Currently, many task allocation methods rely on detailed expert knowledge to coordinate robots.
The task-resource allocation method not only calculates the mission completion time, but also predicts the probability of mission success.
In order to put DRSTOP into practical application, this paper established a series of methods, namely, task classification method, data-bits allocation method, control information generation method, control information formatting and sending method, and decoded results obtaining method and so on.
Assignment is a common method of task allocation in a highly-centralized process (Puranam et al., 2014).
Correspondingly, methods of task allocation should suit the design process, as does the co-specialization process.
We summarize widely used models and methods from the task allocation literature and related areas, such as vehicle routing and scheduling problems, showing similarities and differences.
This study aimed at developing some heuristic methods for the task allocation and collision-free path planning for three robots working in the common workspace.
This planning process invokes optimization methods for computing the task allocation and action sequences for individual robots [26, 27].
Aiming at the task allocation of collaborative technique in wireless sensor network, a method for optimized task allocation based on elastic neural network is proposed under the background of multi-sensor tracking.
A comprehensive failure rate allocation method is proposed for conducting the task of reliability allocation.
Finally, each team invokes optimization methods to perform path planning and task allocation for the collective robots.
