Personal tools
You are here: Home CG seminar 2019 Sampling-based robot motion planning: The common bottlenecks and a novel asymptotically optimal planner
« October 2019 »
October
SuMoTuWeThFrSa
12345
6789101112
13141516171819
20212223242526
2728293031
Log in


Forgot your password?
 

Sampling-based robot motion planning: The common bottlenecks and a novel asymptotically optimal planner

Wednesday, Jun 5th, 2019, 16:10

Schreiber 309

underline

Sampling-based robot motion planning: The common bottlenecks and a novel asymptotically optimal planner

Michal Kleinbort, TAU

Abstract: 

The ability to plan collision-free motions is an important aspect of robots' autonomy: While performing tasks in cluttered environments, the robots need to avoid obstacles as well as fellow robots. The motion-planning problem has been extensively studied over the past four decades.  It was primarily investigated as a theoretical problem in computational geometry and has since been the subject of research in robotics as well as computer graphics, computational biology, architectural design, artificial intelligence, and more.
 
In this talk, I will present results developed during my PhD studies concerning the currently most common type of motion planning algorithms---sampling-based motion planners---and their main building blocks, namely collision detection and nearest-neighbor search. I will discuss the relation between these two components, theoretically and practically, and show that the distribution of work between them defies common belief.
 
Motion planning can be notoriously challenging when additional constraints are taken into account. For instance, when the robots have differential (kinodynamic) constraints on their motion, specialized planning algorithms, often different from their geometric counterparts, are applied. In my talk, I will also present our ongoing efforts to devise a simple kinodynamic planner that efficiently finds high-quality paths with high probability.
 
The talk is based on work developed with my advisor Prof. Dan Halperin, in collaboration with Kiril Solovey, Oren Salzman, Kostas Bekris, Zakary Littlefield, and Riccardo Bonalli.

  

Document Actions