优化农田机器人的重复异构任务规划，降低能耗与路径冗余。
Energy Efficient Planning for Repetitive Heterogeneous Tasks in Precision Agriculture
- 构建任务空间划分结构，预计算任务重叠概率与机械臂可达性。
- 通过混合整数非线性规划，使路径长度减少32%，能耗下降28%。
- 适合需要长时作业的农业机器人系统，尤其关注能效与规划稳定性。
精准农业中的机器人除草带来一种重复异构任务规划（RHTP）挑战，其具有两个独特特性：1）观察-执行时序约束（OFML），要求每个目标先观察后操作；2）通过高效任务共置实现节能，减少无效移动。将RHTP建模为随机更新过程，基于更新奖励定理，长期平均能耗即为每轮任务周期的期望能耗。传统任务与运动规划侧重可行性而非最优性，但农业环境中目标与障碍分布已知，可最小化期望能耗。针对该更新过程中的每个实例，我们首先计算任务空间划分，一种新型数据结构，用于计算所有任务复用可能性及其概率与机器人可达性。随后提出基于区域的集合覆盖问题，将RHTP形式化为混合整数非线性规划问题，并使用分支定界法求解。在真实田间数据驱动的仿真中，相比基线方法，路径长度减少32%，机器人停顿次数下降40%，整体能耗降低28%，重规划次数减少55%。
原文摘要 · Abstract (English)
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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