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Description:
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A widespread use of robotic technology in civilian and military applications has
generated a need for advanced motion planning algorithms that are real -time implementable .
These algorithms are required to navigate autonomous vehicles through
obstacle -rich environments . This research has led to the development of the multilayer
trajectory generation approach . It is built on the principle of separation of
concerns , which partitions a given problem into multiple independent layers , and addresses
complexity that is inherent at each level . We partition the motion planning
algorithm into a roadmap layer and an optimal control layer . At the roadmap layer ,
elements of computational geometry are used to process the obstacle rich environment
and generate feasible sets . These are used by the optimal control layer to generate
trajectories while satisfying dynamics of the vehicle . The roadmap layer ignores the
dynamics of the system , and the optimal control layer ignores the complexity of the
environment , thus achieving a separation of concern . This decomposition enables
computationally tractable methods to be developed for addressing motion planning
in complex environments . The approach is applied in known and unknown environments .
The methodology developed in this thesis has been successfully applied to a 6
DOF planar robotic testbed . Simulation results suggest that the planner can generate
trajectories that navigate through obstacles while satisfying dynamical constraints . |