DTTA - Distributed, Time-division Multiple Access based Task Allocation Framework for Swarm Robots

Swarm robotic systems, unlike traditional multi-robotic systems, deploy number of cost effective robots which can co-operate, aggregate to form patterns/formations and accomplish missions beyond the capabilities of individual robot. In the event of fire, mine collapse or disasters like earthquake, swarm of robots can enter the area, conduct rescue operations, collect images and convey locations of interest to the rescue team and enable them to plan their approach in advance. Task allocation among members of the swarm is a critical and challenging problem to be addressed. DTTA- a distributed, Time-division multiple access (TDMA) based task allocation framework is proposed for swarm of robots which can be utilised to solve any of the 8 different types of task allocation problem identified by Gerkey and Mataric´. DTTA is reactive and supports task migration via extended task assignments to complete the mission in case of failure of the assigned robot to complete the task. DTTA can be utilised for any kind of robot in land or for co-operative systems comprising of land robots and air-borne drones. Dependencies with other layers of the protocol stack were identified and a quantitative analysis of communication and computational complexity is provided. To our knowledge this is the first work to be reported on task allocation for clustered scalable networks suitable for handling all 8 types of multi-robot task allocation problem. Effectiveness and feasibility of deploying DTTA in real world scenarios is demonstrated by testing the framework for two diverse application scenarios

Optimisation of Flight and Maintenance Planning for Defence Aviation with Modified Artificial Bee Colony Algorithm

The planning of flight operations and maintenance is a crucial activity for both commercial and military aircraft. Military aircraft have to be always mission-ready. The task of ensuring this can become quite challenging when several operational requirements and maintenance constraints are to be fulfilled simultaneously. This paper, therefore, addresses the optimisation of flight and maintenance planning (FMP) when several diverse factors such as aircraft flying hours (AFH), flight cycles (FC), calendar life, annual flying requirement (AFR), etc. are to be factored in. Such a problem has not been considered previously. Because the problem can become unwieldy to solve by other methods, two schemes, that is, the genetic algorithm (GA) and modified artificial bee colony (ABC) algorithm for constrained optimisation have been utilised. The objective is to maximise the utilisation rate (UR) of aircraft, while also satisfying other operational and maintenance constraints. The algorithm is tested on a fleet of eight aircraft. In addition to a one-year planning period, a planning horizon of ten years has also been simulated. The results show that both the GA and modified ABC algorithm can be effectively used to solve the FMP problem

Development of an efficiency model for optimal construction productivity through effective supervision of worker performance

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Bayesian Detector Based Superior Selective Reporting Mechanism for Cooperative Spectrum Sensing in Cognitive Radio Networks

AbstractCognitive radio network(CRN) coupled with spectrum sensing technology enables unlicensed secondary users (SUs) to opportunistically access the unused licensed spectrum of primary users (PUs). Cooperative Spectrum Sensing (CSS) significantly improves the detection probability of primary user transmission. Nevertheless, current CSS techniques render shortcomings including energy consumption and overhead in sensing phase. Overheads are consequence of multiple cooperative SUs reporting their decision to the fusion center. In this paper, we propose Bayesian Detector based Superior Selective Reporting Cooperative Sensing(BD- SSRCS)scheme. Superior Selective Reporting (SSR)scheme, competently reduces reporting overhead and mitigates interference to PUs. Bayesian based sensing technique for local sensing improves detection performance, spectrum utilization and secondary user throughput. Our analysis and simulation results manifest the outcome of presented work in terms of higher detection probability, lower miss detection rate and lesser detection overhead, as opposed to the traditional cooperative sensing methods. Moreover, miss detection probability and sensing time can be reduced by ideally choosing sensing time allocation factor

Optimisation of Flight and Maintenance Planning for Defence Aviation with Modified Artificial Bee Colony Algorithm

The planning of flight operations and maintenance is a crucial activity for both commercial and military aircraft. Military aircraft have to be always mission-ready. The task of ensuring this can become quite challenging when several operational requirements and maintenance constraints are to be fulfilled simultaneously. This paper, therefore, addresses the optimisation of flight and maintenance planning (FMP) when several diverse factors such as aircraft flying hours (AFH), flight cycles (FC), calendar life, annual flying requirement (AFR), etc. are to be factored in. Such a problem has not been considered previously. Because the problem can become unwieldy to solve by other methods, two schemes, that is, the genetic algorithm (GA) and modified artificial bee colony (ABC) algorithm for constrained optimisation have been utilised. The objective is to maximise the utilisation rate (UR) of aircraft, while also satisfying other operational and maintenance constraints. The algorithm is tested on a fleet of eight aircraft. In addition to a one-year planning period, a planning horizon of ten years has also been simulated. The results show that both the GA and modified ABC algorithm can be effectively used to solve the FMP problem.</jats:p

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