Multirobot Data Gathering Under Buffer Constraints and Intermittent Communication

Abstract

We consider a team of heterogeneous robots which are deployed within a common workspace to gather different types of data. The robots have different roles due to different capabilities: some gather data from the workspace (source robots) and others receive data from source robots and upload them to a data center (relay robots). The data-gathering tasks are specified locally to each source robot as high-level Linear Temporal Logic (LTL) formulas, that capture the different types of data that need to be gathered at different regions of interest. All robots have a limited buffer to store the data. Thus the data gathered by source robots should be transferred to relay robots before their buffers overflow, respecting at the same time limited communication range for all robots. The main contribution of this work is a distributed motion coordination and intermittent communication scheme that guarantees the satisfaction of all local tasks, while obeying the above constraints. The robot motion and inter-robot communication are closely coupled and coordinated during run time by scheduling intermittent meeting events to facilitate the local plan execution. We present both numerical simulations and experimental studies to demonstrate the advantages of the proposed method over existing approaches that predominantly require all-time network connectivity. of data-gathering actions. The work in [34] considers a single robot transferring data between locations. The proposed approach minimizes the time interval between two consecutive data-uploading time instants. But it does not explicitly model the evolution of the robot's buffer or the inter-robot communication. Similar buffer constraints are considered in [35] for multi-robot frontier-based exploration. However locallyassigned data-gathering tasks described by LTL formulas are not considered there, nor are communication constraints. Another related area is temporal logic task planning under resource constraints. The work in [36] considers a global surveillance task performed by multiple aerial vehicles subject to battery charging constraints. The multi-vehicle routing problem considered in [37] proposes a solution based on Mixed-Integer Linear Programming (MILP), which can potentially be extended to include resource constraints.The main contribution of this work lies in the development of an online distributed framework that jointly controls local data-gathering tasks and data transfer communication events, so that the buffers at every robot never overflow. The proposed framework guarantees the satisfaction of all local tasks specified as LTL formulas, without imposing all-time connectivity on the communication network. The efficiency of the proposed framework compared to a centralized approach and two static approaches is demonstrated via numerical simulations and experimental studies. To the best of our knowledge, this is the first distributed data-gathering framework under intermittent communication that is also online. This work is built on preliminary results presented in...

Distributed State Estimation Using Intermittently Connected Robot Networks

“…Moreover, all-time connectivity constraints may prevent the robots from moving freely in their environment to fulfill their tasks, and instead favor motions that maintain a reliable communication network. Motivated by this fact, intermittent communication frameworks have recently been proposed [17], [41]- [47]. Specifically, [41] addresses a data-ferrying problem by designing paths for a single robot that connect two static sensor nodes while optimizing motion and communication variables.…”

“…When connectivity is recovered in [42] the whole network needs to be connected. To the contrary, [17], [43]- [47] do not require that the communication network is ever connected at once, but they ensure connectivity over time, infinitely often, as in the method proposed here. The key idea in [17], [43]- [46] is to divide the robots into smaller teams and require that communication events take place when the robots in every team meet at a common location in space.…”

“…Instead, we require that communication events take place when the robots in every team form a connected subnetwork somewhere in the continuous space, which introduces challenging connectivity constraints in the proposed planning problem for every team. Distributed intermittent communication controllers for multirobot data-gathering tasks is presented in [47] that allows information to flow intermittently only to the root/user. To the contrary, in our proposed method, information can flow intermittently between any pair of robots and possibly a user in a multi-hop fashion.…”

Distributed assignment with limited communication for multi-robot multi-target tracking

“…A number of algorithms have been designed to improve multi-robot coordination under limited bandwidth [14][15][16][17][18] and under communication range constraints [19][20][21]. This includes algorithms that enforce connectivity constraints [22,23], explicitly trigger when to communicate [24][25][26] and operate when connectivity is intermittent [27,28]. In this section, we focus on work that is most closely related to the SATA problem and local algorithms.…”

A framework of probabilistic 3D map fusion for collaborative robots

“…In DDF-SAM [13], a robust distributed communication framework is proposed, however solely provide simulations results under simplifying assumptions. In [113], the author proposed a distributed scheme for multiple data-gathering robots under limited communication and buffer size constraints. In [21], proposed an efficient and robust algorithm, an offboard ground station takes sensor data from all MAVs and fuses them into a detailed map.…”