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Anytime navigation with Progressive Hindsight optimization | IEEE Conference Publication | IEEE Xplore

Anytime navigation with Progressive Hindsight optimization


Abstract:

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 in...Show More

Abstract:

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.
Date of Conference: 14-18 September 2014
Date Added to IEEE Xplore: 06 November 2014
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Conference Location: Chicago, IL, USA

References

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