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
