Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions (2012.05457v2)

Abstract: We present Neural-Swarm2, a learning-based method for motion planning and control that allows heterogeneous multirotors in a swarm to safely fly in close proximity. Such operation for drones is challenging due to complex aerodynamic interaction forces, such as downwash generated by nearby drones and ground effect. Conventional planning and control methods neglect capturing these interaction forces, resulting in sparse swarm configuration during flight. Our approach combines a physics-based nominal dynamics model with learned Deep Neural Networks (DNNs) with strong Lipschitz properties. We make use of two techniques to accurately predict the aerodynamic interactions between heterogeneous multirotors: i) spectral normalization for stability and generalization guarantees of unseen data and ii) heterogeneous deep sets for supporting any number of heterogeneous neighbors in a permutation-invariant manner without reducing expressiveness. The learned residual dynamics benefit both the proposed interaction-aware multi-robot motion planning and the nonlinear tracking control design because the learned interaction forces reduce the modelling errors. Experimental results demonstrate that Neural-Swarm2 is able to generalize to larger swarms beyond training cases and significantly outperforms a baseline nonlinear tracking controller with up to three times reduction in worst-case tracking errors.

• The paper presents a novel framework combining physics-based models with deep neural networks to learn and predict aerodynamic interactions in multirotor swarms.
• It leverages heterogeneous deep sets and a two-stage motion planning strategy, achieving up to a three-fold reduction in worst-case tracking errors.
• The nonlinear control law incorporates learned interaction forces and delay compensation, ensuring stability and safe close-proximity formation flight.

Neural-Swarm2: Advanced Learning-Based Planning and Control for Heterogeneous Multirotor Swarms

The paper "Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions" presents a novel framework for the motion planning and control of aerial multirotor swarms, incorporating machine learning techniques to model complex aerodynamic interactions. The proposed methodology addresses the challenges associated with flying swarms of heterogeneous drones in close proximity, which are subject to intricate aerodynamic forces such as downwash effects and ground influences.

Summary

Neural-Swarm2 integrates physics-based models with deep neural networks (DNNs) to better predict aerodynamic interaction forces among heterogeneous multirotor drones. It introduces several significant innovations:

1. Modeling Complex Interactions: The framework augments conventional multirotor dynamics with neural networks capable of learning additional dynamics caused by aerodynamic interactions within the swarm. The DNNs are designed to have strong Lipschitz properties via spectral normalization to assure stability and generalization, even with unseen data.
2. Heterogeneous Deep Sets: The authors employ a variant of deep sets that supports permutation-invariance and heterogeneity within the swarm. This approach ensures that neural networks can handle varying numbers and types of multirotors without diminishing expressiveness.
3. Motion Planning: Neural-Swarm2 employs a two-stage motion planning strategy. Initially, it uses an interaction-aware sampling-based method for initial trajectory generation, followed by sequential convex programming (SCP) to refine these trajectories. This ensures both safety and optimality, with explicit consideration of interaction forces and torque constraints.
4. Nonlinear Control: The paper extends prior work by incorporating learned interaction forces into a nonlinear control law for trajectory tracking. The control approach integrates delay compensation and provides theoretical guarantees on the stability and tracking performance.

Implications

The experimental results demonstrate that Neural-Swarm2 achieves a significant reduction in worst-case tracking errors—up to three-fold compared to baseline methods. The ability to predict interaction forces accurately allows swarms to operate safely at densities closer than previously feasible, thus enhancing their applicability in scenarios requiring tight formation flight. The paper’s contributions have both theoretical and practical implications:

• Theoretical Foundations: The introduction of heterogeneous deep sets and their integration into multirotor dynamics modeling represents a noteworthy advancement in learning-based control systems. The authors provide proof of the representational power and stability of their proposed neural networks.
• Practical Deployment: The efficient real-time prediction and control capabilities ensure that this methodology can be applied in real-world drone swarm applications, including search and rescue missions, surveillance, and mapping, where operating in confined spaces or near obstacles is critical.

Future Directions

The framework presented opens several avenues for future research and development:

• Extended Application Scope: While the focus here is on multirotor drones, the principles could be adapted to other robotic systems where interaction forces are significant, such as underwater or ground robots.
• Increased Swarm Size and Complexity: Further research could explore the scaling limits of the framework, optimizing the computational load to handle larger swarms efficiently and adapting to more complex missions.
• Enhanced Learning Models: Investigating more sophisticated machine learning architectures could improve modeling accuracy and generalization, potentially incorporating hybrid models combining learning with parameterized physical models.

In conclusion, Neural-Swarm2 represents a significant step forward in the planning and control of heterogeneous robot swarms in dynamically complex environments. By integrating advanced learning methods with robust control strategies, this research enhances the operational efficiency and safety of multirotor teams, contributing valuable insights to the broader domain of autonomous system coordination.