A Comprehensive Trajectory Planner for a Person-Following ATV
Huckleberry Febbo, Jiawei Huang, David Isele · 2020
This paper presents a trajectory planning algorithm for person following that is more comprehensive than existing algorithms. This algorithm is tailored for a front-wheel-steered vehicle, is designed to follow a person while avoiding collisions with both static and moving obstacles, simultaneously optimizing speed and steering, and minimizing control effort. This algorithm uses nonlinear model predictive control, where the underling trajectory optimization problem is approximated using a simultaneous method. Results collected in an unknown environment show that the proposed planning algorithm works well with a perception algorithm to follow a person in uneven grass near obstacles and over ditches and curbs, and on asphalt over train-tracks and near buildings and cars. Overall, the results indicate that the proposed algorithm can safely follow a person in unknown, dynamic environments.