Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims’ conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area.

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

Application of robotics in offshore oil and gas industry- A review Part II

The emergence of the Internet of things and the widespread deployment of diverse computing systems have led to the formation of heterogeneous multi-agent systems (MAS) to complete a variety of tasks. Motivated to highlight the state of the art on existing MAS while identifying their limitations, remaining challenges, and possible future directions, we survey recent contributions to the field. We focus on robot agents and emphasize the challenges of MAS sub-fields including task decomposition, coalition formation, task allocation, perception, and multi-agent planning and control. While some components have seen more advancements than others, more research is required before effective autonomous MAS can be deployed in real smart city settings that are less restrictive than the assumed validation environments of MAS. Specifically, more autonomous end-to-end solutions need to be experimentally tested and developed while incorporating natural language ontology and dictionaries to automate complex task decomposition and leveraging big data advancements to improve perception algorithms for robotics.

Cooperative Heterogeneous Multi-Robot Systems: A Survey

This research teams an Unmanned Surface Vehicle (USV) with an Unmanned Aerial Vehicle (UAV) to augment and automate marine mass casualty incident search and rescue in emergency response phase. The demand for real-time responsiveness of those missions requires fast and comprehensive situational awareness and precise operations, which are challenging to achieve because of the large area and the flat nature of the water field. The responders, drowning victims, and rescue vehicle are far apart and all located at the sea level. The long distances mean responders cannot clearly discern the rescue vehicle and victims from the surrounding water. Furthermore, being at the same elevation makes depth perception difficult. Rescue vehicle and victims at different distances from the responder will always appear to be close together. This makes it almost impossible for the responders to accurately drive the USV to the victims in time. This paper proposes the use of a UAV to compensate for the lack of elevation of the responders and to automate search and rescue operations. The benefit of this system is two fold: 1) the UAV provides responders with an overhead view of the field, covers larger area than direct visual, and allows more accurate perception of the situation, and 2) it automates the rescue process so that the responders can focus on task-level needs instead of tediously driving the USV to the victims. Thirty autonomous navigation trials in 4 rescue scenarios prove the first known successful implementation of a small UAV visually navigating a USV.

UAV assisted USV visual navigation for marine mass casualty incident response

In this paper, for a marine aerial-surface heterogeneous (MASH) system composed by a quadrotor unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) with heterogeneity, completely unknown dynamics and disturbances, the accurate trajectory-tracking problem is solved by creating a novel coordinated trajectory tracking control (CTTC) scheme. A family of coordinate transformations are devised to convert the MASH system tracking error dynamics into translation-rotation cascade manners whereby the heterogeneity is removed and finite-time observers for complex unknowns are facilitated. In conjunction with sliding mode-based rotation error dynamics, distributed tracking controllers for the quadrotor UAV and the USV are independently synthesized such that cascade tracking error dynamics are globally asymptotically stable. With the aid of cascade and Lyapunov analysis, the entire CTTC solution to the accurate trajectory-tracking problem of the MASH system is eventually put forward. Simulation results and comprehensive comparisons on a prototype MASH system demonstrate the effectiveness and superiority of the proposed CTTC scheme.

Coordinated Trajectory Tracking Control of a Marine Aerial-Surface Heterogeneous System

This field report describes two deployments of heterogeneous unmanned marine vehicle teams at the 2011 Great Eastern Japan Earthquake response and recovery by the Center for Robot-Assisted Search and Rescue (USA) in collaboration with the International Rescue System Institute (Japan). Four remotely operated underwater vehicles (ROVs) were fielded in Minamisanriku and Rikuzentakata from April 18 to 24, 2011, for port clearing and victim recovery missions using sonar and video. The ROVs were used for multirobot operations only 46% of the time due to logistics. The teleoperated ROVs functioned as a dependent team 86% of the time to avoid sensor interference or collisions. The deployment successfully reopened the Minamisanriku New Port area and searched areas prohibited to divers in Rikuzentakata. The IRS-CRASAR team planned to return from October 18 to 28, 2011, with an unmanned aerial vehicle (UAV), an autonomous underwater vehicle (AUV), and an ROV to conduct debris mapping for environmental remediation missions. The intent was to investigate an interdependent strategy by which the UAV and AUV would rapidly conduct low-resolution scans identifying areas of interest for further investigation by the ROV. The UAV and AUV could not be used; however, the ROV was able to cover 80,000 m2 in 6 h, finding submerged wreckage and pollutants in areas previously marked clear by divers. The field work (i) showed that the actual and planned multirobot system configurations did not fall neatly into traditional taxonomies, (ii) identified a new measure, namely perceptual confidence, and (iii) posed five open research questions for multirobot systems operating in littoral regions. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Marine heterogeneous multirobot systems at the great Eastern Japan Tsunami recovery

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Marine heterogeneous multirobot systems at the great Eastern Japan Tsunami recovery