The focus of this dissertation is to improve autonomous navigation in unstructured terrain conditions, with specific application to unmanned casualty extraction in disaster scenarios. Robotic systems are being widely employed for search and rescue applications, especially in disaster scenarios. But a majority of these are focused solely on the search aspect of the problem. This dissertation proposes a conceptual design of a Semi-Autonomous Victim Extraction Robot (SAVER) capable of safe and effective unmanned casualty extraction, thereby reducing the risk to the lives of first responders. In addition, the proposed design addresses the limitations of existing state-of-the-art rescue robots specifically in the aspect of head and neck stabilization as well as fast and safe evacuation. One of the primary capabilities needed for effective casualty extraction is reliable navigation in unstructured terrain conditions. Autonomous navigation in unstructured terrain, particularly for systems with tracked locomotion mode involves unique challenges in path planning and trajectory tracking. The dynamics of robot-terrain interaction, along with additional factors such as slip experienced by the vehicle, slope of the terrain, and actuator limitations of the robotic system, need to be taken into consideration. To realize these capabilities, this dissertation proposes a hybrid navigation architecture that employs a physics engine to perform fast and accurate state expansion inside a graph-based planner. Tracked skid-steer systems experience significant slip, especially while turning. This greatly affects the trajectory tracking accuracy of the robot. In order to enable efficient trajectory tracking in varying terrain conditions, this dissertation proposes the use of an active disturbance rejection controller. The proposed controller is capable of estimating and counter acting the effects of slip in real-time to improve trajectory tracking. As an extension of the above application, this dissertation also proposes the use of support vector machine architecture to perform terrain identification, solely based on the estimated slip parameters. Combining all of the above techniques, an overall architecture is proposed to assist and inform tele-operation of tracked robotic systems in unstructured terrain conditions. All of the above proposed techniques have been validated through simulations and experiments in indoor and simple outdoor terrain conditions. / Doctor of Philosophy / This dissertation explores ways to improve autonomous navigation in unstructured terrain conditions, with specific applications to unmanned casualty extraction in disaster scenarios. Search and rescue applications often put the lives of first responders at risk. Using robotic systems for human rescue in disaster scenarios can keep first responders out of danger. To enable safe robotic casualty extraction, this dissertation proposes a novel rescue robot design concept named SAVER. The proposed design concept consists of several subsystems including a declining stretcher bed, head and neck support system, and robotic arms that conceptually enable safe casualty manipulation and extraction based on high-level commands issued by a remote operator. In order to enable autonomous navigation of the proposed conceptual system in challenging outdoor terrain conditions, this dissertation proposes improvements in planning, trajectory tracking control and terrain estimation. The proposed techniques are able to take into account the dynamic effects of robot-terrain interaction including slip experienced by the vehicle, slope of the terrain and actuator limitations. The proposed techniques have been validated through simulations and experiments in indoor and simple outdoor terrain conditions. The applicability of the above techniques in improving tele-operation of rescue robotic systems in unstructured terrain is also discussed at the end of this dissertation.

This thesis project aims to learn and understand more about implementing a path planner to the unmanned ground vehicle (UGV), UGV Husky, specifically its traversability algorithm, and investigate how it could be further improved. A surrounding grid is generated around the UGV where each cell contains information connected to its traversability. The traversability filter is given this information to score how possible it is to traverse to the cell with regard to angular slope, terrain roughness, and step height. The three parameters have each a critical value that works as a limit where if one of the three parameters were to exceed the critical value then the cell would not be estimated to be traversable. The current problem is that their critical value for angular slope, step height, and terrain roughness are decided arbitrarily and mostly through simulation. To solve this problem, formulas are derived that focus on geometrical aspects of a UGV to define the limits in focus on slope and step. The formulas give threshold values applied to the code and are run in simulation. To validate this, hardware experiments are done to compare simulation and reality. This is to observe and learn if the simulations are good representations of reality and if the threshold values are correct. The results show that the thresholds are good estimations of the UGV Husky's limits. This is if one takes into consideration that other important factors are not included or known, such as the ground conditions in the actual experiments. The simulation studies also prove that Gazebo simulations are not good representations of reality to test terrain difficulties because of simplified physical representation, giving unreliable limits. Based on the successful implementation of the geometrically derived threshold values for slope and step, further work could include similarly derived threshold values for terrain roughness, and attempt to optimize the variables that are in the traversability formula. / Syftet med detta examensabete är attutveckla förståelsen av en ruttplaneringsalgoritm, specifikt dess framkomlighets analays, och kolla hur det går att förbättra den. Problemet som diskuteras i denna rapport är hur tröskelvärdena för vinkellutning, högsta steghöjd och terrängens grovhet är godtyckligt valda och i flesta fall från simuleringsmiljöer eller flera verklighetstester. För att lösa detta problem foukseras det på att utveckla en formel som endast kollar på de geometriska aspekterna av fordonets vinkelgräns och steghöjd. Denna formel blir testad genom att applicera de framräknade tröskelvärdena till simuleringsmiljön och observera ifall det stämmer att UGVn som användts (UGV Husky) har dessa begränsningar. För validering görs tester också i verkligheten för att se hur relationen mellan simulering och verklighet är. Resultaten visar att värdena är bra uppskattningar till vad UGV Husky klarar i verkligheten om man tar i åtanke att viktiga faktorer har inte tagits med exempelvis markförhållanden. Simulering i förhållande till verklighet är inte en bra representation på grund av dålig upplösning av modeleringen av UGVn vilket ger opålitliga gränser. Nästa steg skulle framöver vara att ta med terrängförhållanden i beräkningarna och optimera flera variabler i formeln.