Small Autonomous Helicopter Research Articles

The Use of Unmanned Aerial Vehicles in Remote Sensing Systems.

This paper describes the possibility of using a small autonomous helicopter to perform tasks using a remote sensing system. This article further shows the most effective way to properly set up autopilot and to process its validation during flight tests. The most important components of the remote sensing system are described and the possibilities of using this system to monitor gas transmission and distribution networks are presented.

Scalable Architecture for CPS: A Case Study of Small Autonomous Helicopter

Building a scalable and highly integrated systems is an important research direction and one of key technologies in Cyber-Physical Systems (CPS). Autonomous helicopter is a typical CPS application and its flight presents challenges in flight control system design with scalability. In this paper, we present the integration architecture of hardware and software for the flight control system based TREX 600 helicopter. In order to enhance scalability, the flight control system uses the PC104 and the ARM which is exerted to process the measurement data, including the position, attitude, height etc. The flight control is developed based multi-loop decoupling PI control which is easy to be implemented. Finally, the flight control system is successfully verified in the actual autonomous flight control experiment.

Highly Scalable Flight Control Architecture for Small Autonomous Helicopter Using PC104/ARM Technology

Autonomous helicopter flight presents challenges in flight control system design with scalability. In this paper, we present the integration architecture of hardware and software for the flight control system based TREX 600 helicopter. In order to enhance scalability, the flight control system uses the PC104 and the ARM which is exerted to process the measurement data, including the position, attitude, height etc. The flight control is developed based multi-loop decoupling PID control which is easy to be implemented. Finally, the flight control system is successfully verified in the actual autonomous flight control experiment.

Dynamic visual servoing of a small scale autonomous helicopter in uncalibrated environments

This paper presents a novel adaptive controller for image-based visual servoing of a small autonomous helicopter to cope with uncalibrated camera parameters and unknown 3D geometry of the feature points. The controller is based on the back-stepping technique, but its design has two new features. First, it incorporates the visual feedback into the last step of the backstepping procedure, while existing backsteppingbased methods employ the visual feedback at the early steps. Second, the controller maps the image errors onto the actuator space via a depth-independent interaction matrix to avoid estimation the depths of the feature points. The new design method makes it possible to linearly parameterize the closed-loop dynamics by the unknown camera parameters and coordinates of the feature points in the 3D space so that an adaptive algorithm can be developed to estimate the unknown parameters and coordinates on-line. Two potential functions are introduced in the controller to guarantee convergence of the image errors and to avoid trivial solutions of the estimated parameters. The Lyapunov method is used to prove the asymptotic stability of the proposed controller based on the nonlinear dynamics of the helicopter. Simulations have been also conducted to demonstrate the performance of the proposed method.

Detection of Sensor Faults in Small Helicopter UAVs Using Observer/Kalman Filter Identification

Reliability is a critical issue in navigation of unmanned aerial vehicles (UAVs) since there is no human pilot that can react to any abnormal situation. Due to size and cost limitations, redundant sensor schemes and aeronautical‐grade navigation sensors used in large aircrafts cannot be installed in small UAVs. Therefore, other approaches like analytical redundancy should be used to detect faults in navigation sensors and increase reliability. This paper presents a sensor fault detection and diagnosis system for small autonomous helicopters based on analytical redundancy. Fault detection is accomplished by evaluating any significant change in the behaviour of the vehicle with respect to the fault‐free behaviour, which is estimated by using an observer. The observer is obtained from input‐output experimental data with the Observer/Kalman Filter Identification (OKID) method. The OKID method is able to identify the system and an observer with properties similar to a Kalman filter, directly from input‐output experimental data. Results are similar to the Kalman filter, but, with the proposed method, there is no need to estimate neither system matrices nor sensor and process noise covariance matrices. The system has been tested with real helicopter flight data, and the results compared with other methods.

Transition Management for the Smooth Flight of a Small Autonomous Helicopter

This work is centered in the definition of a transition management system for a small autonomous helicopter based on trajectory smoothing and a finite state machine (FSM). A smooth flight schedule decreases transients originated by direction changes and flight mode transitions (e.g., horizontal flight to hover mode). Although previous works have presented trajectory generation and FSM oriented controls, no previous studies have mixed these approaches in a single framework together with speed transitions. The proposed methods are validated in simulation with a realistic dynamic model of a small helicopter.

State Estimation for Autonomous Helicopter via Sensor Modeling

This paper presents a technique to accurately estimate the states of a small autonomous helicopter using a combination of gyroscopes, accelerometers, and GPS. State estimation is performed by using indirect Kalman filtering based on sensor modeling and error dynamics, not helicopter modeling. The number of measurements available to the Kalman filter is nine: three attitude rates () from the gyroscopes, three accelerations (ẍ, ÿ, z̈), and three position measurements (x, y, z) from the GPS. The Kalman filter is decomposed into two loosely coupled filters — one for attitude and the other for position estimation for fast computation.

Sensor and actuator fault detection in small autonomous helicopters

The use of autonomous helicopters in civilian applications requires the improvement of safety conditions to avoid potential accidents. Fault detection and isolation (FDI) plays an important role in this context. This paper presents an actuator and sensor FDI system for small autonomous helicopters. Fault detection is accomplished by evaluating any significant change in the behaviour of the vehicle with respect to the fault-free behaviour, which is estimated by using observers. Several types of faults have been considered. The effectiveness of the proposed approach is demonstrated by means of experimental results and simulations.

An Inertial and Visual Sensing System for a Small Autonomous Helicopter

This paper describes the design and architecture of a low-cost and light-weight inertial and visual sensing system for a small-scale autonomous helicopter. A custom 6-axis IMU and a stereo vision s...

An Inertial and Visual Sensing System for a Small Autonomous Helicopter

AbstractThis paper describes the design and architecture of a low‐cost and light‐weight inertial and visual sensing system for a small‐scale autonomous helicopter. A custom 6‐axis IMU and a stereo vision system provide vehicle attitude, height, and velocity information. We discuss issues such as robust visual processing, motion resolution, dynamic range, and sensitivity. © 2004 Wiley Periodicals, Inc.

Real-Time Motion Planning for Agile Autonomous Vehicles

Planning the path of an autonomous, agile vehicle in a dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent efforts aimed at using randomized algorithms for planning the path of kinematic and dynamic vehicles have demonstrated considerable potential for implementation on future autonomous platforms. This paper builds upon these efforts by proposing a randomized path planning architecture for dynamical systems in the presence of fixed and moving obstacles. This architecture addresses the dynamic constraints on the vehicle's motion, and it provides at the same time a consistent decoupling between low-level control and motion planning. The path planning algorithm retains the convergence properties of its kinematic counterparts. System safety is also addressed in the face of finite computation times by analyzing the behavior of the algorithm when the available onboard computation resources are limited, and the planning must be performed in real time. The proposed algorithm can be applied to vehicles whose dynamics are described either by ordinary differential equations or by higher-level, hybrid representations. Simulation examples involving a ground robot and a small autonomous helicopter are presented and discussed.