Adoption of Collaborative Robotics in Fruit Harvesting

Collaborative robotics in agriculture is designed to automate labor-intensive processes. In contrast to traditional autonomous systems, collaborative multi-agent robotic systems require active interaction between robots and human operators. This interaction creates the need for new methods for coordination, adaptation, and safety assurance in uncertain and dynamically changing environments. (Research purpose) The study aims to develop both theoretical and practical approaches to modeling the behavior and control of collaborative multi-agent robotic systems. The primary objective is to ensure efficient task allocation, coordinated agent behavior, and safe human-robot interaction during fruit harvesting operations. (Materials and methods) To achieve these objectives, the study employed methods from game theory, machine learning, and risk-aware control. A mathematical model was developed to describe the interactions among agents, incorporating the probabilistic nature of the environment and the involvement of a human operator. The proposed solutions were validated through a combination of numerical simulations and experimental data collected from a testbed replicating real-world agricultural scenarios. (Results and discussion) Algorithms were developed to enable coordination, adaptation, and dynamic task redistribution within the collaborative multi-agent robotic system. These algorithms demonstrated robustness against sensor inaccuracies, communication delays, and external disturbances typical of agricultural settings. Special attention was given to the system’s ability to adapt to human operator inputs, including task prioritization and context-sensitive interaction strategies. Simulation results showed enhanced system performance, characterized by more balanced task distribution among robots, reduced conflict during joint operations, and minimized idle time. Safety metrics also improved, including a reduction in collision risks and fewer incorrect responses to the presence of human operators in the work area. (Conclusions) The developed models and algorithms provide a foundation for the design of intelligent collaborative multi-agent robotic systems capable of adaptive and safe interaction in agricultural production. Their application can enhance the efficiency of automated harvesting processes while reducing reliance on manual labor.

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