3D multi-robot patrolling with a two-level coordination strategy

Abstract

Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and realworld experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks.

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

“…In terms of human-robot collaboration for SAR operations, one of the first EU funded projects in SAR robotics, PeLoTe [19]- [21], designed mobile robots for SAR missions and developed a heterogeneous telematic system for cooperative (human-robot) SAR operations. Other international projects designing and developing autonomous multi-robot systems for SAR operations include the NIFTi EU project (natural human-robot cooperation in dynamic environments) [16], ICARUS (unmanned SAR) [18], [22], TRADR (long-term human-robot teaming for disaster response) [17], [23], [24], or SmokeBot (mobile robots with novel environmental sensors for inspection of disaster sites with low visibility) [25], [26]. Other projects, such as CEN-TAURO (robust mobility and dexterous manipulation in disaster response by fullbody telepresence in a centaur-like robot), have focused on the development of more advanced robots that are not fully autonomous but controlled in realtime [14].…”

“…The focus of the project was mostly on human-robot interaction and on distributing information for human operators at different layers. Similarly, in the TRADR project, the focus was on collaborative efforts towards disaster response of both humans and robots [17], and on multi-robot planning [23], [24]. In particular, the results of TRARD include a framework for the integration of UAVs in SAR missions, from path planning to a global 3D point cloud generator [28].…”

Scalable Multi-Robot System for Non-myopic Spatial Sampling

“…Yan et al , in their survey, classify the communication structure based on the information transfer modes, namely explicit and implicit communication [13]. Explicit communication allows direct exchange of information between the robots [14], [15], whereas in implicit communication, robots get information about their fellow robots through the environment [16]. In this paper, we explore explicit and asynchronous communication between the robots with a constraint on the communication range.…”

Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception

“…ImPACT-TRC aerial vehicles) [17]. More recently, other international projects designing and developing autonomous multi-robot systems for SAR operations include the NIFTi EU project (natural humanrobot cooperation in dynamic environments) [18], ICARUS (unmanned SAR) [20], [21], TRADR (long-term human-robot teaming for disaster response) [19], [22], [23], or SmokeBot (mobile robots with novel environmental sensors for inspection of disaster sites with low visibility) [24], [25]. Other projects, such as CENTAURO (robust mobility and dexterous manipulation in disaster response by fullbody telepresence in a centaur-like robot), have focused on the development of more advanced robots that are not fully autonomous but controlled in real-time [16].…”

“…The focus of the project was mostly on human-robot interaction and on distributing information for human operators at different layers. Similarly, in the TRADR project, the focus was on collaborative efforts towards disaster response of both humans and robots [19], as well as on multi-robot planning [22], [23]. In particular, the results of TRARD include a framework for the integration of UAVs in SAR missions, from path planning to a global 3D point cloud generator [27].…”