Abstract
The goal of this work is to explore ways of generating state trajectories for dynamical systems subject to computational constraints, obstacles and priority assignment. The algorithms are developed for a miniature unmanned aerial vehicle (UAV) in a modular fashion and include (1) a genetic algorithm (GA) for solving the travelling salesman problem (TSP) with respect to priorities and obstacle avoidance, (2) a projective algorithm (PA) for finding the shortest paths around obstacles, (3) a quadratic program (QP) for minimum-snap polynomial trajectory generation subject to equality constraints to guarantee avoidance of static obstacles. Combined, the algorithms enable simple and computationally efficient motion planning with support in both R2 and R3 exemplified in a real-time implementation.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
