Adaptive scheduling method for dynamic robotic cell based on pattern classification algorithm

In order to deal with the dynamic production environment with frequent fluctuation of processing time,robotic cell needs an efficient scheduling strategy which meets the real-time requirements.This paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling process.The method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler,which can adjust the scheduling rules according to the current production status.In the process of establishing scheduler,how to choose essential attributes is the main difficulty.In order to solve the low performance and low efficiency problem of embedded feature selection method,based on the application of Extreme Gradient Boosting model(XGBoost)to obtain the adaptive scheduler,an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization(PSO)is employed to acquire the optimal subset of features.The results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching rules.At the same time,it can meet the demand of real-time scheduling.

Deadlock-free Supervisor Design for Robotic Manufacturing Cells With Uncontrollable and Unobservable Events

In this paper,a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net formalism.First,a Petri net for the deadlock control of such systems is defined.Its admissible markings and first-met inadmissible markings(FIMs)are introduced.Next,place invariants are designed via an integer linear program(ILP)to survive all admissible markings and prohibit all FIMs,keeping the underlying system from reaching deadlocks,livelocks,bad markings,and the markings that may evolve into them by firing uncontrollable transitions.ILP also ensures that the obtained deadlock-free supervisor does not observe any unobservable transition.In addition,the supervisor is guaranteed to be admissible and structurally minimal in terms of both control places and added arcs.The condition under which the supervisor is maximally permissive in behavior is given.Finally,experimental results with the proposed method and existing ones are given to show its effectiveness.