A Computationally Efficient Motion Primitive for Quadrocopter Trajectory Generation

Mueller, Mark W.; Hehn, Markus; D’Andrea, Raffaello

doi:10.1109/tro.2015.2479878

Cited by 323 publications

(251 citation statements)

References 27 publications

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“…The mapping L between low-rate x[dt] and high-rate y[dt ] is implemented by the following trajectory generator, adapted from [18]. It takes in a motion duration T f > 0 and a pair of position, velocity and acceleration tuples, called waypoints: an initial waypoint q 0 = (p 0 , v 0 , a 0 ) and a final waypoint q f = (p f , v f , a f ).…”

Section: A the Trajectory Generatormentioning

confidence: 99%

“…In our control architecture, the waypoints are the elements of the low-rate x computed by solving (3). The generator of [18] formulates the quadrotor dynamics in terms of 3D jerk and this allows a decoupling of the equations along three orthogonal jerk axes. By solving three independent optimal control problems, one along each axis, it obtains three minimum-jerk trajectories, each being a spline…”

Section: A the Trajectory Generatormentioning

confidence: 99%

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Fly-by-Logic: Control of Multi-Drone Fleets with Temporal Logic Objectives

Pant

Abbas

Quaye

et al. 2018

2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)

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The problem of safe planning and control for multi-drone systems across a variety of missions is of critical impor-tance, as the scope of tasks assigned to such systems increases. In this paper, we present an approach to solve this problem for multi-quadrotor missions. Given a mission expressed in Signal Temporal Logic (STL), our controller maximizes robustness to generate trajectories for the quadrotors that satisfy the STL specification in continuous-time. We also show that the constraints on our optimization guarantees that these trajectories can be tracked nearly perfectly by lower level off-the-shelf position and attitude controllers. Our approach avoids the oversimplifying abstractions found in many planning methods, while retaining the expressiveness of missions encoded in STL allowing us to handle complex spatial, temporal and reactive requirements. Through experiments, both in simulation and on actual quadrotors, we show the performance, scalability and real-time applicability of our method. KeywordsMulti-drone fleets, control, signal temporal logic, robustness maximization, quadrotors, crazyflie Disciplines Computer Engineering | Electrical and Computer EngineeringThis conference paper is available at ScholarlyCommons: https://repository.upenn.edu/mlab_papers/107 Abstract-The problem of safe planning and control for multidrone systems across a variety of missions is of critical importance, as the scope of tasks assigned to such systems increases. In this paper, we present an approach to solve this problem for multi-quadrotor missions. Given a mission expressed in Signal Temporal Logic (STL), our controller maximizes robustness to generate trajectories for the quadrotors that satisfy the STL specification in continuous-time. We also show that the constraints on our optimization guarantees that these trajectories can be tracked nearly perfectly by lower level off-the-shelf position and attitude controllers. Our approach avoids the oversimplifying abstractions found in many planning methods, while retaining the expressiveness of missions encoded in STL allowing us to handle complex spatial, temporal and reactive requirements. Through experiments, both in simulation and on actual quadrotors, we show the performance, scalability and real-time applicability of our method.

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Section: A the Trajectory Generatormentioning

confidence: 99%

Section: A the Trajectory Generatormentioning

confidence: 99%

Fly-by-Logic: Control of Multi-Drone Fleets with Temporal Logic Objectives

Pant

Abbas

Quaye

et al. 2018

2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)

View full text Add to dashboard Cite

show abstract

“…We use the approach proposed in [26] to plan optimal, feasible trajectories that prevent the vehicle from colliding with obstacles. The authors of that work propose a fast polynomial trajectory generation method that minimizes the third derivative of the position (namely, the jerk).…”

Section: Trajectory Planningmentioning

confidence: 99%

“…Additionally, during the platform following stage, we exploit the speed of the trajectory planning method [26] to provide the quadrotor with a set of feasible candidate trajectories, and we select the one with the lowest cost. Such a cost is the integral of the jerk along the trajectory, which the authors of [26] show to be an upper bound on the product of the inputs to the vehicle, namely the collective thrust and the angular velocities around the three body axes.…”

Section: Trajectory Planningmentioning

confidence: 99%

“…Such a cost is the integral of the jerk along the trajectory, which the authors of [26] show to be an upper bound on the product of the inputs to the vehicle, namely the collective thrust and the angular velocities around the three body axes. Also, this allows us to quickly replan the desired trajectory during the platform following phase (see Sec.…”

Section: Trajectory Planningmentioning

confidence: 99%

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Vision-based autonomous quadrotor landing on a moving platform

Falanga

Zanchettin

Simovic

et al. 2017

2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR)

159

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Abstract-We present a quadrotor system capable of autonomously landing on a moving platform using only onboard sensing and computing. We rely on state-of-the-art computer vision algorithms, multi-sensor fusion for localization of the robot, detection and motion estimation of the moving platform, and path planning for fully autonomous navigation. Our system does not require any external infrastructure, such as motioncapture systems. No prior information about the location of the moving landing target is needed. We validate our system in both synthetic and real-world experiments using low-cost and lightweight consumer hardware. To the best of our knowledge, this is the first demonstration of a fully autonomous quadrotor system capable of landing on a moving target, using only onboard sensing and computing, without relying on any external infrastructure. SUPPLEMENTARY MATERIALVideo of the experiments: https://youtu.be/ Tz5ubwoAfNE

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Collision Free Path Planning

2021

Autonomous Road Vehicle Path Planning and Tracking Control

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A Computationally Efficient Motion Primitive for Quadrocopter Trajectory Generation

Cited by 323 publications

References 27 publications

Fly-by-Logic: Control of Multi-Drone Fleets with Temporal Logic Objectives

Fly-by-Logic: Control of Multi-Drone Fleets with Temporal Logic Objectives

Vision-based autonomous quadrotor landing on a moving platform

Collision Free Path Planning

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