A Dynamic-Programming-Styled Algorithm for a Class of Multi-Agent Optimal Task Assignment

2001 ◽  
Author(s):  
Guang Yang ◽  
Vikram Kapila ◽  
Ravi Vaidyanathan
Keyword(s):  
Dynamic Programming ◽  
Task Assignment ◽  
Research Activity ◽  
Spacecraft Formation ◽  
Assignment Algorithm ◽  
Decentralized Decision Making ◽  
Current Research Activity ◽  
Multi Agent ◽  
Global Optimal ◽  
Multiple Spacecraft

Abstract In this paper, we use a dynamic programming formulation to address a class of multi-agent task assignment problems that arise in the study of fuel optimal control of multiple agents. The fuel optimal multi-agent control is highly relevant to multiple spacecraft formation reconfiguration, an area of intense current research activity. Based on the recurrence relation derived from the celebrated principle of optimality, we develop an algorithm with a distributed computational architecture for the global optimal task assignment. In addition, we propose a communication protocol to facilitate decentralized decision making among agents. Illustrative studies are included to demonstrate the efficacy of the proposed multi-agent optimal task assignment algorithm.

Fuel optimal manoeuvres for multiple spacecraft formation reconfiguration using multi-agent optimization

2002 ◽  
Vol 12 (2-3) ◽  
pp. 243-283 ◽  
Author(s):  
Guang Yang ◽  
Qingsong Yang ◽  
Vikram Kapila ◽  
Daniel Palmer ◽  
Ravi Vaidyanathan
Keyword(s):  
Spacecraft Formation ◽  
Formation Reconfiguration ◽  
Multi Agent ◽  
Multiple Spacecraft

Applied Mechanics Problems in the Oil and Gas Industry

1986 ◽  
Vol 39 (11) ◽  
pp. 1687-1696 ◽  
Author(s):  
Jean-Claude Roegiers
Keyword(s):  
Oil And Gas ◽  
Petroleum Industry ◽  
Oil Industry ◽  
Oil And Gas Industry ◽  
Production Equipment ◽  
Paper Briefly ◽  
Research Activity ◽  
Gas Industry ◽  
Well Production ◽  
Current Research Activity

The petroleum industry offers a broad spectrum of problems that falls within the domain of expertise of mechanical engineers. These problems range from the design of well production equipment to the evaluation of formation responses to production and stimulation. This paper briefly describes various aspects and related difficulties with which the oil industry has to deal, from the time the well is spudded until the field is abandoned. It attempts to delineate the problems, to outline the approaches presently used, and to discuss areas where additional research is needed. Areas of current research activity also are described; whenever appropriate, typical or pertinent case histories are used to illustrate a point.

scholarly journals Toward delay-tolerant multiple-unmanned aerial vehicle scheduling system using Multi-strategy Coevolution algorithm

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881523 ◽  
Author(s):  
Yohanes Khosiawan ◽  
Sebastian Scherer ◽  
Izabela Nielsen
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Unmanned Aerial Vehicle ◽  
Task Assignment ◽  
Computation Time ◽  
Task Sequence ◽  
Swarm Optimization ◽  
Assignment Algorithm ◽  
Aerial Vehicle ◽  
Delay Tolerant

Autonomous bridge inspection operations using unmanned aerial vehicles take multiple task assignments and constraints into account. To efficiently execute the operations, a schedule is required. Generating a cost optimum schedule of multiple-unmanned aerial vehicle operations is known to be Non-deterministic Polynomial-time (NP)-hard. This study approaches such a problem with heuristic-based algorithms to get a high-quality feasible solution in a short computation time. A constructive heuristic called Retractable Chain Task Assignment algorithm is presented to build an evaluable schedule from a task sequence. The task sequence representation is used during the search to perform seamless operations. Retractable Chain Task Assignment algorithm calculates and incorporates slack time to the schedule according to the properties of the task. The slack time acts as a cushion which makes the schedule delay-tolerant. This algorithm is incorporated with a metaheuristic algorithm called Multi-strategy Coevolution to search the solution space. The proposed algorithm is verified through numerical simulations, which take inputs from real flight test data. The obtained solutions are evaluated based on the makespan, battery consumption, computation time, and the robustness level of the schedules. The performance of Multi-strategy Coevolution is compared to Differential Evolution, Particle Swarm Optimization, and Differential Evolution–Fused Particle Swarm Optimization. The simulation results show that Multi-strategy Coevolution gives better objective values than the other algorithms.

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