The work proposes using ELCs for robust and reactive obstacle avoidance, which allows for stable, smooth trajectories.
Creating mechanisms to manage the interaction and switching between these controllers to enhance safety, flexibility, and reliability. Autonomous vehicle navigation : from behavioral...
Based on the academic work by Lounis Adouane, Autonomous Vehicle Navigation: From Behavioral to Hybrid Multi-Controller Architectures (2016) explores the shift from purely reactive behavioral systems to sophisticated hybrid architectures to achieve safe, fully autonomous vehicle navigation. 1. From Behavioral (Reactive) to Hybrid Architecture The work proposes using ELCs for robust and
Developing reliable local controllers for specific tasks such as target reaching, smooth trajectory planning, and obstacle avoidance. smooth trajectory planning
Ensuring the navigation system can handle moving obstacles by using real-time sensor data and predictive modeling. 3. Safety and Reliability
The techniques are applied to unmanned ground vehicles (UGVs) or urban electric vehicles in dynamic environments.
The core focus is to guarantee safety by allowing the system to re-plan and evade dangerous situations instantly.