Multi-policeman cooperative hunting task allocation and path planning method under road network constraint

Abstract

The invention discloses a multi-policeman cooperative hunting task allocation and path planning method under road network constraints. The method comprises the following steps: 1) obtaining a road topology map G of a road network; 2) acquiring police officer distribution information and task target position information in a road network G; 3) determining a next possible node set of the task targetaccording to the position where the task target appears in combination with the road topological map; 4) obtaining the moving speed of each policeman and the task target; 5) determining a police officer set to be allocated according to the interception point set and the moving speeds of the police officers and the task targets; 6) calculating the congestion degree of each intersection in the interception point set; 7) establishing a multi-policeman cooperative hunting task allocation and path planning optimization model under road network constraints; 8) solving the optimization model to obtain a multi-policeman cooperative hunting task allocation and path planning scheme. According to the multi-policeman cooperative hunting task allocation and path planning method based on the road network constraint, the multi-policeman cooperative hunting task allocation and path planning scheme is obtained by establishing the multi-objective optimization model, the policeman is reasonably allocated to each hunting point, and the hunting efficiency is effectively improved.

Description

technical field [0001] The invention relates to artificial intelligence technology, in particular to a multi-police officer cooperative round-up task assignment and path planning method under the constraints of a road network. Background technique [0002] In order to realize the task of arresting suspicious targets, the cooperative police officers need to move to the place where the suspicious targets appear, and then arrest them. The flow of people, the terrain of the round-up point, etc. The existing literature fails to consider the above impacts, so it is urgent to design round-up strategies and deploy police forces in combination with actual arrest scenarios. The round-up scenarios studied in the existing literature are relatively simple, and can be roughly divided into the following two categories: one is to assume that both the escape target and the round-up robot can only move towards the adjacent grid in the grid map, and the Markov model or Other methods estimate...

AI Technical Summary

Problems solved by technology

The existing literature fails to consider the above impact, so it is urgent to design round-up strategies and deploy police forces in combination with actual arrest scenarios

The round-up scenarios studied in the existing literature are relatively simple, and can be roughly divided into the following two categories: one is to assume that both the escape target and the round-up robot can only move towards the adjacent grid in the grid map, and use the Markov model or Other methods estimate the state of the escaping target; the other is that there is no road network constraint at all, and EKF is used to estimate the trajectory of the escaping target online, ignoring the constraints of the real road network

In the actual arrest process, suspicious targets and police officers can only move strictly according to the real road network, and trajectory prediction is more difficult when there are many intersections

Images