control architecture.