RRT-CoLearn: towards kinodynamic planning without numerical trajectory optimization

10/27/2017
by   Wouter Wolfslag, et al.
0

Sampling-based kinodynamic planners, such as Rapidly-exploring Random Trees (RRTs), pose two fundamental challenges: computing a reliable (pseudo-)metric for the distance between two randomly sampled nodes, and computing a steering input to connect the nodes. The core of these challenges is a Two Point Boundary Value Problem, which is known to be NP-hard. Recently, the distance metric has been approximated using supervised learning, reducing computation time drastically. The previous work on such learning RRTs use direct optimal control to generate the data for supervised learning. This paper proposes to use indirect optimal control instead, because it provides two benefits: it reduces the computational effort to generate the data, and it provides a low dimensional parametrization of the action space. The latter allows us to learn both the distance metric and the steering input to connect two nodes. This eliminates the need for a local planner in learning RRTs. Experimental results on a pendulum swing up show 10-fold speed-up in both the offline data generation and the online planning time, leading to at least a 10-fold speed-up in the overall planning time.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/17/2018

Optimization Strategies for Real-Time Control of an Autonomous Melting Probe

We present an optimization-based approach for trajectory planning and co...
research
04/21/2023

Online Time-Optimal Trajectory Planning on Three-Dimensional Race Tracks

We propose an online planning approach for racing that generates the tim...
research
07/05/2019

Warm-Started Optimized Trajectory Planning for ASVs

We consider warm-started optimized trajectory planning for autonomous su...
research
05/14/2023

Probabilistic RRT Connect with intermediate goal selection for online planning of autonomous vehicles

Rapidly Exploring Random Trees (RRT) is one of the most widely used algo...
research
03/02/2022

L4KDE: Learning for KinoDynamic Tree Expansion

We present the Learning for KinoDynamic Tree Expansion (L4KDE) method fo...
research
09/21/2018

STyLuS^*: A Temporal Logic Optimal Control Synthesis Algorithm for Large-Scale Multi-Robot Systems

This paper proposes proposes a new highly scalable optimal control synth...

Please sign up or login with your details

Forgot password? Click here to reset