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Yuwei Wu

Bio: Yuwei Wu is an academic researcher from Zhejiang University. The author has contributed to research in topics: Computer science & Robust control. The author has an hindex of 1, co-authored 3 publications receiving 7 citations.

Papers
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TL;DR: This paper presents a method to efficiently generate large, free, and guaranteed convex space among arbitrarily cluttered obstacles, which significantly outperforms state-of-the-art works in efficiency and presents practical applications with the method in 3D.
Abstract: In this paper, we present a method to efficiently generate large, free, and guaranteed convex space among arbitrarily cluttered obstacles. Our method operates directly on point clouds, avoids expensive calculations, and processes thousands of points within a few milliseconds, which extremely suits embedded platforms. The base stone of our method is sphere flipping, a one-one invertible nonlinear transformation, which maps a set of unordered points to a nonlinear space. With these wrapped points, we obtain a collision-free star convex polytope. Then, utilizing the star convexity, we efficiently modify the polytope to convex and guarantee its free of obstacles. Extensive quantitative evaluations show that our method significantly outperforms state-of-the-art works in efficiency. We also present practical applications with our method in 3D, including large-scale deformable topological mapping and quadrotor optimal trajectory planning, to validate its capability and efficiency. The source code of our method will be released for the reference of the community.

14 citations

Journal ArticleDOI
08 Sep 2021
TL;DR: In this article, a front-end kinodynamic path is searched with force-biased motion primitives, and a nonlinear model predictive control (NMPC) is developed with Hamilton-Jacobi (HJ) forward reachability analysis for error dynamics caused by external forces.
Abstract: Adaptive autonomous navigation with no prior knowledge of extraneous disturbance is of great significance for quadrotors in a complex and unknown environment. The mainstream approach that considers external disturbance is to implement disturbance-rejected control and path tracking. However, the robust control to compensate for tracking deviations is not well-considered regarding energy consumption, and even the reference path will become risky and intractable with disturbance. As recent external forces estimation advances, it is possible to incorporate a real-time force estimator to develop more robust and safe planning frameworks. This letter proposes a systematic (re)planning framework that can resiliently generate safe trajectories under volatile conditions. Firstly, a front-end kinodynamic path is searched with force-biased motion primitives. Then we develop a nonlinear model predictive control (NMPC) as a local planner with Hamilton-Jacobi (HJ) forward reachability analysis for error dynamics caused by external forces. It guarantees collision avoidance by constraining the ellipsoid of the quadrotor body expanded with the forward reachable sets (FRSs) within safe convex polytopes. Our method is validated in simulations and real-world experiments with different sources of external forces.

13 citations

Journal ArticleDOI
TL;DR: This work proposes a versatile and real-time trajectory optimization method that can generate a high-quality feasible trajectory using a full vehicle model under arbitrary constraints, leveraging the differential property of car-like robots to simplify the trajectory planning problem.
Abstract: —As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, there is no efficient trajectory planning solution capable of spatial-temporal joint optimization due to nonholonomic dynamics, particularly in the presence of unstruc- tured environments and dynamic obstacles. To bridge the gap, we propose a versatile and real-time trajectory optimization method that can generate a high-quality feasible trajectory using a full vehicle model under arbitrary constraints. By leveraging the differential flatness property of car-like robots, we use flat outputs to analytically formulate all feasibility constraints to simplify the trajectory planning problem. Moreover, obstacle avoidance is achieved with full dimensional polygons to generate less conservative trajectories with safety guarantees, especially in tightly constrained spaces. We present comprehensive bench- marks with cutting-edge methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes as open-source packages with the purpose for the reference of the research community. 1

