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Register a new userRobust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight
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Added by
Boyu Zhou
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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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 minimum-time 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.
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Recent advances in trajectory replanning have enabled quadrotor to navigate autonomously in unknown environments. However, high-speed navigation still remains a significant challenge. Given very limited time, existing methods have no strong guarantee on the feasibility or quality of the solutions. Moreover, most methods do not consider environment perception, which is the key bottleneck to fast flight. In this paper, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight. A path-guided optimization (PGO) approach that incorporates multiple topological paths is devised, to ensure finding feasible and high-quality trajectories in very limited time. We also introduce a perception-aware planning strategy to actively observe and avoid unknown obstacles. A risk-aware trajectory refinement ensures that unknown obstacles which may endanger the quadrotor can be observed earlier and avoid in time. The motion of yaw angle is planned to actively explore the surrounding space that is relevant for safe navigation. The proposed methods are tested extensively. We will release our implementation as an open-source package for the community.
The recent works on quadrotor have focused on more and more challenging tasks on increasingly complex systems. Systems are often augmented with slung loads, inverted pendulums or arms, and accomplish complex tasks such as going through a window, grasping, throwing or catching. Usually, controllers are designed to accomplish a specific task on a specific system using analytic solutions, so each application needs long preparations. On the other hand, the direct multiple shooting approach is able to solve complex problems without any analytic development, by using on-the-shelf optimization solver. In this paper, we show that this approach is able to solve a wide range of problems relevant to quadrotor systems, from on-line trajectory generation for quadrotors, to going through a window for a quadrotor-and-pendulum system, through manipulation tasks for a aerial manipulator.
This letter presents a fully autonomous robot system that possesses both terrestrial and aerial mobility. We firstly develop a lightweight terrestrial-aerial quadrotor that carries sufficient sensing and computing resources. It incorporates both the high mobility of unmanned aerial vehicles and the long endurance of unmanned ground vehicles. An adaptive navigation framework is then proposed that brings complete autonomy to it. In this framework, a hierarchical motion planner is proposed to generate safe and low-power terrestrial-aerial trajectories in unknown environments. Moreover, we present a unified motion controller which dynamically adjusts energy consumption in terrestrial locomotion. Extensive realworld experiments and benchmark comparisons validate the robustness and outstanding performance of the proposed system. During the tests, it safely traverses complex environments with terrestrial aerial integrated mobility, and achieves 7 times energy savings in terrestrial locomotion. Finally, we will release our code and hardware configuration as an open-source package.
In this work, we present a geometry-based grasping algorithm that is capable of efficiently generating both top and side grasps for unknown objects, using a single view RGB-D camera, and of selecting the most promising one. We demonstrate the effectiveness of our approach on a picking scenario on a real robot platform. Our approach has shown to be more reliable than another recent geometry-based method considered as baseline [7] in terms of grasp stability, by increasing the successful grasp attempts by a factor of six.
We present a new quadrotor geometric control scheme that is capable of tracking highly aggressive trajectories. Unlike previous works, our geometric controller uses the logarithmic map of SO(3) to express rotational error in the Lie algebra, allowing us to treat the manifold in a more effective and natural manner, and can be shown to be globally attractive. We show the performance of our control scheme against highly aggressive trajectories in simulation experiments. Additionally, we present an adaptation of this controller that allows us to interface effectively with the angular rate controllers on an onboard flight control unit and show the ability of this adapted control scheme to track aggressive trajectories on a quadrotor hardware platform.
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