The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments
Open accessAbstract
We explore the challenges of planning trajectories for quadrotors through cluttered indoor environments. We extend the existing work on polynomial trajectory generation by presenting a method of jointly optimizing polynomial path segments in an unconstrained quadratic program that is numerically stable for high-order polynomials and large numbers of segments, and is easily formulated for efficient sparse computation. We also present a technique for automatically selecting the amount of time allocated to each segment, and hence the quadrotor speeds along the path, as a function of a single parameter determining aggressiveness, subject to actuator constraints. The use of polynomial trajectories, coupled with the differentially flat representation of the quadrotor, eliminates the need for computationally intensive sampling and simulation in the high dimensional state space of the vehicle during motion planning. Our approach generates high-quality trajecrtories much faster than purely sampling-based optimal kinodynamic planning methods, but sacrifices the guarantee of asymptotic convergence to the global optimum that those methods provide. We demonstrate the performance of our algorithm by efficiently generating trajectories through challenging indoor spaces and successfully traversing them at speeds up to 8 m/s. A demonstration of our algorithm and flight performance is available at: http://groups.csail.mit.edu/rrg/quad_polynomial_trajectory_planning.
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Cites
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Probabilistic roadmaps for path planning in high-dimensional configuration spaces
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“Randomized algorithms such as PRM, RRT and RRT* have enjoyed success due to their simplicity and performance in high-dimensional spaces [12, 14, 16].”
BackgroundRandomized Kinodynamic Planning
LaValle, S. M., & Kuffner, J. J. (2001) · The International Journal of Robotics Research · doi:10.1177/02783640122067453 · Cited by 3,254
Survey of Numerical Methods for Trajectory Optimization
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Minimum snap trajectory generation and control for quadrotors
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Geometric tracking control of a quadrotor UAV on SE(3)
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Real-Time Motion Planning With Applications to Autonomous Urban Driving
Kuwata, Y., Karaman, S., Teo, J., Frazzoli, E., How, J. P., & Fiore, G. (2009) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2008.2012116 · Cited by 889
Incremental Sampling-based Algorithms for Optimal Motion Planning
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Autonomous Helicopter Aerobatics through Apprenticeship Learning
Abbeel, P., Coates, A., & Ng, A. Y. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910371999 · Cited by 584
Cited by
Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight
Zhou, B., Gao, F., Wang, L., Liu, C., & Shen, S. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2927938 · Cited by 747
Swarm of micro flying robots in the wild
Zhou, X., Wen, X., Wang, Z., Gao, Y., Li, H., Wang, Q., Yang, T., Lu, H., Cao, Y., Xu, C., et al. (2022) · Science Robotics · doi:10.1126/scirobotics.abm5954 · Cited by 657
Liu, S., Watterson, M., Mohta, K., Sun, K., Bhattacharya, S., Taylor, C. J., & Kumar, V. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2663526 · Cited by 555
Trajectory Planning for Quadrotor Swarms
Honig, W., Preiss, J. A., Kumar, T. K. S., Sukhatme, G. S., & Ayanian, N. (2018) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2853613 · Cited by 315
PaintCopter: An Autonomous UAV for Spray Painting on Three-Dimensional Surfaces
Vempati, A. S., Kamel, M., Stilinovic, N., Zhang, Q., Reusser, D., Sa, I., Nieto, J., Siegwart, R., & Beardsley, P. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2846278 · Cited by 60
Metadata
- Authors
- Richter, C., Bry, A., & Roy, N.
- Venue
- Springer Tracts in Advanced Robotics
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 649-666
- ISSN
- 1610-742X (ISSN-L)
- 1610-7438
- OpenAlex
- W2482392012
- Cited by
- 747
- References
- 19
- FWCI
- 111.10
- Type
- book chapter
- Language
- en
- Open access
- green
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