Energy-efficient precision weed elimination using a quadruped robot

In precision agriculture, an autonomous weed-removal robot (i.e., mobile manipulator) often faces a combination of challenges including large-scale navigation, repetitive coverage, and fine-grained intervention. These challenges give rise to a distinct energy-efficient long-horizon planning problem: coordinating ordered, repetitive, and heterogeneous tasks under domain-specific temporal and geometric constraints. In precision weeding, this problem is further characterized by (i) an observe-first-and-manipulate-later type temporal constraint and (ii) the potential for substantial energy savings through efficient task collocation. We formalize this challenge and propose a new problem: Repetitive and Clustered-Coupled Active Perception and Manipulation (RC-CAPM) problem for mobile manipulators. We model RC-CAPM as a stochastic renewal process and show, via the Renewal Reward Theorem, that the expected energy consumption per task cycle converges to a well-defined long-run average. We introduce a task-space partitioning method that enumerates all feasible task-multiplexing configurations and their associated probabilities under robot reachability constraints. We then derive a two-stage optimization framework and prove that its solution is optimal and equivalent to that of the original RC-CAPM problem. We evaluate the proposed approach in both simulation and on a physical mobile manipulator performing autonomous weeding. Across all evaluations, RC-CAPM consistently outperforms baseline planning strategies, yielding reductions in path length, number of stops, replanning events, and total energy consumption. In particular, the proposed method achieves an average energy reduction of 36.1% relative to the baseline.