Team NimbRo at MBZIRC 2017: Autonomous valve stem turning using a wrench

Schwarz, Max; Droeschel, David; Lenz, Christian; Periyasamy, Arul Selvam; Puang, En Yen; Razlaw, Jan; Rodríguez, Diego; Schüller, Sebastian; Schreiber, Michael; Behnke, Sven

doi:10.1002/rob.21826

Cited by 19 publications

(16 citation statements)

References 23 publications

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“…We mainly developed our ground robot Bob to solve Challenge 2 (pick and place of brick-shaped objects) [21] based on our very successful UGV Mario, which won the MBZIRC 2017 competition [22]. We added a thermal camera, water storage, and pumps to solve the firefighting challenge.…”

Section: Ugv Solutionmentioning

confidence: 99%

Autonomous Fire Fighting with a UAV-UGV Team at MBZIRC 2020

Quenzel¹,

Splietker²,

Pavlichenko³

et al. 2021

Preprint

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Every day, burning buildings threaten the lives of occupants and first responders trying to save them. Quick action is of essence, but some areas might not be accessible or too dangerous to enter. Robotic systems have become a promising addition to firefighting, but at this stage, they are mostly manually controlled, which is error-prone and requires specially trained personal.We present two systems for autonomous firefighting from air and ground we developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. The systems use LiDAR for reliable localization within narrow, potentially GNSS-restricted environments while maneuvering close to obstacles. Measurements from LiDAR and thermal cameras are fused to track fires, while relative navigation ensures successful extinguishing.We analyze and discuss our successful participation during the MBZIRC 2020, present further experiments, and provide insights into our lessons learned from the competition. * : equal contribution.

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Section: Ugv Solutionmentioning

confidence: 99%

Autonomous Fire Fighting with a UAV-UGV Team at MBZIRC 2020

Quenzel¹,

Splietker²,

Pavlichenko³

et al. 2021

Preprint

Self Cite

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“…We build our ground robot Bob based on our very successful UGV Mario, which won the first MBZIRC competition (Schwarz et al, 2019). We improved the basis slightly and adapted the manipulator and sensors for the new challenges.…”

Section: Hardware Designmentioning

confidence: 99%

Team NimbRo's UGV Solution for Autonomous Wall Building and Fire Fighting at MBZIRC 2020

Lenz¹,

Quenzel²,

Periyasamy³

et al. 2021

Preprint

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Autonomous robotic systems for various applications including transport, mobile manipulation, and disaster response are becoming more and more complex. Evaluating and analyzing such systems is challenging. Robotic competitions are designed to benchmark complete robotic systems on complex state-of-the-art tasks. Participants compete in defined scenarios under equal conditions. We present our UGV solution developed for the Mohamed Bin Zayed International Robotics Challenge 2020. Our hard-and software components to address the challenge tasks of wall building and fire fighting are integrated into a fully autonomous system. The robot consists of a wheeled omnidirectional base, a 6 DoF manipulator arm equipped with a magnetic gripper, a highly efficient storage system to transport box-shaped objects, and a water spraying system to fight fires. The robot perceives its environment using 3D LiDAR as well as RGB and thermal camera-based perception modules, is capable of picking box-shaped objects and constructing a pre-defined wall structure, as well as detecting and localizing heat sources in order to extinguish potential fires. A high-level planner solves the challenge tasks using the robot skills. We analyze and discuss our successful participation during the MBZIRC 2020 finals, present further experiments, and provide insights to our lessons learned.

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“…These challenges are located in complex unstructured and dangerous environment such as disaster rescuing, and need the robots to perform a series of complex tasks combining perception, manipulation, localization and even multi-robot cooperation. The robots should have manipulation ability with a high-level requirement [10,11]. Houseroom is another common application situation, which is unstructured and hard for robots to do the housework.…”

Section: Mobile Manipulationmentioning

confidence: 99%

Learning Mobile Manipulation through Deep Reinforcement Learning

Wang

Zhang

Tian

et al. 2020

Sensors

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Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that deep reinforcement learning is a powerful technique for fixed-base manipulation tasks, most of them are not applicable to mobile manipulation. This paper investigates how to leverage deep reinforcement learning to tackle whole-body mobile manipulation tasks in unstructured environments using only on-board sensors. A novel mobile manipulation system which integrates the state-of-the-art deep reinforcement learning algorithms with visual perception is proposed. It has an efficient framework decoupling visual perception from the deep reinforcement learning control, which enables its generalization from simulation training to real-world testing. Extensive simulation and experiment results show that the proposed mobile manipulation system is able to grasp different types of objects autonomously in various simulation and real-world scenarios, verifying the effectiveness of the proposed mobile manipulation system.

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Team NimbRo at MBZIRC 2017: Autonomous valve stem turning using a wrench

Cited by 19 publications

References 23 publications

Autonomous Fire Fighting with a UAV-UGV Team at MBZIRC 2020

Autonomous Fire Fighting with a UAV-UGV Team at MBZIRC 2020

Team NimbRo's UGV Solution for Autonomous Wall Building and Fire Fighting at MBZIRC 2020

Learning Mobile Manipulation through Deep Reinforcement Learning

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