A collaborative control protocol for agricultural robot routing with online adaptation. (September 2019) - Dusadeerungsikul, Puwadol Oak; Nof, Shimon Y.
Highlights: Collaborative control protocol is designed for early detection of stress in plants. Collaborative control theory-based protocol combines humans, robot, and sensors. The protocol consists of routing algorithm and adaptive search algorithm. The protocol is superior in terms of detecting infected plants, and efficiency. Abstract: Stress in crops is one of the major concerns in precision agriculture because it indicates the emergence of disease and damage in plants. Detecting the stress condition of a plant early is critical. A system that can monitor the condition of plants is a desirable solution. In this work, Collaborative Control Theory is utilized to construct a new system, ARS (agricultural robotic system) which synchronizes humans, a mobile robot, and a variable set of sensors to effectively perform the monitoring and detection tasks. A key protocol for that system, which combines routing algorithm, adaptive search algorithm, and collaboration control framework has been developed and validated, and is presented in this article. By using greenhouse as a case study structure, the protocol routes a robot to visit the sampled locations by using a genetic algorithm. In addition, the search algorithm can be guided by the predictive characteristics of the crops' stress, which can spread to other plants according to sunlight, airflow direction, and other known conditions. Based on simulation experiments, the results indicate with statistical significance that (1) the routing algorithm increases the number of successful detections of existing stressed plants by 45.77% compared to monitoring without this routing algorithm. (2) The adaptive search algorithm improves the number of successful detections of stressed plants by 71.88% compared to a system without the adaptive search algorithm. (3) The new protocol developed in this research yields the highest overall robotic efficiency, compared with a system without collaborative control framework.