In human-robot collaboration systems, the human-robot team works together to complete all tasks of the assigned job. Tasks’ precedence order is a factor that determines which task should be completed first. A human agent has an expected performance on each task, but the actual performance of the human agent on a task may differ from the expected performance at different times. Therefore, it is also necessary to monitor the human agent so that the task can be transferred to or from the robot when the actual performance of the human agent is worse or better than the expected performance. We propose an architecture for dynamic task allocation and scheduling in a collaborative system. The method is to first identify the group of tasks that should be executed first based on the precedence order of tasks. After obtaining this group of prioritized tasks, we use the quality metric (a numerical value that indicates which agent is better at completing the task) to determine which agent should be assigned each of the tasks. Finally, real-time human agent monitoring is used to detect performance changes in the human agent so that tasks can be transferred from one agent to another. All of these processes are performed automatically without the need for a human agent to schedule or reschedule. We have validated the effectiveness of the architecture through several experiments.

Precision irrigation and optimization of water use have become essential factors in agriculture because water is critical for crop growth. The proper management of an irrigation system should enable the farmer to use water efficiently to increase productivity, reduce production costs, and maximize the return on investment. Efficient water application techniques are essential prerequisites for sustainable agricultural development based on the conservation of water resources and preservation of the environment. In a previous work, an off-policy deep reinforcement learning model, Deep Q-Network, was implemented to optimize irrigation. The performance of the model was tested for tomato crop at a site in Portugal. In this paper, an on-policy model, Advantage Actor–Critic, is implemented to compare irrigation scheduling with Deep Q-Network for the same tomato crop. The results show that the on-policy model Advantage Actor–Critic reduced water consumption by 20% compared to Deep Q-Network with a slight change in the net reward. These models can be developed to be applied to other cultures with high production in Portugal, such as fruit, cereals, and wine, which also have large water requirements.

For a robot to perform a task, several algorithms must be executed, sometimes simultaneously. The algorithms can be executed either on the robot itself or, if desired, on a cloud infrastructure. The term cloud infrastructure refers to hardware, storage, abstracted resources, and network resources associated with cloud computing. Depending on the decision of where the algorithms are executed, the overall execution time and memory required for the robot, change accordingly. The price of a robot depends on its storage capacity and computational power, among other factors. We answer the question of how to maintain a given performance and deploy a cheaper robot (lower resources) by allocating computational tasks to the cloud infrastructure depending on memory, computational power, and communication constraints. Even for a fixed robot, our model provides a way to achieve optimal overall performance. We provide a general model for optimal algorithm allocation decision under certain constraints. We illustrate the model with simulation results. The main advantage of our model is that it provides optimal task allocation simultaneously for memory and time.

Robotic networks are increasingly relied upon to perform complex tasks that require efficient scheduling and task allocation to optimize processing power, resource management, and energy use. The primary goal in these systems is to enhance performance by minimizing completion time, energy consumption, and delays, while maximizing resource utilization and task throughput. Numerous studies have examined different aspects of task allocation and scheduling, from static approaches to dynamic models that adapt to real-time conditions. This article presents a comprehensive survey of the methods and strategies used in robotic network systems, considering not only traditional approaches but also the role of emerging technologies, such as cloud, fog, and edge computing. We categorize the literature from three perspectives: 1) architectures and applications; 2) methods; and 3) parameters. Furthermore, we analyze the limitations of each approach and propose directions for future research, with a particular focus on scalability, real-world applicability, and the integration of these technologies in dynamic environments.