Cooperative Target Search of UAV Swarm with Communication Distance Constraint

Abstract

This paper proposes a cooperative search algorithm to enable swarms of unmanned aerial vehicles (UAVs) to capture moving targets. It is based on prior information and target probability constrained by inter-UAV distance for safety and communication. First, a rasterized environmental cognitive map is created to characterize the task area. Second, based on Bayesian theory, the posterior probability of a target’s existence is updated using UAV detection information. Third, the predicted probability distribution of the dynamic time-sensitive target is obtained by calculating the target transition probability. Fourth, a customized information interaction mechanism switches the interaction strategy and content according to the communication distance to produce cooperative decision-making in the UAV swarm. Finally, rolling-time domain optimization generates interactive information, so interactive behavior and autonomous decision-making among the swarm members are realized. Simulation results showed that the proposed algorithm can effectively complete a cooperative moving-target search when constrained by communication distance yet still cooperate effectively in unexpected situations such as a fire.

Cooperative Search Optimization of an Unknown Dynamic Target Based on the Modified TPM

“…Considering the problem of trajectory prediction of unknown dynamic targets, in reference [7], the authors established a rasterized environmental uncertainty map, search probability map, and pheromone map model and determined the prediction probability distribution of dynamic timesensitive targets by calculating the target transition probability. Based on the target probability graph method, a linear model was developed to predict the location of unknown targets and solve the search problem of linear moving targets [8].…”

“…For this reason, it is necessary to solve the problem of dynamic target trajectory prediction and then to generate and optimize the search path. The parameter setting in the traditional TPM algorithm [7,8] is not comprehensive enough to make full use of the target motion state data, so it cannot predict the target trajectory well. In addition, the traditional APF algorithm [9] is commonly used to deal with the obstacle avoidance problem but is easy to fall into a local optimum under an uncertain environment.…”