Guiding Autonomous Exploration With Signal Temporal Logic

Algorithms for autonomous robotic exploration usually focus on optimizing time and coverage, often in a greedy fashion. However, obstacle inflation is conservative and might limit mapping capabilities and even prevent the robot from moving through narrow, important places. This letter proposes a met...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2019-10, Vol.4 (4), p.3332-3339
Main Authors: Barbosa, Fernando S., Duberg, Daniel, Jensfelt, Patric, Tumova, Jana
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Exploration
formal methods in robotics and automation
Mapping
motion and path planning
Motion planning
Planning
Robots
Robustness
Safety
search and rescue robots
Semantics
Temporal logic
Three-dimensional displays
Trajectory
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Summary:Algorithms for autonomous robotic exploration usually focus on optimizing time and coverage, often in a greedy fashion. However, obstacle inflation is conservative and might limit mapping capabilities and even prevent the robot from moving through narrow, important places. This letter proposes a method to influence the manner the robot moves in the environment by taking into consideration a user-defined spatial preference formulated in a fragment of signal temporal logic (STL). We propose to guide the motion planning toward minimizing the violation of such preference through a cost function that integrates the quantitative semantics, i.e., robustness of STL. To demonstrate the effectiveness of the proposed approach, we integrate it into the autonomous exploration planner (AEP). Results from simulations and real-world experiments are presented, highlighting the benefits of our approach.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2019.2926669