The field of autonomous vehicle navigation and control is moving towards more sophisticated and decentralized approaches. Researchers are exploring the use of nonlinear model predictive control (NMPC) and consensus graph optimization to enable safe and efficient navigation in complex environments. This includes the development of novel distributed control methods that can handle collisions and obstacles in real-time, as well as more accurate and reliable localization techniques. The use of point cloud processing and directional filtering is also being investigated to reduce computational burden and improve performance. Notable papers include:

• A paper on decentralized nonlinear model predictive control-based flock navigation with real-time obstacle avoidance, which integrates local obstacle avoidance constraints using point clouds into the NMPC framework.
• A paper on integrated localization and path planning for an ocean exploring team of autonomous underwater vehicles, which proposes a systematic approach for localization-aware energy-efficient collision-free path planning using consensus graph model predictive control.