Abstractions for computing all robotic sensors that suffice to solve a planning problem
Yulin Zhang, Dylan A. Shell

TL;DR
This paper develops advanced algorithms for jointly searching robot sensor configurations and plans, expanding the scope of sensor modeling and incorporating domain knowledge to better understand sensor requirements for task success.
Contribution
It introduces new data structures and methods to handle larger sensor search spaces and integrate additional information, improving the analysis of sensor and plan compatibility.
Findings
Algorithms can solve small-scale instances of the joint sensor-plan search problem.
New data structures enable summarization of large sensor sets.
Incorporating domain knowledge reduces search complexity.
Abstract
Whether a robot can perform some specific task depends on several aspects, including the robot's sensors and the plans it possesses. We are interested in search algorithms that treat plans and sensor designs jointly, yielding solutions---i.e., plan and sensor characterization pairs---if and only if they exist. Such algorithms can help roboticists explore the space of sensors to aid in making design trade-offs. Generalizing prior work where sensors are modeled abstractly as sensor maps on p-graphs, the present paper increases the potential sensors which can be sought significantly. But doing so enlarges a problem currently on the outer limits of being considered tractable. Toward taming this complexity, two contributions are made: (1) we show how to represent the search space for this more general problem and describe data structures that enable whole sets of sensors to be summarized via…
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Taxonomy
TopicsRobotic Path Planning Algorithms · AI-based Problem Solving and Planning · Logic, Reasoning, and Knowledge