5 citations

Proceedings ArticleDOI
28 Mar 2022
TL;DR: This paper proposes a novel non-parametric Bayesian statistical method to infer an abstraction, consisting of an unknown number of geometric primitives, from a point cloud, and indicates that the method outperforms the state-of-the-art in terms of accuracy and is generalizable to various types of objects.
Abstract: 3D shape abstraction has drawn great interest over the years. Apart from low-level representations such as meshes and voxels, researchers also seek to semantically abstract complex objects with basic geometric primitives. Recent deep learning methods rely heavily on datasets, with limited generality to unseen categories. Furthermore, abstracting an object accurately yet with a small number of primitives still remains a challenge. In this paper, we propose a novel non-parametric Bayesian statistical method to infer an abstraction, consisting of an unknown number of geometric primitives, from a point cloud. We model the generation of points as observations sampled from an infinite mixture of Gaussian Superquadric Taper Models (GSTM). Our approach formulates the abstraction as a clustering problem, in which: 1) each point is assigned to a cluster via the Chinese Restaurant Process (CRP); 2) a primitive representation is optimized for each cluster, and 3) a merging post-process is incorporated to provide a concise representation. We conduct extensive experiments on two datasets. The results indicate that our method outperforms the state-of-the-art in terms of accuracy and is generalizable to various types of objects.

3 citations

Proceedings ArticleDOI
22 Sep 2022
TL;DR: An indoor exploration framework that uses learning to predict the occupancy of unseen areas, extracts semantic features, samples viewpoints to predict information gains for different exploration goals, and plans informative trajectories to enable safe and smart exploration is developed.
Abstract: We address the problem of efficient 3-D exploration in indoor environments for micro aerial vehicles with limited sensing capabilities and payload/power constraints. We develop an indoor exploration framework that uses learning to predict the occupancy of unseen areas, extracts semantic features, samples viewpoints to predict information gains for different exploration goals, and plans informative trajectories to enable safe and smart exploration. Extensive experimentation in simulated and real-world environments shows the proposed approach outperforms the state-of-the-art exploration framework by 24% in terms of the total path length in a structured indoor environment and with a higher success rate during exploration.

1 citations


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TL;DR: In this article, an optimization-based framework for multicopter trajectory planning subject to geometrical spatial constraints and user-defined dynamic constraints is presented, which is a novel trajectory representation built upon the novel optimality conditions for unconstrained control effort minimization.
Abstract: We present an optimization-based framework for multicopter trajectory planning subject to geometrical spatial constraints and user-defined dynamic constraints. The basis of the framework is a novel trajectory representation built upon our novel optimality conditions for unconstrained control effort minimization. We design linear-complexity operations on this representation to conduct spatial-temporal deformation under various planning requirements. Smooth maps are utilized to exactly eliminate geometrical constraints in a lightweight fashion. A wide range of state-input constraints are supported by the decoupling of dense constraint evaluation from sparse parameterization, and backward differentiation of flatness map. As a result, the proposed framework transforms a generally constrained multicopter planning problem into an unconstrained optimization that can be solved reliably and efficiently. Our framework bridges the gaps among solution quality, planning frequency and constraint fidelity for a multicopter with limited resources and maneuvering capability. Its generality and robustness are both demonstrated by applications and experiments for different tasks. Extensive simulations and benchmarks are also conducted to show its capability of generating high-quality solutions while retaining the computation speed against other specialized methods by orders of magnitudes. Details and source code of our framework will be freely available at: this http URL.

31 citations

Journal ArticleDOI
TL;DR: In this article , an optimization-based framework for multicopter trajectory planning subject to geometrical configuration constraints and user-defined dynamic constraints is presented, which is a novel trajectory representation built upon the novel optimality conditions for unconstrained control effort minimization.
Abstract: In this article, we present an optimization-based framework for multicopter trajectory planning subject to geometrical configuration constraints and user-defined dynamic constraints. The basis of the framework is a novel trajectory representation built upon our novel optimality conditions for unconstrained control effort minimization. We design linear-complexity operations on this representation to conduct spatial–temporal deformation under various planning requirements. Smooth maps are utilized to exactly eliminate geometrical constraints in a lightweight fashion. A variety of state-input constraints are supported by the decoupling of dense constraint evaluation from sparse parameterization and the backward differentiation of flatness map. As a result, this framework transforms a generally constrained multicopter planning problem into an unconstrained optimization that can be solved reliably and efficiently. Our framework bridges the gaps among solution quality, planning efficiency, and constraint fidelity for a multicopter with limited resources and maneuvering capability. Its generality and robustness are both demonstrated by applications to different flight tasks. Extensive simulations and benchmarks are also conducted to show its capability of generating high-quality solutions while retaining the computation speed against other specialized methods by orders of magnitude.

