Search-based motion planning for quadrotors using linear quadratic minimum time control

Liu, Sikang; Atanasov, Nikolay; Mohta, Kartik; Kumar, Vijay

doi:10.1109/iros.2017.8206119

Cited by 203 publications

(216 citation statements)

References 23 publications

(33 reference statements)

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“…Since the resolution of voxel grids is a critical factor for the performance of our proposed method, different resolutions are used for comprehensive evaluation (Tab.I, column 1, rows 3-5). For a fair comparison, we use the open source implementation of [23]. Results are listed in Tab.I.…”

Section: ) Comparison Of Path Searchingmentioning

confidence: 99%

Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight

Zhou

Gao

Wang

et al. 2019

IEEE Robot. Autom. Lett.

438

292

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In this paper, we propose a robust and efficient quadrotor motion planning system for fast flight in 3-D complex environments. We adopt a kinodynamic path searching method to find a safe, kinodynamic feasible and minimumtime initial trajectory in the discretized control space. We improve the smoothness and clearance of the trajectory by a B-spline optimization, which incorporates gradient information from a Euclidean distance field (EDF) and dynamic constraints efficiently utilizing the convex hull property of B-spline. Finally, by representing the final trajectory as a non-uniform B-spline, an iterative time adjustment method is adopted to guarantee dynamically feasible and non-conservative trajectories. We validate our proposed method in various complex simulational environments. The competence of the method is also validated in challenging real-world tasks. We release our code as an open-source package 1 .

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Section: ) Comparison Of Path Searchingmentioning

confidence: 99%

Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight

Zhou

Gao

Wang

et al. 2019

IEEE Robot. Autom. Lett.

438

292

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“…However, the solution quality largely depends on the number of states pre-sampled. On the other hand, Liu et al [14] explore the search-based kinodynamic planning counterpart and develop efficient heuristics by solving a linear quadratic minimum time problem. Their solution is resolution-complete with respect to the discretization on the control input, and achieves near real-time performance.…”

Section: B Kinodynamic Motion Planningmentioning

confidence: 99%

“…However, the resultant trajectory only has limited continuity. To improve the smoothness, both [14] and [23] adopt trajectory reparameterization using the unconstrained QP formulation [3], which may break the dynamical feasibility and safety.…”

Section: B Kinodynamic Motion Planningmentioning

confidence: 99%

“…1 different from the traditional geometric path search: 1) the cost function f k,∆t (·) evaluates the control effort of the k-degree vertex tuple, instead of a simple path length measure; 2) the function INDEX(·) regards the k-degree vertex tuple as the "state", which expands the high-dimensional state space supported by the control point coordinates, while the geometric path search typically regards positions as states; and 3) the function FEASIBLESUCCS(·) will expand to the neighboring k-degree tuples with dynamical feasibility checking, while the geometric path search cannot check feasibility without parameterization. As for the heuristic function HEURISTIC(·), we adopt the admissible minimum time heuristic in [14].…”

Section: Optimal B-spline-based Kinodynamic Searchmentioning

confidence: 99%

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An Efficient B-Spline-Based Kinodynamic Replanning Framework for Quadrotors

et al. 2019

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Trajectory replanning for quadrotors is essential to enable fully autonomous flight in unknown environments. Hierarchical motion planning frameworks, which combine path planning with path parameterization, are popular due to their time efficiency. However, the path planning cannot properly deal with non-static initial states of the quadrotor, which may result in non-smooth or even dynamically infeasible trajectories. In this paper, we present an efficient kinodynamic replanning framework by exploiting the advantageous properties of the B-spline, which facilitates dealing with the non-static state and guarantees safety and dynamical feasibility. Our framework starts with an efficient B-spline-based kinodynamic (EBK) search algorithm which finds a feasible trajectory with minimum control effort and time. To compensate for the discretization induced by the EBK search, an elastic optimization (EO) approach is proposed to refine the control point placement to the optimal location. Systematic comparisons against the state-of-the-art are conducted to validate the performance. Comprehensive onboard experiments using two different vision-based quadrotors are carried out showing the general applicability of the framework.

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“…The path is then online searched by a graph search method, like anytime dynamic A* (AD*) (Likhachev, Ferguson, Gordon, Stentz, & Thrun 2005). Similarly, in Liu, Atanasov, Mohta, and Kumar (2017), motion primitives are online computed using a time-optimal LQR control policy, and a search graph is online expanded. Usually, the path found by the front-end cannot be directly executed by vehicles since it may be discontinuous or contain unnatural swerves.…”

Section: Path Findingmentioning

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

140

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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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Search-based motion planning for quadrotors using linear quadratic minimum time control

Cited by 203 publications

References 23 publications

Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight

Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight

An Efficient B-Spline-Based Kinodynamic Replanning Framework for Quadrotors

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

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