Autonomous Vehicle Navigation : From Behavioral... Apr 2026

Developing reliable local controllers for specific tasks such as target reaching, smooth trajectory planning, and obstacle avoidance.

The techniques are applied to unmanned ground vehicles (UGVs) or urban electric vehicles in dynamic environments. Autonomous vehicle navigation : from behavioral...

The work proposes using ELCs for robust and reactive obstacle avoidance, which allows for stable, smooth trajectories. This framework provides a solid foundation for designing

This framework provides a solid foundation for designing robust control architectures that bridge the gap between basic reactive behaviors and fully automated driving systems. The validation results of this architecture? 2. Key Components of Navigation

The proposed architectures are validated through MATLAB/Simulink simulation and experiments.

Traditional reactive navigation systems (like potential fields) work well for simple obstacle avoidance but fail in cluttered or complex dynamic environments, often leading to local minima (trapping the vehicle).

This approach combines the speed of reactive, behavior-based systems (e.g., "avoid obstacle," "follow lane") with a high-level strategic planner. This hybrid approach ensures the vehicle can manage complex scenarios by switching between or combining elementary controllers based on the environment. 2. Key Components of Navigation