Path planning for automated guided vehicle systems with time constraints using timed Petri nets

Abstract

Automated guided vehicles (AGVs) are extensively used in many applications such as intelligent transportation, logistics, and industrial factories. In this paper, we address the path planning problem for an AGV system (i.e. a team of identical AGVs) with logic and time constraints using Petri nets. We propose a method to model an AGV system and its static environment by timed Petri nets. Combining the structural characteristics of Petri nets and integer linear programming technique, a path planning method is developed to ensure that all task regions are visited by AGVs in time and forbidden regions are always avoided. Finally, simulation studies are presented to show the effectiveness of the proposed path planning methodology.

Path Planning of Multi-Type Robot Systems with Time Windows Based on Timed Colored Petri Nets

“…For the temporal requirement, we assumed that the task regions r 69 , r 3 , r 24 , r 45 , r 21 , r 10 , and r 11 should be visited in the time windows [10,12], [3,5], [10,15], [17,25], [8,22], [3,8], and [15,20], respectively. Therefore, the task requirement ϕ for the MTRS can be represented as follows: ϕ = (r 65 , E 1 ), (r 69 , E 1 , [10,12]), (r 3 , E 2 , [3,5]), (r 24 , E 3 , [10,15]), (r 45 , E 3 , [17,25]), (r 21 , S 2 , [8,22]), (r 21 , S 3 , [8,22]), (r 10 , S 1 , [3,8]), (r 10 , S 3 , [3,8]), (r 13 , A), (r 48 , A), (r 11 , J, [15,20]), (r 37 , J), (r 68 , J), which requires that task region r 65 be visited by a C 1 robot; task region r 69 be visited by a C 1 robot in the time window [10,12], task region r 3 be visited by a C 2 robot in the time window [3,5]; task region r 24 be visited by a C 3 robot in the time window [10,15]; task region r 45 be visited by a C 3 robot in the time window [17,25]; task region r 21 be visited by C 2 and C 3 robots in the time window [8,22]; task region r ...…”

“…In an MRS, the system's environment was divided into zones, and the movement of any robot from one zone to another was treated as a discrete event [20][21][22][23]. Petri nets(PNs) are an efficient tool to model, analyze, and control discrete event systems [24][25][26][27][28][29][30][31], such as robotics and intelligent transportation.…”

“…Owing to the restricted sensors and actuators, an optimal PN controller for MRSs was devised [20] to minimize crashes when some events were indistinguishable and uncontrollable. The problem of path planning for MRSs with logic and time constraints was discussed using timed Petri nets [22], and an ILPP was established to determine the optimum paths of robots so that the task zones were visited by a robot within a given time window and the forbidden regions were always avoided.…”

A refinement and abstraction method of the SPZN formal model for intelligent networked vehicles systems

“…Through the application of Petri Net, researchers and engineers can establish system models, and apply techniques such as model checking, state space analysis, and performance optimization for verification and analysis. In the intelligent networked vehicle system, Petri Net can be used to establish the behavior model of the system, describing the interaction and concurrent operation among vehicles, sensors, communication modules and control algorithms [24][25][26]. The performance and correctness of the system can be deduced by analyzing the state transition and transition trigger conditions of Petri Net.…”