5 research outputs found
A hierarchical control scheme for multiple aerial vehicle transportation systems with uncertainties and state/input constraints
No full textMultiple aerial vehicles have the potential to transport payload in any direction and with any orientation in Special Euclidean Group. In this article, the control problem of the multiple aerial vehicle transportation system with uncertainties and state/input constraints is considered. The multiple aerial vehicles connect with the load via spherical pairs that coincide with the center of mass of the aerial vehicles. A hierarchical control scheme of such challenging complex systems is proposed. The outer loop is designed by a tube-based model predictive control in order to deal with uncertainties, state constraints and input boundedness, while the inner loop is designed by robust control technique which forces the attitude tracking error of the aircraft into robust invariant set. The attitude tracking error of the inner loop induces a difference between the commanded and the actual equivalent wrench acting on the load. Such difference is treated as the equivalent disturbance of the outer loop to guarantee the fulfillment of the state and input constraints. The hierarchical control structure simplifies the design procedure, while still preserves the convergence and feasibility of the overall controlled system under uncertainties, which are proved strictly in the paper. Numerical simulations on a six-quadrotor transportation system are conducted to demonstrate the performance of the proposed control scheme. A real-world prototype including three quadrotors is developed. The hardware-in-the-loop simulation on the prototype supports the real-time feasibility of the proposed scheme
Real-Time Multi-Modal Active Vision for Object Detection on UAVs Equipped With Limited Field of View LiDAR and Camera
No full textThis letter aims to solve the challenging problems in multi-modal active vision for object detection on unmanned aerial vehicles (UAVs) with a monocular camera and a limited Field of View (FoV) LiDAR. The point cloud acquired from the low-cost LiDAR is firstly converted into a 3-channel tensor via motion compensation, accumulation, projection, and up-sampling processes. The generated 3-channel point cloud tensor and RGB image are fused into a 6-channel tensor using an early fusion strategy for object detection based on a Gaussian YOLO network structure. To solve the low computational resource problem and improve the real-time performance, the velocity information of the UAV is further fused with the detection results based on an extended Kalman Filter (EKF). A perception-aware model predictive control (MPC) is designed to achieve active vision on our UAV. According to our performance evaluation, our pre-processing step improves other literature methods running time by a factor of 10 while maintaining acceptable detection performance. Furthermore, our fusion architecture reaches 94.6 mAP on the test set, outperforming the individual sensor networks by roughly 5%. We also described an implementation of the overall algorithm on a UAV platform and validated it in real-world experiments
Design and Implementation of a Fully-Actuated Integrated Aerial Platform Based on Geometric Model Predictive Control
No full textUnlike individual unmanned aerial vehicles (UAVs), integrated aerial platforms (IAPs) containing multiple UAVs do not suffer from underactuation and can move omnidirectionally in six dimensions, providing a basis for constructing aerial manipulation platforms. Compared to single UAVs, multi-UAV IAPs are also advantageous in terms of payload and fault-tolerance capacity, making them promising candidates as platforms with integrated-response, observation, and strike capabilities. Herein, an IAP structure design containing three sub-UAVs connected in a star-like configuration is presented. This form of integration enables the IAP, as a whole, to simultaneously adjust its position and attitude in six dimensions. The dynamics of the overall system of the IAP are modeled. On this basis, an overall system controller is designed. To simplify control, based on stability of cascaded system, the rotational motion of the sub-UAVs is treated as a inner-loop subsystem, whereas the overall motion of the IAP is seen as a outer-loop subsystem. Because the configuration space of the sub-UAVs is non-Euclidean, a controller is designed for the outer-loop subsystem based on model predictive control on the manifold. Subsequently, the stability of the closed-loop system is demonstrated. Fieldbus technology is employed to design a real-time, scalable communication architecture for multiple sub-UAVs, followed by the development of a principle prototype of the multi-UAV IAP that consists of hardware and software systems. The effectiveness of the IAP design and control method is validated through simulation and real-world prototype-based tests. In the simulation and real-world tests, the proposed methodology can make the IAP system converge to the desired configuration at the presence of large initial configuration error. The same test scenario cannot be finished by a baseline PID controller. The advantage of the proposed control scheme in dealing with state and input constraints is shown via such tests
Expandable Fully Actuated Aerial Vehicle Assembly: Geometric Control Adapted from an Existing Flight Controller and Real-World Prototype Implementation
No full textAn assembly composed of multiple aerial vehicles can realize omnidirectional motion with six degrees of freedom. Such an assembly has a heavier payload capacity and better fault tolerance compared with a single aircraft. Thus, such assemblies have the potential to become an ideal platform for manipulation. This paper investigates the controller design and prototype implementation for an expandable aerial vehicle assembly (AVA). The proposed AVA is composed of multiple sub-aircraft connected together via spherical joints at their center of mass. Each sub-aircraft can rotate around the spherical joint. The system dynamics of such an AVA can be separated into a slowly varying system and a fast varying system. The design criteria for a controller for this type of AVA was analyzed based on the similarity between the slowly varying system and a fully actuated rigid aircraft. This can reduce the design procedure for the controller and increase the expandability of the AVA. The stability criteria were carefully analyzed by considering the tracking error of each sub-aircraft. As an example, the controller of the AVA was designed using trajectory linearization control on the manifold, since the configuration space of the aircraft is a non-Euclidean space. A prototype composed of three quadrotors was implemented. The real-time expandable communication protocol among the different sub-aircraft was designed based on the CAN bus. Furthermore, the software and the hardware of the real-world prototype were developed. Both simulation and real-world tests were conducted, which validated the feasibility of the control design and the software implementation for an expandable assembly containing multiple aerial vehicles
Multi-Agent Visual-Inertial Localization for Integrated Aerial Systems with Loose Fusion of Odometry and Kinematics
No full textReliablyand efficiently estimating the relative pose and global localization of robots in a common reference for Integrated Aerial Platforms (IAPs) is a challenging problem. Unlike unmanned aerial vehicle (UAV) swarms, where the agent individual is able to move freely, IAPs connect UAV agents with mechanical joints, such as spherical joints, and form a rigid central platform, limiting the degree of freedom (DOF) of agents. Traditional methods, which rely on forming loop closures, object detection, or range sensors, suffer from degeneration or inefficiency due to the restricted relative motion between agents. In this paper, we present a centralized multi-agent localization system that fuses the internal kinematic constraints of IAPs and odometry measurements, using only visual-inertial suits for ego-motion estimation for agents and an additional 9-DOF Inertial Measurement Unit (IMU) attached to the central platform for posture estimation. A general formulation for kinematic constraints is derived without requiring knowledge about detailed kinematic parameters. A sliding-window optimization-based state estimator is constructed to estimate the relative transformation between agents. Our proposed approach is validated in our collected dataset. The results show that the proposed method reduces the global localization drift by 27.15% and relative localization error by 53.4% in the translation part and 36.99% in the rotation part compared to the baseline.</p
