Intermittent Rendezvous Plans with Mixed Integer Linear Program for Large-Scale Multi-Robot Exploration

TL;DR

The paper addresses coordinating large-scale multi-robot exploration under intermittent connectivity (MRE-CCIC) by formulating a linearized Mixed-Integer Linear Program (MILP) to generate optimal rendezvous plans and pairing it with a Rendezvous Tracking in Unknown Scenarios (RTUS) policy to follow them using realistic trajectories. The authors introduce a rendezvous allocation framework with matrices such as the binary rendezvous matrix $K$, the latest-available-allocation $L$, and the highest-end times $H$, jointly optimizing the plan to minimize the total work-done error $W_{err}$ and the processing-time dispersion $J_{err}$ under a horizon constraint $m_{assign}$. Key contributions include an open-source MILP plan generator, an open-source ROS-based framework for large-scale intermittent-connectivity exploration, and empirical validation in a Gazebo-based $65000$ m$^2$ environment showing timely rendezvous, reduced waiting times, and competitive exploration performance with interpretable mission tracking. The work advances human–robot collaboration in stealthy and unpredictable environments by enabling predictable rendezvous timing and information exchange despite uncertain exploration dynamics and communication limits.

Abstract

Multi-Robot Exploration (MRE) systems with communication constraints have proven efficient in accomplishing a variety of tasks, including search-and-rescue, stealth, and military operations. While some works focus on opportunistic approaches for efficiency, others concentrate on pre-planned trajectories or scheduling for increased interpretability. However, scheduling usually requires knowledge of the environment beforehand, which prevents its deployment in several domains due to related uncertainties (e.g., underwater exploration). In our previous work, we proposed an intermittent communications framework for MRE under communication constraints that uses scheduled rendezvous events to mitigate such limitations. However, the system was unable to generate optimal plans and had no mechanisms to follow the plan considering realistic trajectories, which is not suited for real-world deployments. In this work, we further investigate the problem by formulating the Multi-Robot Exploration with Communication Constraints and Intermittent Connectivity (MRE-CCIC) problem. We propose a Mixed-Integer Linear Program (MILP) formulation to generate rendezvous plans and a policy to follow them based on the Rendezvous Tracking for Unknown Scenarios (RTUS) mechanism. The RTUS is a simple rule to allow robots to follow the assigned plan, considering unknown conditions. Finally, we evaluated our method in a large-scale environment configured in Gazebo simulations. The results suggest that our method can follow the plan promptly and accomplish the task efficiently. We provide an open-source implementation of both the MILP plan generator and the large-scale MRE-CCIC.

Figures (9)

• Figure 1: Example of application in MRE with limited connectivity. Four robots performing an information gathering mission, trying to avoid being perceived by a Surveillance Tower. To achieve this, they rely on scheduled rendezvous to exchange information using a low-range communications network. In this snapshot, robots 1 and 2 meet in a sub-team rendezvous while robots 3 and 4 explore the area through exploration points. The scheduled rendezvous must occur promptly to allow a base of operations to predict how the mission unfolds, which fosters human-robot collaboration.
• Figure 2: Scheduled rendezvous plan represented by coupled exploration jobs. In this example, robots $1$ and $2$ must meet after 50 time steps exploring the environment, because they are coupled by the jobs marked as the $1^{st}$ schedule. We call these coupled jobs agreements between robots, and they are analogous to humans exploring a place and establishing their meeting points based on the information they gather.
• Figure 3: Large-scale environment we used to evaluate the performance of the RTUS and the optimal local planning.
• Figure 4: Rendezvous plan we generated for evaluation. It is comprised of $3$ robots and $5$ rendezvous encounters for an exploration mission of $30^{+5}_{-5}$ minutes. Each color represents a sub-team of robots. For example, robots $0$ and $1$ at minute $24$.
• Figure 5: Velocity profile of $3$ robots when executing the mission to help decide the expected velocity of the RTUS (Rendezvous Tracking in Unknown Scenarios). The Y axis represents the velocities in m/s. The X axis shows the mission time in seconds.