arXiv:2606.14763v1 Announce Type: cross Abstract: Real-time autonomous navigation in dynamic, unknown environments remains a fundamental challenge for mobile robotics. We propose a map-free framework that tightly integrates reactive rolling-horizon planning with nonlinear Model Predictive Control (MPC). At each control cycle, a LiDAR-based Gaussian occupancy representation is constructed and used to generate collision-free trajectories via A* search, which are then tracked by a CasADi/IPOPT MPC formulation incorporating a smooth sigmoid obstacle barrier. To improve robustness to parameter sens
The continuous advancements in AI and robotics research, particularly in areas like Bayesian Optimization and Model Predictive Control, are enabling more sophisticated autonomous navigation solutions for dynamic and unknown environments.
This research addresses a critical challenge in mobile robotics, promising more robust and adaptable autonomous agents, which is essential for expanding their utility across various applications, including defence and logistics.
The proposed map-free framework integrating reactive planning with nonlinear MPC, using real-time sensor data for obstacle avoidance, enhances the adaptability and reliability of autonomous navigation in complex settings.
- · Robotics manufacturers
- · Logistics and delivery companies
- · Defense sector
- · AI software developers
- · Companies reliant on less autonomous navigation solutions
- · Sectors with high labor costs for navigation-intensive tasks
More sophisticated and reliable autonomous agents will become available for deployment in unstructured environments.
Increased adoption of autonomous mobile robots in industries such as warehousing, last-mile delivery, and reconnaissance.
Potential for new regulations and ethical frameworks to govern the widespread deployment of highly autonomous systems capable of complex real-time decision-making.
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Read at arXiv cs.LG