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.

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 can be used to accomplish tasks that exceed the capacity of a single robot. In a robotic network, robots can work together to accomplish a common task. Cloud robotics allows robots to benefit from the massive storage and computing power of the cloud. Previous studies mainly focus on minimizing the cost of resource retrieval by robots by knowing the resource allocation in advance. Duplicating algorithms on multiple nodes can reduce the total time required to execute a task. We address the question of which algorithms should be duplicated and where the duplicates should be placed to improve overall performance. We have developed a procedure to answer wherein a robotic network cloud system should algorithms be executed and whether they should be duplicated to achieve optimal performance in terms of overall task execution time for all robots. Our proposed duplication procedure is optimal in the sense that the number of duplicated algorithms is minimal, while the result provides minimal overall completion time for all robots.