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<p>While traditional rigid robots are widely used in almost all applications today, their rigidity restricts the use of these robots in environments where interaction with the surroundings or humans is inevitable. This is where soft robots come into play. Due to their compliant and adaptable nature, these robots can safely interact with humans and traverse through unpredictable, cluttered environments. This research focuses on the navigation of a special class of soft growing robots called Vine robots. Vine robots can easily maneuver through tight spaces and rough terrain and have an added advantage of speed over general soft robots. In this work, we develop a model which localizes the Vine robot in an unknown surrounding by giving us the position of the tip of the robot at every instant. The model exploits the passive steering of growing robots using obstacle aided navigation. The robot is sensorized to record the orientation of the its tip and the total length it has grown to. This data along with the force generated on collision with the environment is used to localize the robot in space. The localization model is implemented using the sensor data. The accuracy of this model is then verified by comparing the tip position of the robot we have calculated with its predicted position and the actual position as measured by an overhead camera. It is concluded that the robot can be localized in an environment with a maximum error of 7.65 cm (10\%) when the total length the robot has grown to is 170 cm. </p>

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A cruise missile is a guided missile that uses a lifting wing and a jet propulsion system to allow sustained flight. Cruise missiles are, in essence, unmanned aircraft and they are generally designed to carry a large conventional or nuclear warhead many hundreds of miles with excellent accuracy. In this study, navigation and control studies on cruise missiles are performed. Due to the variety and complexity of the subsystems of the cruise missiles, the main concern is limited with the navigation system. Navigation system determines the position, velocity, attitude and time solutions of the missile. Therefore, it can be concluded that an accurate self-contained navigation system directly influences the success of the missile. In the study, modern radar data association algorithms are implemented as new Terrain Aided Navigation (TAN) algorithms which can be used with low-cost Inertial Measurement Units (IMU&rsquo / s). In order to perform the study, first a thorough survey of the literature on mid-course navigation of cruise missiles is performed. Then, study on modern radar data association algorithms and their implementations to TAN are done with simple simulations. At the case study part, a six degree of freedom (6 DOF) flight simulation tool is developed which includes the aerodynamic and dynamic model of the cruise missile model including error model of the navigation system. Finally, the performances of the designed navigation systems with the implemented TAN algorithms are examined in detail with the help of the simulations performed.

Autonomous land vehicles (ALVs) have received considerable attention after their introduction into military and commercial applications. ALVs still stand as a challenging research topic. One of the main problems arising in ALV operations is the navigation accuracy while the other is the dynamic effects of road irregularities which may prevent the vehicle and its cargo to function properly. In this thesis, we propose intelligent solutions to these two basic problems of ALV. First, an intelligent method is proposed to enhance the performance of a coupled global positioning/inertial navigation system (GPS/INS) for land navigation applications during the GPS signal loss. Our method is based on using an artificial neural network (ANN) to intelligently aid the GPS/INS coupled navigation system in the absence of GPS signals. The proposed enhanced GPS/INS is used in the dynamic environment of a tour of an autonomous van and we provide the results here. GPS/INS+ANN system performance is thus demonstrated with the land trials. Secondly, our work focuses on the identification and enlargement of the stability region of the ALV. In this thesis, the domain of attraction of the ALV is found to be patched by chaotic and regular regions with chaotic boundaries which are extracted using novel technique of cell mapping equipped with measures of fractal dimension and rough sets. All image cells in the cellular state space, with their individual fractal dimension are classified as being members of lower approximation (surely stable), upper approximation (possibly stable) or boundary region using rough set theory. The obtained rough set with fractal dimension as its attribute is used to model the uncertainty of the regular regions. This uncertainty is then smoothed by a reinforcement learning algorithm in order to enlarge regular regions that are used for chassis control, critical in ALV in preventing vibration damages that can harm the payload. Hence, we will make ALV work in the largest safe area in dynamical sense and prevent the vehicle and its cargo.