Somasundar Kannan

Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers.

Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly

Vision based fuzzy control autonomous landing with UAVs: From V-REP to real experiments

213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing.

A modularization approach for nonlinear model predictive control of distributed fast systems

This paper is focused on the design of a vision based control approach for the autonomous landing task of Vertical Take-off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs). Here is presented the setup of a simulated environment developed in V-REP connected to ROS, and its uses for tuning a vision based control approach. In this work, a Fuzzy control approach was proposed to command the UAVs vertical, longitudinal, lateral and orientation velocities. The UAVs pose estimation was done based on a vision algorithm and the knowledge of the landing target. Real experiments with a quadrotor landing in a moving platform are also presented.

A real-time model predictive position control with collision avoidance for commercial low-cost quadrotors

Distributed interconnected systems are omnipresent today. The development of advanced control methods for such systems are still challenging. Herein, the real-time applicability, flexibility, portability and ease of implementation are issues of the existing control solutions, especially for more advanced methods such as model predictive control. This paper is addressing these issues by presenting an efficient modular composition scheme for distributed fast nonlinear systems. The advantage of this modularization approach is the capability of changing control objectives, constraints, dynamics and system topology online while maintaining fast computation. This work analyzes the functions that have to be provided for a continuation generalized minimal residual method (CGMRES) model predictive controller based on the underlying control problem. The specific structure of these functions allows their decomposition into suitable fast modules. These modules are then used to recompose the functions which are required for the control of distributed systems in a computational efficient way, while maintaining the flexibility to dynamically exchange system parts. To validate this computational efficiency, the computation time of the proposed modular control approach is compared with a standard nonmodular implementation in a pursuit scenario of quadrotor unmanned aerial vehicles (UAV). Furthermore the real-time applicability is discussed for the given scenario.

Adaptive control of Aerial Manipulation Vehicle

Unmanned aerial vehicles (UAVs) are the future technology for autonomous fast transportation of individual goods. They have the advantage of being small, fast and not to be limited to the local infrastructure. This is not only interesting for delivery of private consumption goods up to the doorstep, but also particularly for smart factories. One drawback of autonomous drone technology is the high development costs, that limit research and development to a small audience. This work is introducing a position control with collision avoidance as a first step to make low-cost drones more accessible to the execution of autonomous tasks. The paper introduces a semilinear state-space model for a commercial quadrotor and its adaptation to the commercially available AR.Drone2 system. The position control introduced in this paper is a model predictive control (MPC) based on a condensed multiple-shooting continuation generalized minimal residual method (CMSCGMRES). The collision avoidance is implemented in the MPC based on a sigmoid function. The real-time applicability of the proposed methods is demonstrated in two experiments with a real AR.Drone quadrotor, adressing position tracking and collision avoidance. The experiments show the computational efficiency of the proposed control design with a measured maximum computation time of less than 2ms.

Modeling and Control of Aerial Manipulation Vehicle with Visual sensor

Adaptive Control of an Aerial Manipulation Vehicle is discussed here. The aerial manipulation vehicle consisting of a quadrotor and a robotic arm has a highly coupled dynamics. The nonlinear coupling introduces additional forces and moments on the quadrotor which prevents it from precisely hovering at a position and tracking of reference trajectory. A decentralized control of robotic arm and quadrotor is considered. The robotic arm is controlled by a PID approach with acceleration feedback, and the quadrotor is controlled by PD method in the inner loop and adaptive position control in the outer loop. The proposed method successfully handles the problem of hover stabilization and trajectory tracking.

Model Predictive Control for Spacecraft Rendezvous

Abstract Modeling and control of a Quadrotor with robotic arm which uses vision sensor is discussed. A quadrotor model coupled with a two link manipulator is first developed and then the integrated control mechanism is investigated. An Image Based Visual Servo system is introduced and then used with the aerial manipulator to successfully perform specific tasks of positioning and stabilization during manipulation.

Setting up a testbed for UAV vision based control using V-REP & ROS: A case study on aerial visual inspection

The current paper addresses the problem of Spacecraft Rendezvous using Model Predictive Control (MPC). The Clohessy-Wiltshire-Hill equations are used to model the spacecraft relative motion. Here the rendezvous problem is discussed by trajectory control using MPC method. Two different scenarios are addressed in trajectory control. The first scenario consist of position control with fuel constraint, secondly the position control is performed in the presence of obstacles. Here the problem of fuel consumption and obstacle avoidance is addressed directly in the cost function. The proposed methods are successfully analysed through simulations.

Operational Space Control of a Lightweight Robotic Arm Actuated By Shape Memory Alloy (SMA) Wires

This paper focuses on the use of the Virtual Robotics Experimental Platform (V-REP) and the Robotics Operative System (ROS) working in parallel for design, test, and tuning of a vision based control system to command an Unmanned Aerial Vehicle (UAV). Here, is presented how to configure the V-REP and ROS to work in parallel, and the developed software in ROS for the pose estimation based on vision and for the design and use of a fuzzy logic control system. It is also explained how to interact with a virtual and a real quadrotor (QR) to command it for the specific task of aerial visual inspection task. The control system approach presented in this work is based on three fuzzy logic controllers (FLC) working in parallel on an external control loop based on the visual information. The three controllers were designed and tuned to command the vertical, longitudinal and lateral velocities of the UAV. The task to accomplish by the control system is to modify the position of the UAV in real time for the visual inspection of an object or specific parts of a structure. The virtual environment of the V-REP was used to tune manually the control system. Finally, the behavior of the tuned controllers was validated by a set of tests in a real environment with a quadrotor.

Area exploration with a swarm of UAVs combining deterministic chaotic ant colony mobility with position MPC

This paper presents the design and control of a two link lightweight robotic arm using a couple of antagonistic Shape Memory Alloy (SMA) wires as actuators. A nonlinear robust control law for accurate positioning of the end effector of the twolink SMA based robotic arm is developed to handle the hysteresis behavior present in the system. The model presented consists of two subsystems: firstly the SMA wires model and secondly the dynamics of the robotic arm itself. The control objective is to position the robotic arm’s end effector in a given operational plane position. For this regulation problem a sliding mode control law is applied to the hysteretic system. Finally a Lyapunov analysis is applied to the closed-loop system demonstrating the stability of the system under given conditions. The simulation results demonstrate the accurate and fast response of the control law for position regulation. In addition, the stability of the closed-loop system can be corroborated.