Role-Adaptive Collaborative Formation Planning for Team of Quadruped Robots in Cluttered Environments

TL;DR

This paper presents a role-adaptive Leader-Follower-based formation planning and control framework for teams of quadruped robots operating in cluttered environments that integrates dynamic role assignment and partial goal planning, enabling flexible, collision-free navigation in complex scenarios.

Abstract

This paper presents a role-adaptive Leader-Follower-based formation planning and control framework for teams of quadruped robots operating in cluttered environments. Unlike conventional methods with fixed leaders or rigid formation roles, the proposed approach integrates dynamic role assignment and partial goal planning, enabling flexible, collision-free navigation in complex scenarios. Formation stability and inter-robot safety are ensured through a virtual spring-damper system coupled with a novel obstacle avoidance layer that adaptively adjusts each agent's velocity. A dynamic look-ahead reference generator further enhances flexibility, allowing temporary formation deformation to maneuver around obstacles while maintaining goal-directed motion. The Fast Marching Square (FM2) algorithm provides the global path for the leader and local paths for the followers as the planning backbone. The framework is validated through extensive simulations and real-world experiments with teams of quadruped robots. Results demonstrate smooth coordination, adaptive role switching, and robust formation maintenance in complex, unstructured environments. A video featuring the simulation and physical experiments along with their associated visualizations can be found at https://youtu.be/scq37Tua9W4.

Figures (10)

• Figure 1: Overview of the proposed framework. The current dynamically assigned leader, $r_3$, plans a global path to the final formation goal. The remaining robots are assigned partial goals $p_2, ... p_N$ and plan local paths to them. The formation is connected by virtual springs and dampers.
• Figure 2: Examples of base configurations and connection setups for formations with three or four robots.
• Figure 3: Path planning with the FM2 method. The binary occupancy grid (a) is inflated to occupancy grid $W_1$ (b), from which distance transform $D_1$ is calculated using FMM (c). $D_1$ is saturated at a safe distance, giving the velocity map $W_2$ (d). Applying FMM to $W_2$, produces the time of arrival map $D_2$ (e), from which the path is calculated using gradient descent (f).
• Figure 4: Illustration of the obstacle avoidance step. The blue circle is the robot's position. $\mathbf{v}$ denotes the velocity vector before the adjustment, and $\mathbf{v'}$ denotes the velocity vector after the adjustment. The adjustment factor in this case is approximately $\alpha=0.6$.
• Figure 5: Snapshots from the Gazebo simulation along with RViz visualization.