Subterranean Platoon of Mobile Robots for Inspection and Maintenance

Subterranean environments, including tunnels, sewers, and water pipes, present a complex setting with multiple environmental and structural hazards as their conditions degrade and change over time which are too risky situations for humans. Moreover, inspection and maintenance of these environments require road excavations which causes road closures, in addition, to being costly, noisy, dirty, and inconvenient. To cope with these issues, infrastructure robots can be utilised to do tasks such as checking the degradation condition and leak localisation and doing reparation. However, these environments are characterized by darkness, moisture, temperature, and water conditions, isolation and restriction in space, and curvatures. These conditions present significant challenges in the robot structure design, control, localisation, navigation, and communication. Most current research on subterranean robots focuses on using a single robot to do inspection tasks. For data acquisition and supervisory control, a communication link between the base station and the robot is mandatory. This link must be a wireless channel due to the subterranean environment geometry. However, the above-mentioned characteristics make communication unreliable where Non-Line-of-Sight (NLOS) occurs. This project presents a multi-robot system to save the time and money that goes into the maintenance of these environments considering the above-mentioned hazards and challenges.

Platooning of Car-like Vehicles in Urban Environments: Simulation - Experiments

A platoon of Car-like Vehicles Navigating in an Urban Environment: simulation and experiments using software called “ICARS” developed under ROS at the Ecole Centrale de Nantes.

Car-like Vehicles Platoon Control with Gap Closure and Collision Avoidance Capabilities

You can check this paper for details.

Formation Control of Three Ardrone Quadcopters

Formation control algorithm is devopled for UAVs which can cooperatively fly in formation in three-dimensional space. The algorithm is application to bidirectional or unidirectional network connections. You can check this paper for details.

Ardrone visual servoing

State estimation and control of a quadrotor of type ardrone v2 are successfully implemented. The ardrone is equipped with downward and front cameras, IMU, downward sonar, and a flight controller that is connected wirelessly with a ground station, PC, in which the estimation and control algorithms are implemented. The information obtained from the onboard sensors is fused by an Extended Kalman filter to get 3D quadrotor states. A high-level PID control algorithm is used to control the 3D linear position of the quadrotor. The ground station interface, estimation, and control algorithms are developed under ROS.

A New 6-DOF Nonredundant Quadrotor Manipulation System

You can check these papers 1 and 2 for details.

Asctec Pelican Quadrotor Rotor-assembly Identification

To estimate the rotor-assembly (ESC, Motor, and Propeller) parameters (Thrust and Drag Coefficients), an experimental setup is carried out. In this experiment, the rotor is mounted on a 6-DOF torque/force sensor (see Appendix I for more technical details) that is connected to a NI Data Acquisition Card (NI DAC). Then, the DAC is connected to a PC, running SIMULINK program as an interface, to read data from DAC. The velocity of rotor is changed gradually, and in each time, the generated thrust and drag moment is measured and recorded using SIMULINK program. By using MATLAB Curve Fitting toolbox the acquired data of thrust and moment is fitted to be in the form of (2.88 and 2.89). Thus, the thrust and moment coefficients can be obtained. You can check this paper and this thesis for details.

Identification of A Customised Quadrotor with its validation

We present a methodology to identify all the parameters of a quadrotor system including the structure parameters and rotor assembly parameters. A CAD model is developed using SOLIDWORKS to calculate the mass moment of inertia and all the missing geometrical parameters. A three simple test rigs are built and used to identify the relationship between the motor input Pulse Width Modulation (PWM) signal and the angular velocity, the thrust force, and drag moment of the rotors. A simple algorithm is implemented to an Inertial Measurement Unit (IMU) for estimating the attitude and altitude of the quadrotor. Experimental set up is built to verify and test the accuracy of these proposed techniques. A controller is designed based on the feedback linearization method such that the quadrotor attitude can be stabilized. Finally, the experimental results show the effectiveness of the proposed techniques and the controller design. You can check this paper and this thesis for details.