Stanisław Gardecki

Estimation of Altitude and Vertical Velocity for Multirotor Aerial Vehicle Using Kalman Filter

Knowledge about precise robot localization is a key ingredient in controlling it, but the task is not trivial without any visual or GPS feedback. In this paper, authors concentrate on estimation of information about the robot’s altitude. One of the ways to acquire it, is a barometer. This type of sensor returns atmospheric pressure from which the height above the sea level can be computed. These readings have some disadvantages e.i.: vulnerability to pressure jumps and temperature drift as well as delay on the output. These problems can be solved by using Kalman filter algorithm for estimating altitude and vertical velocity, based not only on barometer readings, but also on accelerometer data. In the paper, derivation of the Kalman equations for the process to estimated are shown. Also improvements of the algorithm are described. The results of tests of this algorithm on real flying robot proved that estimates calculated with this method are precise and noise resistant.

Falcon: A Compact Multirotor Flying Platform with High Load Capability

Multirotor flying platforms are very popular research subjects in the field of robotics. However, there are some major disadvantages for this type of vehicles, such as limited flight time, insufficient lifting capability and reduced range of operation. In this paper, the multirotor flying platform Falcon is presented. Its design is aimed to provide versatile, multipurpose research platform with high payload capabilities, maintaining compact dimensions and simple, reliable mechanical design. Presented platform was evaluated in various scenarios performing both autonomous and semi-autonomous flights.

Performance of Coaxial Propulsion in Design of Multi-rotor UAVs

There are many different types of propulsion systems developed for multi rotor UAVs. One of the most interesting designs is so called X8 quadrocopter, which extends original quadrotor concept to 8 motors, arranged in 4 coaxial pairs. The advantage of this solution is increased lift of platform, with reasonable volume of platform kept. However, this design suffers from the loss of efficiency due to coaxial propellers’ configuration, because the lower propeller loses thrust working in prop wash of upper propeller. This paper presents the experimental verification of performance of such propulsion system in practical terms of designing multi rotor platforms, comparing to design with 8 isolated propulsion units. In addition, its advantages versus classic quadrotor concept is shown. The series of experiments with different motors and sizes of propellers were conducted to estimate efficiency of coaxial propulsion regarding useful thrust generated by each configuration.

Thrust estimation by fuzzy modeling of coaxial propulsion unit for multirotor UAVs

In this paper, a simple and easily applicable model of the coaxial propulsion unit for multirotor UAVs is presented. Measurements performed on the experimental test bench provided information about the generated thrust in relation to PWM control signals and supply voltage. Modelling techniques based on Takagi-Sugeno fuzzy interface and surface fitting are proposed. Implementation of the first order inertial element with the varying time constant allows to consider the propulsion units dynamics. A fuzzy model was chosen for implementation taking into consideration a computational complexity benchmark. Fusion of four independent models provides information about a total thrust generated by the physical platform during real flight scenarios. Promising results of experimental studies open the way for possible applications of the presented method, such as expanding estimation algorithms of attitude and vertical velocity or improvement of the mathematical model.

Unscented Kalman Filter for an orientation module of a quadrotor mathematical model

The article describes the Unscented Kalman Filter, used in an orientation module of the quadrotor. Control of the quadrocopter is an important issue. Since it belongs to the group of the UAVs (Unmanned Aerial Vehicle), quadrotor must pass safety requirements in order to be used in urban spaces. Here, authors are solving one of the most important problems: estimation of the state vector of the quadrotor. Multirotors like quadrotor can work in semi autonomous or full autonomous mode. Regardless of the used mode, at least one control loop must be always on: an orientation loop. Normally a regulator responsible for an orientation stabilization is based on measurements from IMU (Inertial Measurement Unit). Here, an extended version of the state estimator, based on IMU readings and prediction from the model is introduced. Authors have implemented the Unscented Kalman Filter (firstly described in [1]) and proved that, this estimator is suitable in case of UAV.

