Name: RÔMULO RAMOS RADAELLI
Publication date: 26/08/2013
Advisor:
Name |
Role |
|---|---|
| CLAUDINE SANTOS BADUE | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| ALBERTO FERREIRA DE SOUZA | Co advisor * |
| CLAUDINE SANTOS BADUE | Advisor * |
Summary: This work presents a motion planning for car-like robots based on the Rapidly-Exploring Random Tree (RRT) algorithm. RRT was chosen because it is capable of handling with the motion model of a car-like vehicle that is subject to kinematic and dynamic constraints, which make the motion planning problem harder. Kinematic constraints of a car-like robot prevent it from turning around its own axis or moving laterally, and dynamic constraints limit its speed and acceleration. The motion planner developed in this work is able to plan on the road and in open areas. When planning on the road, the goal is to keep the car in its lane, without invading the counter hand. When planning in open areas, the goal is to generate trajectories able to park the car. The performance of the motion planner was evaluated in a simulation framework for autonomous vehicles and in a robotic platform called IARA. IARA is built upon the Ford Escape Hybrid equipped with sensors and modified with mechanisms for controlling the throttle, brake, steering wheel position, etc., through computers installed in the car. Experimental results demonstrate that the motion planner is capable of planning collision-free trajectories in real time. Moreover, the trajectories generated by the motion planner can spend an amount of energy equivalent to those generated by a driver
