Finding Near-optimal Solutions in Multi-robot Trajectory Planning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00236625" target="_blank" >RIV/68407700:21230/15:00236625 - isvavai.cz</a>

Výsledek na webu

<a href="http://agents.fel.cvut.cz/~cap/wp-content/papercite-data/pdf/cap_2015_c.pdf" target="_blank" >http://agents.fel.cvut.cz/~cap/wp-content/papercite-data/pdf/cap_2015_c.pdf</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Finding Near-optimal Solutions in Multi-robot Trajectory Planning

Popis výsledku v původním jazyce

We deal with the problem of planning collision-free trajectories for robots operating in a shared space. Given the start and destination position for each of the robots, the task is to find trajectories for all robots that reach their destinations with minimum total cost such that the robots will not collide when following the found trajectories. Our approach starts from individually optimal trajectory for each robot, which are then penalized for being in collision with other robots. The penalty is gradually increased and the individual trajectories are iteratively replanned to account for the increased penalty until a collision-free solution is found. Using extensive experimental evaluation, we find that such a penalty method constructs trajectories with near-optimal cost on the instances where the optimum is known and otherwise with 4-10 % lower cost than the trajectories generated by prioritized planning and up to 40 % cheaper than trajectories generated by local collision avoidance

Název v anglickém jazyce

Finding Near-optimal Solutions in Multi-robot Trajectory Planning

Popis výsledku anglicky

We deal with the problem of planning collision-free trajectories for robots operating in a shared space. Given the start and destination position for each of the robots, the task is to find trajectories for all robots that reach their destinations with minimum total cost such that the robots will not collide when following the found trajectories. Our approach starts from individually optimal trajectory for each robot, which are then penalized for being in collision with other robots. The penalty is gradually increased and the individual trajectories are iteratively replanned to account for the increased penalty until a collision-free solution is found. Using extensive experimental evaluation, we find that such a penalty method constructs trajectories with near-optimal cost on the instances where the optimum is known and otherwise with 4-10 % lower cost than the trajectories generated by prioritized planning and up to 40 % cheaper than trajectories generated by local collision avoidance

Druh

O - Ostatní výsledky

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů