An Optimization-Based Distributed Planning Algorithm: A Blackboard-Based Collaborative Framework

Abstract

Motivated by the need for multiple agents to collaborate in order to solve a distributed resource allocation planning problem, this paper develops a distributed framework that combines each agent's information, expertise, responsibility, and asset ownership with the goal of optimizing a given mission objective. A mission is a collection of interdependent tasks to be executed in a directed/sequential sequence. Each task is modeled by a vector of resource requirements, a processing time, and a start time (release time). Each agent has a subset of tasks for which it is responsible, and owns a set of heterogeneous assets, where each asset is modeled by a vector of resource capabilities that it provides. Multiple agents must collaboratively allocate assets to tasks to maximize an expected mission performance, defined by how well all of the tasks' requirements are satisfied by the allocated asset capabilities. Our agent-based distributed planning framework uses a blackboard communication paradigm to exchange information among agents. The framework contains an intra-agent and an interagent module that support individual and cooperative planning, respectively. The intra-agent module employs an optimization-based m-best asset allocation algorithm to match an agent's own tasks with its locally owned assets. The interagent module coordinates the exchange of information and asset allocations among agents to improve the local plans using an asset pricing mechanism, and includes a means for characterizing an agent's cooperative behavior.Index Terms-Blackboard architecture, collaborative planning, distributed agents, operational-level planning, resource allocation.

Distributed tasks-platforms scheduling method to holonic-C2 organization

“…The measure model of tasks-platforms scheduling is used to judge whether the plan is good or not. In this paper, the task execution quality model is designed as the measure [18,19]. The higher the quality is, the better the plan is.…”

A Two-Stage Approach to Collaborative Fiber Placement through Coordination of Multiple Autonomous Industrial Robots

“…Han et al [17] developed algorithms for allocating asset packages to a set of interdependent tasks that are associated with resource requirements. Later, Han et al [18] extended the work to handle asset allocation and task planning for multiple agents by devising a blackboard-based collaborative framework. A generic framework for distributed multi-robot cooperation is presented in [19].…”

Formation and adjustment of manned/unmanned combat aerial vehicle cooperative engagement system

“…So far, previous researches on MCAV/UCAV coalition and UCAV coalition mainly focus on SCF, but are seldom involved in DCA. However, dynamic adjustment of action plan has been studied in some other areas, which mainly include the adjustment of C2 organization [10] and train operation [11,12].…”

“…Tables 2 -5 show resource demand of initial tasks, resource capability of UCAVs, location information of all initial tasks and initial position information of all UCAVs. In addition, the completion time set of initial tasks is (25,15,10,12,13,17,23,15,14,7,22,16,15,8,19,14,23,18), and the velocity set of UCAVs is (2, 2.5, 4, 3, 3, 4.5, 3.5, 5, 6, 2.5, 3.5, 5, 4, 3, 4.5, 6, 4.5, 3.5, 4, 7).…”