UAV tracking one moving target in an oilfield environment with dynamic obstacles based on an improved salp swarm algorithm

Abstract Unmanned aerial vehicles (UAVs) tracking one moving target in an oilfield environment with dynamic obstacles has been studied. Firstly, the UAV tracking one target is modeled mathematically, including the oilfield environment, the UAV kinematic model, the moving target model and so on. Considering the oilfield environment with dynamic obstacles, the chance-constraint method is incorporated into the distributed model predictive control (DMPC) to realise the target tracking and avoid the obstacles in the environment. Secondly, to obtain high-quality control-input sequences, the improved salp swarm algorithm (ISSA) is designed as the solver for the chance-constraint DMPC, integrating the sine-tent-cosine chaotic mapping in the initialisation, the differential mutation operation in the leader-position updating and the nonlinear adaptive inertia weights in the follower-position updating. Finally, the simulation results show that the proposed method has great performance in tracking the moving target in an oilfield environment with dynamic obstacles.