Energy Efficient Planning for Repetitive Heterogeneous Tasks in Precision Agriculture

Shuangyu Xie; Ken Goldberg; Dezhen Song

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arXiv · 2504.03938

Energy Efficient Planning for Repetitive Heterogeneous Tasks in Precision Agriculture

Shuangyu Xie·Ken Goldberg·Dezhen Song

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Abstract

Robotic weed removal in precision agriculture introduces a repetitive heterogeneous task planning (RHTP) challenge for a mobile manipulator. RHTP has two unique characteristics: 1) an observe-first-and-manipulate-later (OFML) temporal constraint that forces a unique ordering of two different tasks for each target and 2) energy savings from efficient task collocation to minimize unnecessary movements. RHTP can be framed as a stochastic renewal process. According to the Renewal Reward Theorem, the expected energy usage per task cycle is the long-run average. Traditional task and motion planning focuses on feasibility rather than optimality due to the unknown object and obstacle position prior to execution. However, the known target/obstacle distribution in precision agriculture allows minimizing the expected energy usage. For each instance in this renewal process, we first compute task space partition, a novel data structure that computes all possibilities of task multiplexing and its probabilities with robot reachability. Then we propose a region-based set-coverage problem to formulate the RHTP as a mixed-integer nonlinear programming. We have implemented and solved RHTP using Branch-and-Bound solver. Compared to a baseline in simulations based on real field data, the results suggest a significant improvement in path length, number of robot stops, overall energy usage, and number of replans.

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Shuangyu Xie, Ken Goldberg, Dezhen Song. 2025-04-04. Energy Efficient Planning for Repetitive Heterogeneous Tasks in Precision Agriculture. https://arxiv.org/abs/2504.03938

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