Fast, autonomous flight in GPS‐denied and cluttered environments

Mohta, Kartik; Watterson, Michael; Mulgaonkar, Yash; Liu, Sikang; Qu, Chao; Makineni, Anurag; Saulnier, Kelsey; Sun, Ke; Zhu, Alex Zihao; Delmerico, Jeffrey A.; Karydis, Konstantinos; Atanasov, Nikolay; Loianno, Giuseppe; Scaramuzza, Davide; Daniilidis, Kostas; Taylor, Camillo J.

doi:10.1002/rob.21774

Cited by 162 publications

(137 citation statements)

References 55 publications

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“…There has been much research on developing autonomous quadrotor systems. Among these works, some focus on the perception functionality (Bry, Richter, Bachrach, & Roy, ; Faessler et al, ; S. Shen, Michael, & Kumar, ) and some present complete system results (Fang et al, ; Lin et al, ; Mohta et al, ). In this study, we propose a motion planning framework for the safe navigation of an autonomous quadrotor.…”

Section: Related Workmentioning

confidence: 99%

Flying on point clouds: Online trajectory generation and autonomous navigation for quadrotors in cluttered environments

Gao

et al. 2018

Journal of Field Robotics

128

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Micro aerial vehicles (MAVs), especially quadrotors, have been widely used in field applications, such as disaster response, field surveillance, and search‐and‐rescue. For accomplishing such missions in challenging environments, the capability of navigating with full autonomy while avoiding unexpected obstacles is the most crucial requirement. In this paper, we present a framework for online generating safe and dynamically feasible trajectories directly on the point cloud, which is the lowest‐level representation of range measurements and is applicable to different sensor types. We develop a quadrotor platform equipped with a three‐dimensional (3D) light detection and ranging (LiDAR) and an inertial measurement unit (IMU) for simultaneously estimating states of the vehicle and building point cloud maps of the environment. Based on the incrementally registered point clouds, we online generate and refine a flight corridor, which represents the free space that the trajectory of the quadrotor should lie in. We represent the trajectory as piecewise Bézier curves by using the Bernstein polynomial basis and formulate the trajectory generation problem as a convex program. By using Bézier curves, we can constrain the position and kinodynamics of the trajectory entirely within the flight corridor and given physical limits. The proposed approach is implemented to run onboard in real‐time and is integrated into an autonomous quadrotor platform. We demonstrate fully autonomous quadrotor flights in unknown, complex environments to validate the proposed method.

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Section: Related Workmentioning

confidence: 99%

Flying on point clouds: Online trajectory generation and autonomous navigation for quadrotors in cluttered environments

Gao

et al. 2018

Journal of Field Robotics

128

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“…Such techniques are typically computationally expensive, but advances in processor capabilities have allowed successful onboard implementations of more robust direct visual odometry methods (48). Hybrid algorithms that leverage the advantages of both approaches have also been successful (49,50). Vision data can be fused with IMU measurements using graph-based (51-54) or filtering-based approaches (55).…”

Section: State Estimation and Perceptionmentioning

confidence: 99%

“…However, maintaining a complete map for longer-range planning is computationally expensive, and as a result, most mapping algorithms have been executed using either offboard resources (50) or onboard specialized hardware, such as GPUs (53) or field-programmable gate arrays (69). These mapping algorithms can be combined with trajectory-generation methods to enable autonomous navigation through cluttered environments (49,53,68).…”

Section: State Estimation and Perceptionmentioning

confidence: 99%

“…Many works have successfully demonstrated autonomous navigation in unknown workspaces using completely onboard mapping, estimation, planning, and control pipelines (53,66,85,86,90) (49,85), which uses a stereo camera and IMU for estimation and a laser sensor for mapping, can reach speeds of 7 m/s. Figure 5 illustrates identified safe trajectories from a single start position to multiple goals throughout a space.…”

Section: Applications For Agile High-speed Flightmentioning

confidence: 99%

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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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“…In Mohta et al., , one co‐author's name was missed during the submission process. The authors’ list should read as:…”

mentioning

confidence: 99%