Heuristic Predictive Control for Multi-Robot Flocking in Congested Environments

Multi-robot flocking possesses extraordinary advantages over a single-robot system in diverse domains, but it is challenging to ensure safe and optimal performance in congested environments. Hence, this paper is focused on the investigation of distributed optimal flocking control for multiple robots in crowded environments. A heuristic predictive control solution is proposed based on a Gibbs Random Field (GRF), in which bio-inspired potential functions are used to characterize robot-robot and robot-environment interactions. The optimal solution is obtained by maximizing a posteriori joint distribution of the GRF in a certain future time instant. A gradient-based heuristic solution is developed, which could significantly speed up the computation of the optimal control. Mathematical analysis is also conducted to show the validity of the heuristic solution. Multiple collision risk levels are designed to improve the collision avoidance performance of robots in dynamic environments. The proposed heuristic predictive control is evaluated comprehensively from multiple perspectives based on different metrics in a challenging simulation environment. The competence of the proposed algorithm is validated via the comparison with the non-heuristic predictive control and two existing popular flocking control methods. Real-life experiments are also performed using four quadrotor UAVs to further demonstrate the efficiency of the proposed design.

Multi-Robot Motion Planning: A Learning-Based Artificial Potential Field Solution

Motion planning is a crucial aspect of robot autonomy as it involves identifying a feasible motion path to a destination while taking into consideration various constraints, such as input, safety, and performance constraints, without violating either system or environment boundaries. This becomes particularly challenging when multiple robots run without communication, which compromises their real-time efficiency, safety, and performance. In this paper, we present a learning-based potential field algorithm that incorporates deep reinforcement learning into an artificial potential field (APF). Specifically, we introduce an observation embedding mechanism that pre-processes dynamic information about the environment and develop a soft wall-following rule to improve trajectory smoothness. Our method, while belonging to reactive planning, implicitly encodes environmental properties. Additionally, our approach can scale up to any number of robots and has demonstrated superior performance compared to APF and RL through numerical simulations. Finally, experiments are conducted to highlight the effectiveness of our proposed method.