Robust estimation algorithm of altitude and vertical velocity for multirotor UAVs

This paper describes the process of the development and improvement of the altitude and vertical velocity estimation algorithm. The previous method was developed by authors two years back. After a diagnosis of pressure temperature drift caused by the main IMU, the additional barometric sensor was introduced. Based on readings from this reference component, three main modifications were developed to the algorithms structure. In addition, three experimental sequences are presented to compare the previous approach with new ones. Results showed that new methods achieve better performance and are free from pressure drift caused by sensors heating. Decreasing frequency of altitude measurements in Kalman filter resulted in more robust estimates for pressure changes not related to the vertical movement in indoor and outdoor flights. With properly working estimation algorithm, there is a possibility to develop a controller to maintain desired altitude or vertical velocity.

Introduction of the Flying Robots into the Human Environment: An Adaptive Square-Root Unscented Kalman Filter for a Fault Tolerant State Estimation in a Quadrotor

Recently, there have been growing interest in an autonomous control of Unmanned Aerial Vehicles (UAV). The key objective for scientists is to make the robots capable to operate in human shared environment. The main problem concerns control as well as filtering sensory data in all scenarios. Among many types of control algorithms there are a few which takes into account fault cases. The control algorithm can be fault tolerant in case of actuators saturation or their damage. The situation is more complicated in sensory system failure, in which case, the control is defenseless. In this article, a novel Adaptive Square-Root Unscented Kalman Filter (ASRUKF) in application of fault tolerant (FT) state estimator is presented. The SRUKF was designed for an orientation module of a quad rotors mathematical model. The emphasis was put on the estimators adaptability in case of the sensory system fault. The main objective of this paper was to show the idea of adaptability of the ASRUKF in terms of FT system. The work includes model derivation, explanation on the SRUKF algorithm as well as description of an adaptive parameters change of the estimator. Finally, the paper shows the experiment with a quad rotor in test bed and promising results.

An Efficient PSO-Based Method for an Identification of a Quadrotor Model Parameters

This paper considers the method of Quadrotor’s model parameters identification. Nowadays, restrictions are being imposed on the drons, forcing their control algorithms to be robust and faultless. This can be partially ensured by Model Reference Adaptive Control (MRAC) as well as dedicated state estimators (e.g. Extended Kalman Filter). Although those methods can be easy implemented and used, in all scenario, the parameterized model is needed. In this work we proposed the identification method for parameters of the quadrotor’s orientation model, based on the PSO (Particle Swarm Optimization). We also add different physical aspects to model, so it can characterize the real Quadrotor more precisely. The conducted experiments shows that the PSO, can provide fast and reliable estimation of the model parameters. It also reveals interesting nature of the proposed models.

The Influence of the End Effector Gyroscopic Torques on a Base of the Manipulator

In this paper, the model of the robotic arm with payload is presented. The payload is configured as a rotating mass, which imitate the tool on a robot’s wrist. While manipulating with working tool the gyroscopic effect can occur. This leads to extra moments in each joint of the manipulator. In order to evaluate the scale of the process and the consequences in the robot’s trajectory, mathematical model of the robot including rotating mass was derived. The system was simulated for different parameters. It was proven that the gyroscopic effect cannot be neglected, especially when the robot’s movement are rapid and conducted simultaneously in more then one joint at once. For the purpose of the work, authors decided to use manipulator with five degrees of freedom, equipped with rotational joints only.

Development of Vertical Movement Controller for Multirotor UAVs

In this paper, a vertical velocity controller for multirotor UAVs is proposed. Based on the previous research, authors developed a model of vertical movement which takes into consideration measurement noises, and designed a simulation that allowed tuning of mentioned controller. As a regulation scheme, a classical PI structure was used. The derivative part was neglected because of high amplitude of noise during harsh touchdowns. Tuning of parameters was achieved by PSO optimisation. Experimental results showed that the selected control structure and its parameters fulfill stated requirements. In addition, developed simulation is adequate to the real platform.