Reactive Task Allocation and Planning for Quadrupedal and Wheeled Robot Teaming
This addresses the challenge of robust multi-robot coordination in real-world settings like hospitals, though it appears incremental by building on existing hierarchical planning methods.
The paper tackles the problem of reactive task allocation and planning for heterogeneous robot teams under disturbances, proposing local and global reallocation strategies that guarantee task completion without full replanning, as demonstrated in dynamic simulations in a hospital environment.
This paper takes the first step towards a reactive, hierarchical multi-robot task allocation and planning framework given a global Linear Temporal Logic specification. The capabilities of both quadrupedal and wheeled robots are leveraged via a heterogeneous team to accomplish a variety of navigation and delivery tasks. However, when deployed in the real world, all robots can be susceptible to different types of disturbances, including but not limited to locomotion failures, human interventions, and obstructions from the environment. To address these disturbances, we propose task-level local and global reallocation strategies to efficiently generate updated action-state sequences online while guaranteeing the completion of the original task. These task reallocation approaches eliminate reconstructing the entire plan or resynthesizing a new task. To integrate the task planner with low-level inputs, a Behavior Tree execution layer monitors different types of disturbances and employs the reallocation methods to make corresponding recovery strategies. To evaluate this planning framework, dynamic simulations are conducted in a realistic hospital environment with a heterogeneous robot team consisting of quadrupeds and wheeled robots for delivery tasks.