Path Tracking for a Skid-steer Vehicle using Learning-based Model Predictive Control

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2017. 2. 김현진.Skid-steer vehicle can generate a large traction force, which is especially good for navigation on a rough terrain. However, the turning motion is so sensitive to slippage effect that designing a controller is still a challenging problem. Also, the motion of the vehicle is affected not only by wheel motion, but also by the road properties and the characteristics of wheel control. With this in mind, we employ a model predictive control (MPC) with an on-line model learning. The velocity model, which represents the relationship between true vehicle velocity and input command, is learned with an on-line sparse Gaussian process (GP). The on-line sparse GP can reduce the computational complexity of GP and also consistently update the model from the driving data. Finally, combining with MPC makes it possible to generate an optimal policy based on the learned model. Experiments are conducted to test the tracking performance of a skid-steer robot at the indoor and the outdoor environment. The results show the more reliable performance than the method based on a conventional model with parameter adaptation.1 Introduction 1 1.1 Literature review 1 1.2 Thesis contribution 3 1.3 Thesis outline 4 2 On-line sparse Gaussian process for velocity model 5 2.1 Kinematic model 5 2.2 Sparse Gaussian process 7 2.3 On-line updating 10 3 Model predictive control 11 3.1 Iterative linear quadratic regulator 11 3.2 Cost formulation 15 3.3 Summary of the algorithm 16 4 Experiments 18 4.1 Experimental setup 18 4.2 Indoor experimental results 22 4.3 Outdoor experimental results 29 5 Conclusion 32 5.1 Challenges and future works 32 References 34 국문초록 37Maste

Control of Outdoor Robots at Higher Speeds on Challenging Terrain

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A graph-theory-based C-space path planner for mobile robotic manipulators in close-proximity environments

In this thesis a novel guidance method for a 3-degree-of-freedom robotic manipulator arm in 3 dimensions for Improvised Explosive Device (IED) disposal has been developed. The work carried out in this thesis combines existing methods to develop a technique that delivers advantages taken from several other guidance techniques. These features are necessary for the IED disposal application. The work carried out in this thesis includes kinematic and dynamic modelling of robotic manipulators, T-space to C-space conversion, and path generation using Graph Theory to produce a guidance technique which can plan a safe path through a complex unknown environment. The method improves upon advantages given by other techniques in that it produces a suitable path in 3-dimensions in close-proximity environments in real time with no a priori knowledge of the environment, a necessary precursor to the application of this technique to IED disposal missions. To solve the problem of path planning, the thesis derives the kinematics and dynamics of a robotic arm in order to convert the Euclidean coordinates of measured environment data into C-space. Each dimension in C-space is one control input of the arm. The Euclidean start and end locations of the manipulator end effector are translated into C-space. A three-dimensional path is generated between them using Dijkstra’s Algorithm. The technique allows for a single path to be generated to guide the entire arm through the environment, rather than multiple paths to guide each component through the environment. The robotic arm parameters are modelled as a quasi-linear parameter varying system. As such it requires gain scheduling control, thus allowing compensation of the non-linearities in the system. A Genetic Algorithm is applied to tune a set of PID controllers for the dynamic model of the manipulator arm so that the generated path can then be followed using a conventional path-following algorithm. The technique proposed in this thesis is validated using numerical simulations in order to determine its advantages and limitations