Pawit Pharpatara

TL;DR: This brief presents a trajectory planning algorithm for aerial vehicles traveling in 3-D space while avoiding obstacles based on the optimal rapidly exploring random tree (RRT*) algorithm to accelerate the convergence speed to a suboptimal solution by biasing the random state generation.

TL;DR: Shortest paths in 3-dimensional space of a hypersonic glider in a heterogeneous environment are considered and paths found are more realistic than existing Dubins’ paths.

TL;DR: The proposed solution to this problem combines a sampling-based path planner, Dubins' curves and a locally-optimal guidance law to find feasible trajectories that anticipate future flight conditions, especially the loss of manoeuverability at high altitude.

TL;DR: In this article, a new midcourse guidance algorithm is proposed, which is a combination of sampling based path planning, Dubins' curves and classical guidance laws, and it allows to anticipate the future changes of flight conditions along the trajectory.

TL;DR: The focus of this PhD thesis is on the trajectory planning module as a part of autonomous aircraft system and the RRT algorithm, combined with a preprocessing of the exploration space, is used for a complete realistic model of the system.