A swarm of flying smartphones

Loianno, Giuseppe; Mulgaonkar, Yash; Brunner, Chris; Ahuja, Dheeraj; Ramanandan, Arvind; Chari, Murali; Diaz, Serafin; Kumar, Vijay

doi:10.1109/iros.2016.7759270

Cited by 23 publications

(15 citation statements)

References 23 publications

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“…Bringing the same level of agility and autonomy to vehicles with consumer-grade electronics is difficult. Fortunately, the rapid development of sensor-rich and computationally powerful communication devices has opened up the potential for plug-and-play interfaces between smartphones, such as the Google Tango (129) and the Samsung Galaxy S5 (130), and off-the-self quadrotor frames. This would allow the development of algorithms on standardized sensor suites that are easily accessible to consumers.…”

Section: Discussionmentioning

confidence: 99%

Autonomous Flight

Tang

Kumar

2018

Annu. Rev. Control Robot. Auton. Syst.

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This review surveys the current state of the art in the development of unmanned aerial vehicles, focusing on algorithms for quadrotors. Tremendous progress has been made across both industry and academia, and full vehicle autonomy is now well within reach. We begin by presenting recent successes in control, estimation, and trajectory planning that have enabled agile, highspeed flight using low-cost onboard sensors. We then examine new research trends in learning and multirobot systems and conclude with a discussion of open challenges and directions for future research. 6.1

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Section: Discussionmentioning

confidence: 99%

Autonomous Flight

Tang

Kumar

2018

Annu. Rev. Control Robot. Auton. Syst.

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“…Forster et al () deployed their system on two UAVs flying in a small indoor environment, but as discussed in Section , no communication from the server to the agent takes place in this system. The multirobot planner in Loianno et al () was as well demonstrated during real flight of three small aircraft. However, the system works in a sequential manner: When receiving information from the server to calculate trajectories, the UAV agents hover on the spot.…”

Section: Related Workmentioning

confidence: 98%

“…Other works, such as Google's “Project Tango” (Google Inc., ) and the work of Guo et al (), do not directly target robotic application scenarios, but collect data with multiple handheld platforms such as tablets or mobile phones to build a common map with the input from these devices. The recent work of Loianno et al () demonstrates a system that combines both UAVs and mobile phones. Using multiple UAVs with off‐the‐shelf smartphones as computation units, they present a system for map‐building in a cooperative manner and trajectory planning.…”

Section: Related Workmentioning

confidence: 99%

“…Using multiple UAVs with off‐the‐shelf smartphones as computation units, they present a system for map‐building in a cooperative manner and trajectory planning. Once collaborative SLAM established a collective estimate of the environment and the relative localization of the agents, systems such as Loianno et al () and other approaches such as Chung and Slotine () and Kushleyev, Mellinger, Powers, and Kumar () can be used on top of it to guide the trajectories of the agents, even avoiding hazardous areas of the environment as demonstrated in Schwager, Dames, Rus, and Kumar ().…”

Section: Related Workmentioning

confidence: 99%

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CCM‐SLAM: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams

Schmuck

Chli

2018

Journal of Field Robotics

174

104

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Robotic collaboration promises increased robustness and efficiency of missions with great potential in applications, such as search‐and‐rescue and agriculture. Multiagent collaborative simultaneous localization and mapping (SLAM) is right at the core of enabling collaboration, such that each agent can colocalize in and build a map of the workspace. The key challenges at the heart of this problem, however, lie with robust communication, efficient data management, and effective sharing of information among the agents. To this end, here we present CCM‐SLAM, a centralized collaborative SLAM framework for robotic agents, each equipped with a monocular camera, a communication unit, and a small processing board. With each agent able to run visual odometry onboard, CCM‐SLAM ensures their autonomy as individuals, while a central server with potentially bigger computational capacity enables their collaboration by collecting all their experiences, merging and optimizing their maps, or disseminating information back to them, where appropriate. An in‐depth analysis on benchmarking datasets addresses the scalability and the robustness of CCM‐SLAM to information loss and communication delays commonly occurring during real missions. This reveals that in the worst case of communication loss, collaboration is affected, but not the autonomy of the agents. Finally, the practicality of the proposed framework is demonstrated with real flights of three small aircraft equipped with different sensors and computational capabilities onboard and a standard laptop as the server, collaboratively estimating their poses and the scene on the fly.

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“…In order to realize this advantage, however, the agents need to be able to control their relative positions. The simplest solution is to use an open-loop approach that relies on a global positioning systems or a local reconstruction of the enviroments [17], [37]. A complementary approach is the one of formation control, which relies exclusively on measurements between pairs of agents.…”

Section: Introductionmentioning

confidence: 99%