Time-Optimal Ergodic Retrieval
In this work, we propose a new formulation of the time-optimal ergodic search problem that models detection as a Poisson process and minimizes total mission time by including the return phase in the objective.
ProofPaper ↗
Key points
- Retrieval tasks are a form of search task where a robot must explore, find an object, and return with it as quickly as possible.
- Recent work in ergodic control has demonstrated time-optimal path planning for search tasks where visitation is proportionally matched, within some tolerance, to a given target distribution.
- However, these planners minimize the duration of a full deterministic search, while retrieval missions end their search at an unknown point in the trajectory and must return to a given base.
- Experimental results show that time-optimal ergodic retrieval (TOER) achieves up to $19%$ shorter mission durations relative to ergodic baselines and that this advantage grows as the return phase becomes more costly.
Sources (1)
- [1]Time-Optimal Ergodic RetrievalarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 01:08 PM
In this work, we propose a new formulation of the time-optimal ergodic search problem that models detection as a Poisson process and minimizes total mission time by including the return phase in the objective.
Retrieval tasks are a form of search task where a robot must explore, find an object, and return with it as quickly as possible.
Extractive summary: sentences quoted from the sources.
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