U rban search and rescue missions raise special requirements on robotic systems. Small aerial systems provide essential support to human task forces in situation assessment and surveillance. As external infrastructure for navigation and communication is usually not available, robotic systems must be able to operate autonomously. A limited payload of small aerial systems poses a great challenge to the system design. The optimal tradeoff between flight performance, sensors, and computing resources has to be found. Communication to external computers cannot be guaranteed; therefore, all
Fig. 1: The torque-controlled humanoid robot TORO and its development stages from 2010 (DLR Biped [1]) to 2014.Abstract-This paper gives an overview on the torquecontrolled humanoid robot TORO, which has evolved from the former DLR Biped. In particular, we describe its mechanical design and dimensioning, its sensors, electronics and computer hardware. Additionally, we give a short introduction to the walking and multi-contact balancing strategies used for TORO.
Micro air vehicles have become very popular in recent years. Autonomous navigation of such systems plays an important role in many industrial applications as well as in search‐and‐rescue scenarios. We present a quadrotor that performs autonomous navigation in complex indoor and outdoor environments. An operator selects target positions in the onboard map and the system autonomously plans an obstacle‐free path and flies to these locations. An onboard stereo camera and inertial measurement unit are the only sensors. The system is independent of external navigation aids such as GPS. No assumptions are made about the structure of the unknown environment. All navigation tasks are implemented onboard the system. A wireless connection is only used for sending images and a three‐dimensional (3D) map to the operator and to receive target locations. We discuss the hardware and software setup of the system in detail. Highlights of the implementation are the field‐programmable‐gate‐array‐based dense stereo matching of 0.5 Mpixel images at a rate of 14.6 Hz using semiglobal matching, locally drift‐free visual odometry with key frames, and sensor data fusion with compensation of measurement delays of 220 ms. We show the robustness of the approach in simulations and experiments with ground truth. We present the results of a complex, autonomous indoor/outdoor flight and the exploration of a coal mine with obstacle avoidance and 3D mapping.
Joint simultaneous localization and mapping (SLAM) constitutes the basis for cooperative action in multi-robot teams. We designed a stereo vision-based 6D SLAM system combining local and global methods to benefit from their particular advantages: (1) Decoupled local reference filters on each robot for real-time, longterm stable state estimation required for stabilization, control and fast obstacle avoidance; (2) Online graph optimization with a novel graph topology and intra-as well as inter-robot loop closures through an improved submap matching method to provide global multi-robot pose and map estimates; (3) Distribution of the processing of high-frequency and high-bandwidth measurements enabling the exchange of aggregated and thus compacted map data. As a result, we gain robustness with respect to communication losses between robots. We evaluated our improved map matcher on simulated and real-world datasets and present our full system in five realworld multi-robot experiments in areas of up 3,000 m 2 (bounding box), including visual robot detections and submap matches as loop-closure constraints. Further, we demonstrate its application to autonomous multi-robot exploration in a challenging rough-terrain environment at a Moon-analogue site located on a volcano. K E Y W O R D S graph SLAM, map matching, mobile robots, multi-robot, navigation filter 1 | INTRODUCTION The exploration of moons and foreign planets is an important current and future application for mobile robots as their surfaces are difficult to reach and hard to access for humans. The application of huge and complex robot systems such as Curiosity, landed on Mars in 2012, creates many single points of failure for a mission. As a consequence, these rovers have to move very slowly and carefully to avoid getting stuck, as the Mars rover Spirit did in 2009 (Wolchover, 2011). The future deployment of teams of multiple robots can avoid these single points of failure by gaining robustness through redundancy and, in addition, can improve efficiency through parallelization. The robots have to travel through previously unknown unstructured rough terrain, operating in areas where external methods for localization like global navigation satellite systems (GNSS) are not available or expensive to set up. Communication links to the robots are limited and heavily delayed, featuring for example 8-40 min round trip time between Earth andMars. Furthermore, communication between the robots cannot be guaranteed at all times, in particular at scientifically interesting places such as craters, canyons, or caves. As teleoperation therefore becomes inefficient or infeasible, robot autonomy is a key aspect for future planetary exploration missions. Any coordinated (semi-)autonomous operation in such challenging environments requires up-to-date localization estimates for all robots in a team as well as a joint map to operate on.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.