In multi-robot systems, efficiently navigating in a a partially-known environment is an ubiquitous but challenging task, as each robot must account for the uncertainty introduced, for example, by other moving robots. This uncertainty makes pre-computed plans not always applicable, and often hinders the desired efficient use of the robot's resources. In this work, we present a local anytime approach for robot motion planning that accounts for the uncertainty of the environment by generating 'snapshots' of possible future scenarios. Our approach adapts the Hindsight optimization technique to allow robots to plan their immediate motion based on long-term efficiency. We validate our approach by comparing the efficiency on the paths executed against a state-of-the art navigation technique in a variety of scenarios, and show that by accounting for the uncertainty in the environment, agents can improve their time- and energy-efficient motions.
|Original language||English (US)|
|Title of host publication||IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Oct 31 2014|
|Event||2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 - Chicago, United States|
Duration: Sep 14 2014 → Sep 18 2014
|Name||IEEE International Conference on Intelligent Robots and Systems|
|Other||2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014|
|Period||9/14/14 → 9/18/14|
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© 2014 IEEE.