119 research outputs found

    Minimum-time trajectory generation for quadrotors in constrained environments

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    In this paper, we present a novel strategy to compute minimum-time trajectories for quadrotors in constrained environments. In particular, we consider the motion in a given flying region with obstacles and take into account the physical limitations of the vehicle. Instead of approaching the optimization problem in its standard time-parameterized formulation, the proposed strategy is based on an appealing re-formulation. Transverse coordinates, expressing the distance from a frame path, are used to parameterise the vehicle position and a spatial parameter is used as independent variable. This re-formulation allows us to (i) obtain a fixed horizon problem and (ii) easily formulate (fairly complex) position constraints. The effectiveness of the proposed strategy is proven by numerical computations on two different illustrative scenarios. Moreover, the optimal trajectory generated in the second scenario is experimentally executed with a real nano-quadrotor in order to show its feasibility.Comment: arXiv admin note: text overlap with arXiv:1702.0427

    Enhancing 3D Autonomous Navigation Through Obstacle Fields: Homogeneous Localisation and Mapping, with Obstacle-Aware Trajectory Optimisation

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    Small flying robots have numerous potential applications, from quadrotors for search and rescue, infrastructure inspection and package delivery to free-flying satellites for assistance activities inside a space station. To enable these applications, a key challenge is autonomous navigation in 3D, near obstacles on a power, mass and computation constrained platform. This challenge requires a robot to perform localisation, mapping, dynamics-aware trajectory planning and control. The current state-of-the-art uses separate algorithms for each component. Here, the aim is for a more homogeneous approach in the search for improved efficiencies and capabilities. First, an algorithm is described to perform Simultaneous Localisation And Mapping (SLAM) with physical, 3D map representation that can also be used to represent obstacles for trajectory planning: Non-Uniform Rational B-Spline (NURBS) surfaces. Termed NURBSLAM, this algorithm is shown to combine the typically separate tasks of localisation and obstacle mapping. Second, a trajectory optimisation algorithm is presented that produces dynamically-optimal trajectories with direct consideration of obstacles, providing a middle ground between path planners and trajectory smoothers. Called the Admissible Subspace TRajectory Optimiser (ASTRO), the algorithm can produce trajectories that are easier to track than the state-of-the-art for flight near obstacles, as shown in flight tests with quadrotors. For quadrotors to track trajectories, a critical component is the differential flatness transformation that links position and attitude controllers. Existing singularities in this transformation are analysed, solutions are proposed and are then demonstrated in flight tests. Finally, a combined system of NURBSLAM and ASTRO are brought together and tested against the state-of-the-art in a novel simulation environment to prove the concept that a single 3D representation can be used for localisation, mapping, and planning

    Control, estimation, and planning algorithms for aggressive flight using onboard sensing

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    Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 107-111).This thesis is motivated by the problem of fixed-wing flight through obstacles using only on-board sensing. To that end, we propose novel algorithms in trajectory generation for fixed-wing vehicles, state estimation in unstructured 3D environments, and planning under uncertainty. Aggressive flight through obstacles using on-board sensing involves nontrivial dynamics, spatially varying measurement properties, and obstacle constraints. To make the planning problem tractable, we restrict the motion plan to a nominal trajectory stabilized with an approximately linear estimator and controller. This restriction allows us to predict distributions over future states given a candidate nominal trajectory. Using these distributions to ensure a bounded probability of collision, the algorithm incrementally constructs a graph of trajectories through state space, while efficiently searching over candidate paths through the graph at each iteration. This process results in a search tree in belief space that provably converges to the optimal path. We analyze the algorithm theoretically and also provide simulation results demonstrating its utility for balancing information gathering to reduce uncertainty and finding low cost paths. Our state estimation method is driven by an inertial measurement unit (IMU) and a planar laser range finder and is suitable for use in real-time on a fixed-wing micro air vehicle (MAV). The algorithm is capable of maintaining accurate state estimates during aggressive flight in unstructured 3D environments without the use of an external positioning system. The localization algorithm is based on an extension of the Gaussian Particle Filter. We partition the state according to measurement independence relationships and then calculate a pseudo-linear update which allows us to use 25x fewer particles than a naive implementation to achieve similar accuracy in the state estimate. Using a multi-step forward fitting method we are able to identify the noise parameters of the IMU leading to high quality predictions of the uncertainty associated with the process model. Our process and measurement models integrate naturally with an exponential coordinates representation of the attitude uncertainty. We demonstrate our algorithms experimentally on a fixed-wing vehicle flying in a challenging indoor environment. The algorithm for generating the trajectories used in the planning process computes a transverse polynomial offset from a nominal Dubins path. The polynomial offset allows us to explicitly specify transverse derivatives in terms of linear equality constraints on the coefficients of the polynomial, and minimize transverse derivatives by using a Quadratic Program (QP) on the polynomial coefficients. This results in a computationally cheap method for generating paths with continuous heading, roll angle, and roll rate for the fixed-wing vehicle, which is fast enough to run in the inner loop of the RRBT.by Adam Parker Bry.S.M

