Time-optimal planning for quadrotor waypoint flight

Foehn, Philipp; Romero, Angel; Scaramuzza, Davide

doi:10.1126/scirobotics.abh1221

Cited by 149 publications

(177 citation statements)

References 38 publications

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“…In maneuver or maneuver primitives, a drone is required to complete a certain position and attitude change in the shortest time; inspired by [24,[31][32][33][34][35], we formulated this problem as a keyframe-based time optimization problem. A schematic diagram of the problem is shown in Figure 5.…”

Section: Maneuver Optimizationmentioning

confidence: 99%

“…[33] emphasizes the Bang-Bang characteristics of the minimum time trajectory and compares the existing timeoptimal approaches, and through analysis, it is concluded that the polynomial method can only obtain non-optimal trajectories. On this basis, Foehn proposes complementary constraints [34] and solved both the time allocation and time-optimal trajectory planning problem elaborately. Through comparison and verification, it can be concluded that the trajectory designed by the algorithm is faster than that of professional pilots.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid-Driven Optimization Framework for Fixed-Wing UAV Maneuvering Flight Planning

et al. 2021

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Performing autonomous maneuvering flight planning and optimization remains a challenge for unmanned aerial vehicles (UAVs), especially for fixed-wing UAVs due to its high maneuverability and model complexity. A novel hybrid-driven fixed-wing UAV maneuver optimization framework, inspired by apprenticeship learning and nonlinear programing approaches, is proposed in this paper. The work consists of two main aspects: (1) Identifying the model parameters for a certain fixed-wing UAV based on the demonstrated flight data performed by human pilot. Then, the features of the maneuvers can be described by the positional/attitude/compound key-frames. Eventually, each of the maneuvers can be decomposed into several motion primitives. (2) Formulating the maneuver planning issue into a minimum-time optimization problem, a novel nonlinear programming algorithm was developed, which was unnecessary to determine the exact time for the UAV to pass by the key-frames. The simulation results illustrate the effectiveness of the proposed framework in several scenarios, as both the preservation of geometric features and the minimization of maneuver times were ensured.

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Section: Maneuver Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid-Driven Optimization Framework for Fixed-Wing UAV Maneuvering Flight Planning

et al. 2021

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“…The recent work by Foehn et al [13] proposes a timeoptimal trajectory planning for a quadrotor flying through a sequence of points. A minimum time trajectory is computed given the maximum propulsion of each motor.…”

Section: Trajectory Planningmentioning

confidence: 99%

“…The nine first elements of the function f (x, u, Δt) are based on the movement of a rigid body. The bias states do not change on the propagation, only on the correction; thus, we have that f (x, u, Δt) is given by the vector: (13) in which J r ≡ J r φ, θ is the Jacobian matrix that transforms the angular velocities u ω in the derivatives of the Euler angles r. Note that the measurements u ω and u a are corrected with the bias states of the IMU. It is important to emphasize that the term u ω × vb is the Coriolis compensation and ẑ = [0, 0, 1] T .…”

Section: State Estimationmentioning

confidence: 99%

An integrated solution for an autonomous drone racing in indoor environments

Rezende

Miranda

Rezeck

et al. 2021

Intel Serv Robotics

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The use of drones is becoming more present in modern daily life. One of the most challenging tasks associated with these vehicles is the development of perception and autonomous navigation systems. Competitions such as Artificial Intelligence Robotic Racing (AIRR) and Autonomous Drone Racing were launched to drive the advances in such strategies, requiring the development of integrated systems for autonomous navigation in a racing environment. In this context, this paper presents an improved integrated solution for autonomous drone racing, which focuses on simple, robust, and computationally efficient techniques to enable the application in embedded hardware. The strategy is divided into four modules: (i) A trajectory planner computes a path that passes through the sequence of desired gates; (ii) a perception system that obtains the global pose of the vehicle by using an onboard camera; (iii) a localization system which merges several sensed information to estimate the drone's states; and, (iv) an artificial vector field-based controller in charge of following the plan by using the estimated states. To evaluate the performance of the entire integrated system, we extended the FlightGoggles simulator to use gates similar to those used in the AIRR competition. A computational cost analysis demonstrates the high performance of the proposed perception method running on limited hardware commonly embedded in aerial robots. In addition, we evaluate the localization system by comparing it with ground truth and when there is a disturbance in the location of the gates. Results in a representative race circuit based on the AIRR competition showed the proposed strategy's competing performance, presenting itself as a feasible solution for drone racing systems.

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“…The two leading approaches are model-based and learning-based system design. The model-based approach follows a classical sense-plan-control scheme, which is modular, and requires very accurate knowledge about the drone dynamics, the drone’s state, and the ability to perform low-latency minimum-time control onboard [ 8 , 12 , 15 ]. Indeed, this approach has been very successful and has been able to outperform experienced drone racing pilots on challenging race maneuvers in highly controlled environments [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Visual attention prediction improves performance of autonomous drone racing agents

et al. 2022

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Humans race drones faster than neural networks trained for end-to-end autonomous flight. This may be related to the ability of human pilots to select task-relevant visual information effectively. This work investigates whether neural networks capable of imitating human eye gaze behavior and attention can improve neural networks’ performance for the challenging task of vision-based autonomous drone racing. We hypothesize that gaze-based attention prediction can be an efficient mechanism for visual information selection and decision making in a simulator-based drone racing task. We test this hypothesis using eye gaze and flight trajectory data from 18 human drone pilots to train a visual attention prediction model. We then use this visual attention prediction model to train an end-to-end controller for vision-based autonomous drone racing using imitation learning. We compare the drone racing performance of the attention-prediction controller to those using raw image inputs and image-based abstractions (i.e., feature tracks). Comparing success rates for completing a challenging race track by autonomous flight, our results show that the attention-prediction based controller (88% success rate) outperforms the RGB-image (61% success rate) and feature-tracks (55% success rate) controller baselines. Furthermore, visual attention-prediction and feature-track based models showed better generalization performance than image-based models when evaluated on hold-out reference trajectories. Our results demonstrate that human visual attention prediction improves the performance of autonomous vision-based drone racing agents and provides an essential step towards vision-based, fast, and agile autonomous flight that eventually can reach and even exceed human performances.

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Time-optimal planning for quadrotor waypoint flight

Cited by 149 publications

References 38 publications

A Hybrid-Driven Optimization Framework for Fixed-Wing UAV Maneuvering Flight Planning

A Hybrid-Driven Optimization Framework for Fixed-Wing UAV Maneuvering Flight Planning

An integrated solution for an autonomous drone racing in indoor environments

Visual attention prediction improves performance of autonomous drone racing agents

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