22 citations

Journal ArticleDOI
08 Sep 2021
TL;DR: In this article, a front-end kinodynamic path is searched with force-biased motion primitives, and a nonlinear model predictive control (NMPC) is developed with Hamilton-Jacobi (HJ) forward reachability analysis for error dynamics caused by external forces.
Abstract: Adaptive autonomous navigation with no prior knowledge of extraneous disturbance is of great significance for quadrotors in a complex and unknown environment. The mainstream approach that considers external disturbance is to implement disturbance-rejected control and path tracking. However, the robust control to compensate for tracking deviations is not well-considered regarding energy consumption, and even the reference path will become risky and intractable with disturbance. As recent external forces estimation advances, it is possible to incorporate a real-time force estimator to develop more robust and safe planning frameworks. This letter proposes a systematic (re)planning framework that can resiliently generate safe trajectories under volatile conditions. Firstly, a front-end kinodynamic path is searched with force-biased motion primitives. Then we develop a nonlinear model predictive control (NMPC) as a local planner with Hamilton-Jacobi (HJ) forward reachability analysis for error dynamics caused by external forces. It guarantees collision avoidance by constraining the ellipsoid of the quadrotor body expanded with the forward reachable sets (FRSs) within safe convex polytopes. Our method is validated in simulations and real-world experiments with different sources of external forces.

13 citations

Proceedings ArticleDOI
24 Feb 2022
TL;DR: In this paper , the authors propose a Kinodynamic Jump Space Search (Kino-JSS) to generate a safe and efficient route in unknown environments with aerodynamic disturbances, where a real-time Gaussian Process is employed to model the errors caused by aerodynamic disturbance, which then integrates with a Model Predictive Controller to achieve efficient and accurate trajectory optimization and tracking.
Abstract: Unmapped areas and aerodynamic disturbances render autonomous navigation with quadrotors extremely challenging. To fly safely and efficiently, trajectory planners and trackers must be able to navigate unknown environments with unpredictable aerodynamic effects in real-time. When encountering aerodynamic effects such as strong winds, most current approaches to quadrotor trajectory planning and tracking will not attempt to deviate from a determined plan, even if it is risky, in the hope that any aerodynamic disturbances can be resisted by a robust controller. This paper presents a novel systematic trajectory planning and tracking framework for autonomous quadrotors. We propose a Kinodynamic Jump Space Search (Kino-JSS) to generate a safe and efficient route in unknown environments with aerodynamic disturbances. A real-time Gaussian Process is employed to model the errors caused by aerodynamic disturbances, which we then integrate with a Model Predictive Controller to achieve efficient and accurate trajectory optimization and tracking. We demonstrate our system to improve the efficiency of trajectory generation in unknown environments by up to 75% in the cases tested, compared with recent state-of-the-art. We also show that our system improves the accuracy of tracking in selected environments with unpredictable aerodynamic effects. Our implementation is available in an open source package11https://github.com/Alex-yanranwang/Imperial-KinoJGM.

6 citations

Journal ArticleDOI
TL;DR: In this article , a method for simultaneously planning a path and a sequence of deformations for a tense-grity quadrotor is proposed, which takes advantage of the need to use mixed-integer variables in choosing the drone path and simultaneously choosing the configuration of the drone.
Abstract: This paper proposes a method for simultaneously planning a path and a sequence of deformations for a tensegrity drone. Previous work in the field required the use of bounding surfaces, making the planning more conservative. The proposed method takes advantage of the need to use mixed-integer variables in choosing the drone path (using big-M relaxation) to simultaneously choose the configuration of the drone, eliminating the need to use semidefinite matrices to encode configurations, as was done previously. The numerical properties of the algorithm are demonstrated in numerical studies. To show the viability of tensegrity drones, the first tensegrity quadrotor Tensodrone was build. The Tensodrone is based on a six-bar tensegrity structure that is inherently compliant and can withstand crash landings and frontal collisions with obstacles. This makes the robot safe for the humans around it and protects the drone itself during aggressive maneuvers in constrained and cluttered environments, a feature that is becoming increasingly important for challenging applications that include cave exploration and indoor disaster response.

6 citations