    Optimization-based Estimation and Control Algorithms for Quadcopter Applications

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    Optimization-based Estimation and Control Algorithms for Quadcopter Applications

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    Advanced Feedback Linearization Control for Tiltrotor UAVs: Gait Plan, Controller Design, and Stability Analysis

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    Three challenges, however, can hinder the application of Feedback Linearization: over-intensive control signals, singular decoupling matrix, and saturation. Activating any of these three issues can challenge the stability proof. To solve these three challenges, first, this research proposed the drone gait plan. The gait plan was initially used to figure out the control problems in quadruped (four-legged) robots; applying this approach, accompanied by Feedback Linearization, the quality of the control signals was enhanced. Then, we proposed the concept of unacceptable attitude curves, which are not allowed for the tiltrotor to travel to. The Two Color Map Theorem was subsequently established to enlarge the supported attitude for the tiltrotor. These theories were employed in the tiltrotor tracking problem with different references. Notable improvements in the control signals were witnessed in the tiltrotor simulator. Finally, we explored the control theory, the stability proof of the novel mobile robot (tilt vehicle) stabilized by Feedback Linearization with saturation. Instead of adopting the tiltrotor model, which is over-complicated, we designed a conceptual mobile robot (tilt-car) to analyze the stability proof. The stability proof (stable in the sense of Lyapunov) was found for a mobile robot (tilt vehicle) controlled by Feedback Linearization with saturation for the first time. The success tracking result with the promising control signals in the tiltrotor simulator demonstrates the advances of our control method. Also, the Lyapunov candidate and the tracking result in the mobile robot (tilt-car) simulator confirm our deductions of the stability proof. These results reveal that these three challenges in Feedback Linearization are solved, to some extents.Comment: Doctoral Thesis at The University of Toky

    Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

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    The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

    Formation control of swarms of unmanned aerial vehicles

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    The objective of this thesis is to design a distributed formation control system for swarms of unmanned aerial vehicles which addresses the challenges of scalability, collision avoidance, failure recovery, energy efficiency, and control performance. The swarms are arranged in tightly/loosely coupled architectures, which are based on homogeneous nodes in a distributed network of leader-follower/leaderless structures. The model of each node in the swarm formation is based on the nonlinear/linear dynamic model of a quadcopter, i.e. an unmanned aerial vehicle. The goal is to design the formation control of swarms of unmanned aerial vehicles, which is divided into high- and low-level control. From the high-level control perspective, the main contribution is to propose continuous path planning which can quickly react to events. Setpoints are generated for the swarms of unmanned aerial vehicles considering the complex movement of a hierarchical formation, soft landing, and failure recovery. The hierarchical formation and soft landing are executed using a fixed formation. Reconfiguration of the formation after node failures is implemented using a shortest path algorithm, combinatorial algorithms, and a thin plate spline. Besides this, from the low-level control perspective, the main contribution is to manoeuvre the nodes smoothly. The tracking of setpoints and stabilisation of each node is handled by a nonlinear sliding mode control with proportional derivative control and a linear quadratic regulator with integral action. The proposed strategies are evaluated using simulations, and the obtained results are compared and analysed both qualitatively and quantitatively using different scenario-relevant metrics
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