IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018

Kinodynamic Comfort Trajectory Planning for Car-like Robots Kinodynamic Comfort Trajectory Planning
for Car-like Robots

by Heechan Shin , Donghyuk Kim and Sung-Eui Yoon

Korea Advanced Institute of Science and Technology (KAIST)

In this work, our goal was propose how to generate trajectory which has comfort property. By doing so, we defined new definition of 'comfort' and suggest how to generate the trajectory with it. Additionally, we propose a novel method of measuring obstacle cost, which improves optimizing the trajectory w.r.t. comfort.


Left figure shows how the kinodynamic comfort trajectory planner converges and related objective value is on the right. With our method, it is not enough to escape obstacles and make a trajectory from a start to a goal. A trajectory colored by magenta almost escape the obstacles while connecting from the start to the goal. However, since it has zaggy shape, it needs more optimizing to satisfy comfort.

A newly proposed method of measuring obstacle cost named Bidirectional Obstacle Detection (BOD) works like above. This method measures perpendicular obstacle boundaries from the trajectory. When a collocation point of trajectory is inside of the obstacles, the planner pushes this point outside of the obstacles acooring to a gradient of short distance between perpendicular distance lines. With this method, we can reduce an effect of changing dynamic properties of each state which may increase discomfort.


Abstract

As personal autonomous mobility is getting to be more widely adopted, it is more important to consider comfortability of stuffs and persons carried by such mobility. In this work, we define the comfort of a trajectory as forces, specifically, translational force, received to objects carried by a robot while following the trajectory by measuring impulse. To maximize such a comfort, we propose a novel, kinodynamic comfort path planning method based on our definition of comfort. Our work is based on direct collocation method for handling our nonconvex objective function. We also introduce Bidirectional Obstacle Detection(BOD) that identifies the distances along the perpendicular directions to the trajectory. This is mainly designed for avoiding obstacles while minimizing forces causing discomfort. Our experimental results show that our method can compute trajectories whose comfort measures can be up to 18 times higher than those computed by prior related objectives, e.g., squared velocity used for generating smooth trajectory.

VIDEO

Experimental result (1 Min) Spotlight talk (3 Min, pre-recorded)

Contents

Paper (author preprint / pdf / 1.6MiB)
Source code: ZIP file, github
Spotlight slides (pptx / 10.5MiB)