Decentralized Nonlinear Model Predictive Control-Based Flock Navigation with Real-Time Obstacle Avoidance in Unknown Obstructed Environments (2505.09434v2)

Abstract: This work extends our prior work on the distributed nonlinear model predictive control (NMPC) for navigating a robot fleet following a certain flocking behavior in unknown obstructed environments with a more realistic local obstacle avoidance strategy. More specifically, we integrate the local obstacle avoidance constraint using point clouds into the NMPC framework. Here, each agent relies on data from its local sensor to perceive and respond to nearby obstacles. A point cloud processing technique is presented for both two-dimensional and three-dimensional point clouds to minimize the computational burden during the optimization. The process consists of directional filtering and down-sampling that significantly reduce the number of data points. The algorithm's performance is validated through realistic 3D simulations in Gazebo, and its practical feasibility is further explored via hardware-in-the-loop (HIL) simulations on embedded platforms.

Decentralized Nonlinear Model Predictive Control-Based Flock Navigation with Real-Time Obstacle Avoidance

The paper authored by Nuthasith Gerdpratoom and Kaoru Yamamoto addresses the challenge of autonomous robot navigation in obstructed environments through a decentralized nonlinear model predictive control (NMPC) approach. This research extends previous work by incorporating a more nuanced strategy for local obstacle avoidance using point cloud data. The focus is on facilitating robot fleet navigation by simulating the flocking behavior seen in nature, such as that of birds or fish.

In the context of multi-agent systems, the NMPC framework is adopted to navigate a fleet of robots using local sensors for real-time response to obstacles, which are typically represented by point clouds from sensors like LiDAR. This paper introduces a specific point cloud processing technique involving directional filtering and down-sampling to efficiently manage the computational demands associated with obstacle detection and avoidance. These methods are essential for accommodating both 2D and 3D point cloud data while ensuring the computational feasibility of the optimizations.

The proposed NMPC strategy is validated through simulations in Gazebo, demonstrating its efficiency in navigating unknown obstructed environments. Additionally, the practical viability of this method is confirmed through hardware-in-the-loop (HIL) simulations on embedded systems, specifically using Raspberry Pi 4, which provides insights into the computational load and feasibility of implementation in low-resource embedded platforms.

The authors successfully illustrate that their approach enables effective flock navigation while ensuring obstacle avoidance in real-time, leveraging the local sensing capabilities of each agent. A key contribution of this work is the introduction of a local obstacle avoidance constraint within the NMPC framework, facilitating the autonomous navigation of a robot fleet with real-world applicability.

Key Findings and Numerical Results

1. Local Obstacle Avoidance Integration: Integration of point cloud data into the NMPC framework for obstacle avoidance in real-time.
2. Obstacle Processing: The use of directional filtering and down-sampling effectively reduced computational demand, managing the large data sets derived from common robotic sensors.
3. Simulation Validation: The methodology was validated through realistic simulations, indicating robustness in various scenarios.
4. HIL Feasibility: Demonstrated feasibility of NMPC-based navigation on Raspberry Pi 4, with an average solver time significantly below the established threshold (25 ms), and the system efficiently utilizing CPU resources across simulations.

Implications and Future Perspectives

The practical implications of this research are significant, suggesting that NMPC frameworks can be effectively applied to robotic navigation tasks in complex environments with unknown obstacles. These findings provide a pathway for more autonomous systems in industrial and service robots, where local obstacle navigation is crucial.

From a theoretical perspective, this work opens avenues for refining control strategies in robotics, particularly in the optimization of computational resources in distributed systems. Future developments could include simplifying obstacle representation to further reduce computational burdens, as well as adaptive strategies for multi-agent communication to enhance fleet coordination in more complex environments.

The paper presents a solid foundation for advancing the application of NMPC in robotics, bridging the gap between theory and practical implementation by addressing the challenges posed by real-time obstacle avoidance.