Unmanned Ground Robots for Rescue Tasks

Berns, Karsten; Nezhadfard, Atabak; Tosa, Massimo; Balta, Haris; Cubber, Geert De

doi:10.5772/intechopen.69491

Cited by 12 publications

(6 citation statements)

References 9 publications

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“…Two complimentary examples of ground robots for SAR operations are introduced in [41], where both large and small robots are described. Ground robots for SAR missions can be characterized among those with dexterous manipulation capabilities and robust mobility on uneven terrain, such as the robot developed within the CENTAURO project [14], smaller robots with the ability of moving through tight spaces [41], or serpentine-like robots able of tethered operation across complex environments [42]. The recent DARPA SubT challenge has seen the design and deployment of flexible and robust ground units able of long-term autonomy and carry aerial units.…”

Section: ) Ground Robotsmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: ) Ground Robotsmentioning

confidence: 99%

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

et al. 2020

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Section: A System Requirements and Equipment Usedmentioning

confidence: 99%

“…Two complimentary examples of ground robots for SAR operations are introduced in [31], where both large and small robots are described. Ground robots for SAR missions can be characterized among those with dexterous manipulation capabilities and robust mobility on uneven terrain, such as the robot developed within the CENTAURO project [16], smaller robots with the ability of moving through tight spaces [31], or serpentine-like robots able of tethered operation across complex environments [32]. Some typical sensors in ground robots for SAR operations, as described in [31], are inertial and GNSS sensors, RGB-D cameras, infrared and thermal cameras, laser scanners, gas discrimination sensors, and microphones and speakers to offer a communication channel between SAR personnel and victims.…”

Section: A System Requirements and Equipment Usedmentioning

confidence: 99%

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

Queralta,

Taipalmaa,

Pullinen

et al. 2020

Preprint

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Autonomous or teleoperated robots have been playing increasingly important roles in civil applications in recent years. Across the different civil domains where robots can support human operators, one of the areas where they can have more impact is in search and rescue (SAR) operations. In particular, multi-robot systems have the potential to significantly improve the efficiency of SAR personnel with faster search of victims, initial assessment and mapping of the environment, real-time monitoring and surveillance of SAR operations, or establishing emergency communication networks, among other possibilities. SAR operations encompass a wide variety of environments and situations, and therefore heterogeneous and collaborative multirobot systems can provide the most advantages. In this paper, we review and analyze the existing approaches to multi-robot SAR support, from an algorithmic perspective and putting an emphasis on the methods enabling collaboration among the robots as well as advanced perception through machine vision and multi-agent active perception. Furthermore, we put these algorithms 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). This is, to the best of our knowledge, the first review considering heterogeneous SAR robots across different environments, while giving two complimentary points of view: control mechanisms and machine perception. Based on our review of the state-of-the-art, we discuss the main open research questions, and outline our insights on the current approaches that have potential to improve the real-world performance of multi-robot SAR systems.

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“…An unmanned ground vehicle (UGV) can be defined as a land-based vehicle that is capable of intelligent motion and action without human input [1]. UGVs can be used in a huge number of applications such as path tracking [2], storage [3], surveillance [4], transportation [5], in unstructured environments [6] (e.g., search and rescue operations [7], planetary exploration [8], agricultural works [9]), and so on. In our work, a path tracking application is developed, using a two-wheeled differential drive robot as UGV.…”

Section: Introductionmentioning

confidence: 99%

Dual-Rate Extended Kalman Filter Based Path-Following Motion Control for an Unmanned Ground Vehicle: Realistic Simulation

Carbonell

Cuenca

Casanova

et al. 2021

Sensors

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In this paper, a two-wheel drive unmanned ground vehicle (UGV) path-following motion control is proposed. The UGV is equipped with encoders to sense angular velocities and a beacon system which provides position and orientation data. Whereas velocities can be sampled at a fast rate, position and orientation can only be sensed at a slower rate. Designing a dynamic controller at this slower rate implies not reaching the desired control requirements, and hence, the UGV is not able to follow the predefined path. The use of dual-rate extended Kalman filtering techniques enables the estimation of the fast-rate non-available position and orientation measurements. As a result, a fast-rate dynamic controller can be designed, which is provided with the fast-rate estimates to generate the control signal. The fast-rate controller is able to achieve a satisfactory path following, outperforming the slow-rate counterpart. Additionally, the dual-rate extended Kalman filter (DREKF) is fit for dealing with non-linear dynamics of the vehicle and possible Gaussian-like modeling and measurement uncertainties. A Simscape Multibody™ (Matlab®/Simulink) model has been developed for a realistic simulation, considering the contact forces between the wheels and the ground, not included in the kinematic and dynamic UGV representation. Non-linear behavior of the motors and limited resolution of the encoders have also been included in the model for a more accurate simulation of the real vehicle. The simulation model has been experimentally validated from the real process. Simulation results reveal the benefits of the control solution.

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Unmanned Ground Robots for Rescue Tasks

Cited by 12 publications

References 9 publications

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

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

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

Dual-Rate Extended Kalman Filter Based Path-Following Motion Control for an Unmanned Ground Vehicle: Realistic Simulation

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