Navigation of Robotic Swarmalators with Dynamics and Constraints
Xinyue Xu, Wei Xiao, Steven Ceron
AI summary
Problem
Prior swarmalator research assumes ideal omnidirectional robots in obstacle-free spaces, limiting real-world deployment. This work addresses how to adapt emergent collective behaviors to constrained unicycle and bicycle dynamics while ensuring safe navigation in cluttered environments.
Approach
The authors map ideal swarmalator velocity commands to unicycle and bicycle kinematic models, then integrate Control Barrier Functions (CBFs) with Control Lyapunov Functions (CLFs) to guarantee obstacle avoidance and target tracking while preserving self-organization.
Key results
- Reproduction of static sync, phase waves, and active waves under unicycle and bicycle dynamics
- Quantitative analysis of formation radii expansion and repulsion parameter adjustments for non-holonomic constraints
- CBF-augmented global and local control enabling safe obstacle avoidance and object transport
- Validation that kinematic constraints can be mapped to swarmalator commands without redesigning core self-organization
Why it matters
Provides a scalable framework for deploying heterogeneous robot swarms that self-organize into complex formations while safely navigating cluttered real-world environments.
Abstract
Swarmalator studies have enabled self-organized collective behaviors that emerge from dual spatial and temporal coupling, without relying on external inputs. These behav- iors arise solely from attractive and repulsive interactions modulated by a few global parameters. Here, we treat the swarmalator model as a planner and study how several of the collective behaviors change in terms of space-phase organiza- tion when the agents are robots with vehicle dynamics and constraints: including omnidirectional, unicycle, and bicycle dynamics. Furthermore, we use the control barrier function method to guarantee that the collective can navigate around objects, through cluttered environments, and transport objects in between obstacles by exploiting global and local control methods. This work brings us closer to realizing large groups of robotic swarmalators, of heterogeneous dynamics, that can enable shape formation, navigation, and object manipulation in cluttered environments. Swarm Robotics, Distributed Robot Systems, Optimization and Optimal Control.