Role-Adaptive Collaborative Formation Planning for Team of Quadruped Robots in Cluttered Environments
Authors: Magnus Norén, Marios-Nektarios Stamatopoulos, Avijit Banerjee, George Nikolakopoulos
Organizations: Robotics and Artificial Intelligence Group, Department of Computer, Electrical and Space Engineering, Lulea University of Technology, 971 87 Lule˚a, Sweden
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 & tables
Figure 1: Overview of the proposed framework. The current dynamically assigned leader, r3 , plans a global path to the final formation goal. The remaining robots are assigned partial goals p2,...pN 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 FM 2 method. The binary occupancy grid (a) is inflated to occupancy grid W1 (b), from which distance transform D1 is calculated using FMM (c). D1 is saturated at a safe distance, giving the velocity map W2 (d). Applying FMM to W2 , produces the time of arrival map D2 (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. v denotes the velocity vector before the adjustment, and v′ denotes the velocity vector after the adjustment. The adjustment factor in this case is approximately α=0.6 .
Figure 5: Snapshots from the Gazebo simulation along with RViz visualization.
Figure 6: Simulation snapshot highlighting the ability to take different paths around smaller obstacles.
Figure 7: Snapshots from a lab experiment with an unstructured environment.
Figure 8: Snapshots from a lab experiment in which the formation is passing through a narrow corridor.
Figure 9: Distances between each robot and its closest obstacle for the narrow corridor lab experiment.
Figure 10: Distances between each pair of robots for the narrow corridor lab experiment.
GRASP Lab, University of Pennsylvania, Philadelphia, PA 19104, USA · Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA
National Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China · School of Artificial Intelligence, Nanjing University, Nanjing, China · Polixir Technologies, Nanjing, China