Trust-based Symbolic Motion Planning for Multi-robot Bounding Overwatch
This work addresses safe and reliable coordination for multi-robot teams in complex military or surveillance scenarios, representing an incremental improvement through integration of trust modeling.
The paper tackled the problem of coordinating multi-robot systems for bounding overwatch tasks under temporal logic constraints by developing a decentralized symbolic motion planning framework with a computational trust model, resulting in guaranteed correctness and reliability as demonstrated in ROS Gazebo simulations.
Multi-robot bounding overwatch requires timely coordination of robot team members. Symbolic motion planning (SMP) can provide provably correct solutions for robot motion planning with high-level temporal logic task requirements. This paper aims to develop a framework for safe and reliable SMP of multi-robot systems (MRS) to satisfy complex bounding overwatch tasks constrained by temporal logics. A decentralized SMP framework is first presented, which guarantees both correctness and parallel execution of the complex bounding overwatch tasks by the MRS. A computational trust model is then constructed by referring to the traversability and line of sight of robots in the terrain. The trust model predicts the trustworthiness of each robot team's potential behavior in executing a task plan. The most trustworthy task and motion plan is explored with a Dijkstra searching strategy to guarantee the reliability of MRS bounding overwatch. A robot simulation is implemented in ROS Gazebo to demonstrate the effectiveness of the proposed framework.