A Human-Centered Task Allocation and Scheduling Framework for Multi-Human-Multi-Robot Collaboration in Precision Agriculture Settings

Abstract

Human-multi-robot teaming in precision agriculture represents a promising approach to addressing labor shortages and managing the complexities of agricultural practices. An effective coordination of these teams, including task allocation and scheduling strategies while accounting for the inherent unpredictability of human behavior, is crucial for maximizing system productivity and ensuring user comfort. In this study, we introduce a Mixed-Integer Linear Programming (MILP) approach that aims to minimize workers’ waiting times, robots’ energy consumption during the different phases of the robots’ motions, and the overall makespan. To enhance the robustness of our framework and consider human preferences, a user interface is designed to capture real-time human feedback; then, an adaptive online updating strategy that dynamically adjusts plans responding to variations in human operators’ parameters is devised. To handle large-scale problems, we extend the solution approach by leveraging Constraint Programming (CP) combined with a batch decomposition strategy. The approach is validated through extensive simulations in a Unity-based realistic virtual reality environment and laboratory experiments using two TurtleBot2 robots and two human operators performing grape harvesting tasks. Note to Practitioners—This paper was inspired by the necessity to coordinate a heterogeneous team of humans and robots within an agricultural setting to optimize relevant performance indices. The proposed approach focuses on a scenario where mobile service robots perform assistance activities for human and robotic working agents. It enables the allocation and scheduling of all tasks while accounting for the following key factors: i) the different characteristics of the agents, ii) their variability due to changing environment conditions and human dynamic behavior, also captured through chance-constrained programming, and iii) human preferences and feedback provided in real-time. Furthermore, an extension to handle large-scale systems is proposed. Beyond agricultural applications, this approach applies to various domains where cooperation among heterogeneous agents, including logistics, industry, and search and rescue, can be advantageous. Realistic simulation results and laboratory experiments validate the effectiveness of the approach. As future work, we aim to integrate human intent prediction strategies to proactively adapt plans based on anticipated human actions, further enhancing coordination and efficiency.