Efficient Trajectory Planning for High Speed Flight in Unknown Environments

Ryll, Markus; Ware, John; Carter, John; Roy, Nicholas

doi:10.1109/icra.2019.8793930

Cited by 59 publications

(53 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…algorithms have been proposed to achieve autonomous flight in previously unknown environments [14][15][16][17][18][19][20][21][22]. A taxonomy of prior works is presented in Figure S5 in the Supplementary Materials.…”

Section: Introductionmentioning

confidence: 99%

Learning high-speed flight in the wild

et al. 2021

View full text Add to dashboard Cite

Quadrotors are agile. Unlike most other machines, they can traverse extremely complex environments at high speeds. To date, only expert human pilots have been able to fully exploit their capabilities. Autonomous operation with onboard sensing and computation has been limited to low speeds. State-of-the-art methods generally separate the navigation problem into subtasks: sensing, mapping, and planning. Although this approach has proven successful at low speeds, the separation it builds upon can be problematic for high-speed navigation in cluttered environments. The subtasks are executed sequentially, leading to increased processing latency and a compounding of errors through the pipeline. Here we propose an end-to-end approach that can autonomously fly quadrotors through complex natural and human-made environments at high speeds, with purely onboard sensing and computation. The key principle is to directly map noisy sensory observations to collision-free trajectories in a receding-horizon fashion. This direct mapping drastically reduces processing latency and increases robustness to noisy and incomplete perception. The sensorimotor mapping is performed by a convolutional network that is trained exclusively in simulation via privileged learning: imitating an expert with access to privileged information. By simulating realistic sensor noise, our approach achieves zero-shot transfer from simulation to challenging real-world environments that were never experienced during training: dense forests, snow-covered terrain, derailed trains, and collapsed buildings. Our work demonstrates that end-to-end policies trained in simulation enable high-speed autonomous flight through challenging environments, outperforming traditional obstacle avoidance pipelines. CODE AND MULTIMEDIA MATERIALA video demonstrating our approach is available at https://youtu.be/ m89bNn6RFoQ. We publicly release code and training datasets at https://github.com/uzh-rpg/agile_autonomy.

show abstract

Section: Introductionmentioning

confidence: 99%

Learning high-speed flight in the wild

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Generating smooth trajectories is important for high-speed applications to avoid sudden changes in actuators’ accelerations and mechanical vibration problems [ 67 ]. Therefore, it can be seen from the literature that control structures II–III are commonly used for aggressive maneuvers, whether by combining path planning and trajectory generation, as in [ 59 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ], or by direct trajectory planning, as in [ 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 ].…”

Section: Navigation Techniquesmentioning

confidence: 99%

“…In contrast to optimization-based trajectory generation where dynamic constraints are considered in the optimization problem, motion primitives were considered as a simpler computationally efficient way to generate collision-free trajectories in 3D in some works such as [ 77 , 90 , 91 , 92 , 93 , 94 ]. Motion primitives offer a light-weight algebraic solution to the problem which can then be checked for dynamic constraints violation.…”

Section: Navigation Techniquesmentioning

confidence: 99%

“…Multi-rotors are the most popular UAV type for many civilian applications due to their simplicity in mechanical design and control. Therefore, there have been many recent developments in nonlinear control of multi-rotors enabling high-speed navigation [ 59 , 77 ], aggressive flights [ 165 , 166 , 167 ] and aerial manipulation [ 168 , 169 , 170 ].…”

Section: Uav Modeling and Controlmentioning

confidence: 99%

See 1 more Smart Citation

Towards Fully Autonomous UAVs: A Survey

Elmokadem

Savkin

2021

Sensors

View full text Add to dashboard Cite

Unmanned Aerial Vehicles have undergone rapid developments in recent decades. This has made them very popular for various military and civilian applications allowing us to reach places that were previously hard to reach in addition to saving time and lives. A highly desirable direction when developing unmanned aerial vehicles is towards achieving fully autonomous missions and performing their dedicated tasks with minimum human interaction. Thus, this paper provides a survey of some of the recent developments in the field of unmanned aerial vehicles related to safe autonomous navigation, which is a very critical component in the whole system. A great part of this paper focus on advanced methods capable of producing three-dimensional avoidance maneuvers and safe trajectories. Research challenges related to unmanned aerial vehicle development are also highlighted.

show abstract

“…Autonomous navigation of unmanned micro-aerial vehicles (MAVs) in an urban environment is a challenging problem and an active field of research [1]- [4]. An aerial vehicle, operating at a low altitude, in an unknown and unstructured environment has to simultaneously solve several problems including perception, self-localization, trajectory planning and control in order to ensure safe motion.…”

Section: Introductionmentioning

confidence: 99%

Semantic Trajectory Planning for Long-Distant Unmanned Aerial Vehicle Navigation in Urban Environments

Ryll

Ware

Carter

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

There has been a considerable amount of recent work on high-speed micro-aerial vehicle flight in unknown and unstructured environments. Generally these approaches either use active sensing or fly slowly enough to ensure a safe braking distance with the relatively short sensing range of passive sensors. The former generally requires carrying large and heavy LIDARs and the latter only allows flight far away from the dynamic limits of the vehicle. One of the significant challenges for high-speed flight is the computational demand of trajectory planning at sufficiently high rates and length scales required in outdoor environments. We tackle both problems in this work by leveraging semantic information derived from an RGB camera on-board the vehicle. We first describe how to use semantic information to increase the effective range of perception on certain environment classes. Second, we present a sparse representation of the environment that is sufficiently lightweight for long distance path planning. We show how our approach outperforms more traditional metric planners which seek the shortest path, demonstrate the semantic planner's capabilities in a set of simulated and excessive real-world autonomous quadrotor flights in an urban environment.

show abstract

Efficient Trajectory Planning for High Speed Flight in Unknown Environments

Cited by 59 publications

References 23 publications

Learning high-speed flight in the wild

Learning high-speed flight in the wild

Towards Fully Autonomous UAVs: A Survey

Semantic Trajectory Planning for Long-Distant Unmanned Aerial Vehicle Navigation in Urban Environments

Contact Info

Product

Resources

About