The paper introduces a distributed predictive flocking controller for a swarm of miniature vibration‑driven robots operating in a confined arena without a leader or global map. Each robot solves a nonlinear model predictive control problem in real time, optimizing cohesion, alignment, safety, and task objectives while exchanging only predicted trajectories with neighbors. Experiments on a digital twin and real robots show that a look‑ahead discrete‑time control‑barrier‑function formulation yields the fewest collisions and widest safety margins across various swarm sizes, maintaining coordination and cohesion even near the geometric packing limit.
Coordinating many miniature robots in a confined arena, with no leader, central planner, or global map, is a core challenge for swarm robotics. As the swarm grows denser, collision avoidance becomes t...
arXiv:2607. 12861v1 Announce Type: cross Abstract: Multi-agent Reinforcement Learning (MARL) holds great potential for robot swarms, but the black-box nature of neural policies complicates strategic analysis, limiting multi-robot applications.
Multi-agent Reinforcement Learning (MARL) holds great potential for robot swarms, but the black-box nature of neural policies complicates strategic analysis, limiting multi-robot applications. Furthermore, complex swarm behaviors can surprisingly emerge from simple rewards without explicit aggregation incentives.
The paper introduces a conflict‑predictive variable horizon for distributed model predictive control in multi‑drone collision avoidance. Each drone locally estimates future conflicts using observed positions and confidence funnels, then selects the minimal horizon that covers the farthest predicted conflict, shrinking in clear air and expanding only when necessary. The authors prove that this adaptive horizon preserves recursive feasibility and asymptotic stability for linear models, and demonstrate in simulation that it reduces per‑step solver cost and total computation while maintaining separation on dense benchmarks.
arXiv:2610.16522v2 Announce Type: replace-cross Abstract: Safe robot navigation in unknown, dynamic environments requires more than geometric collision checking: a robot still far from an obstacle ma...
arXiv:2510. 09204v4 Announce Type: replace-cross Abstract: Centralized trajectory optimization in the joint space of multiple robots allows access to a larger feasible space that can result in smoother trajectories, especially while planning in tight spaces.
arXiv:2608. 07751v1 Announce Type: cross Abstract: Safe and efficient robot navigation in crowds requires anticipating pedestrian motion despite uncertain and potentially shifting prediction errors.
arXiv:2509. 23960v2 Announce Type: replace-cross Abstract: Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge.
arXiv:2610.14248v1 Announce Type: cross Abstract: Diffusion models provide expressive trajectory priors for multi-robot motion planning (MRMP), but scaling them to large teams requires coordinating i...
arXiv:2608. 06587v1 Announce Type: cross Abstract: Robot swarms utilize many independent limited-sensing agents to produce complex emergent behaviors without requiring centralized control.
The paper compares Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) for cooperative motion planning of dual‑arm space manipulators tasked with reaching a target while avoiding inter‑arm collisions. A centralized state representation and multi‑objective reward function are used, and SAC consistently outperforms PPO across five random seeds, achieving an 80% success rate within 1.6 M steps versus PPO’s failure to reach this threshold within 4 M steps. Both algorithms degrade on unseen boundary targets, but SAC retains most of its performance while PPO collapses, revealing a reward‑loitering failure mode for PPO. whyItMatters":"The study demonstrates that off‑policy SAC is more robust and effective than on‑policy PPO for coordinating dual‑arm space robots, informing algorithm choice for active debris removal and on‑orbit servicing tasks."
arXiv:2610.28286v1 Announce Type: cross Abstract: Teams of large language model (LLM) agents are increasingly applied to complex tasks, but their collaboration protocols are mostly designed empirical...