Toward a Better Understanding of Robot Energy Consumption in Agroecological Applications

Abstract

In this paper, we present a comprehensive analysis and discussion of energy consumption in agricultural robots. Robots are emerging as a promising solution to address food production and agroecological challenges, offering potential reductions in chemical use and the ability to perform strenuous tasks beyond human capabilities. The automation of agricultural tasks introduces a previously unattainable level of complexity, enabling robots to optimize trajectories, control laws, and overall task planning. Consequently, automation can lead to higher levels of energy optimization in agricultural tasks. However, the energy consumption of robotic platforms is not fully understood, and a deeper analysis of contributing factors is essential to optimize energy use. We analyze the energy data of an automated agricultural tractor performing tasks throughout the year, revealing nontrivial correlations between the robot's velocity, the type of task performed, and energy consumption. This suggests a tradeoff between task efficiency, time to completion, and energy expenditure that can be harnessed to improve the energy efficiency of robotic agricultural operations.

Figures (6)

• Figure 1: Example of an autonomous robot performing an agricultural task in a vineyard
• Figure 2: Automated tractor used in the experiment. (a) Lidar sensor; (b) GPS sensor; (c) driver location for possible supervision; (d) Attach point for agricultural tools. Note that although a person was present in the vehicle for clear safety purposes, the robot was fully autonomous during the experiments.
• Figure 3: Agricultural tools used during the experiments: (a) Crosskill Roller; (b) Vibro-cultivator; (c) Seeder; (d) Harrow
• Figure 4: Example of trajectories performed by the agricultural robot, colored by the consumed energy. Left: The robot performed a task in a field while using the vibro-cultivator; Right: The robot is going back to the farm with no tool equipped.
• Figure 5: Violin plot relation between the speed level classes (0.5 m/s step) and the energy consumption. The black boxes represent the box plot with median in white, quartiles at 25% and 75% and extremum indicated by the whiskers. The underlying curves show the data distribution. The number below each violin represent the data count.