Robust hovering controller for uncertain multirotor micro aerial vehicles (MAVS) in gps-denied environments: IMAGE-BASED

This paper proposes an image-based robust hovering controller for multirotor micro aerial vehicles (MAVs) in GPS-denied environments. The proposed controller is robust against the effects of multiple uncertainties in angular dynamics of vehicle which contain external disturbances, nonlinear dynamics, coupling, and parametric uncertainties. Based on visual features extracted from the image, the proposed controller is capable of controlling the pose (position and orientation) of the multirotor relative to the fixed-target. The proposed controller scheme consists of two parts: a spherical image-based visual servoing (IBVS) and a robust flight controller for velocity and attitude control loops. A robust compensator based on a second order robust filter is utilized in the robust flight control design to improve the robustness of the multirotor when subject to multiple uncertainties. Compared to other methods, the proposed method is robust against multiple uncertainties and does not need to keep the features in the field of view. The simulation results prove the effectiveness and robustness of the proposed controller

Robust visual servoing in 3d reaching tasks

This paper describes a novel approach to the problem of reaching an object in space under visual guidance. The approach is characterized by a great robustness to calibration errors, such that virtually no calibration is required. Servoing is based on binocular vision: a continuous measure of the end-effector motion field, derived from real-time computation of the binocular optical flow over the stereo images, is compared with the actual position of the target and the relative error in the end-effector trajectory is continuously corrected. The paper outlines the general framework of the approach, shows how visual measures are obtained and discusses the synthesis of the controller along with its stability analysis. Real-time experiments are presented to show the applicability of the approach in real 3-D applications

Aerial-Ground collaborative sensing: Third-Person view for teleoperation

Rapid deployment and operation are key requirements in time critical application, such as Search and Rescue (SaR). Efficiently teleoperated ground robots can support first-responders in such situations. However, first-person view teleoperation is sub-optimal in difficult terrains, while a third-person perspective can drastically increase teleoperation performance. Here, we propose a Micro Aerial Vehicle (MAV)-based system that can autonomously provide third-person perspective to ground robots. While our approach is based on local visual servoing, it further leverages the global localization of several ground robots to seamlessly transfer between these ground robots in GPS-denied environments. Therewith one MAV can support multiple ground robots on a demand basis. Furthermore, our system enables different visual detection regimes, and enhanced operability, and return-home functionality. We evaluate our system in real-world SaR scenarios.Comment: Accepted for publication in 2018 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR

Learning visual docking for non-holonomic autonomous vehicles

This paper presents a new method of learning visual docking skills for non-holonomic vehicles by direct interaction with the environment. The method is based on a reinforcement algorithm, which speeds up Q-learning by applying memorybased sweeping and enforcing the “adjoining property”, a filtering mechanism to only allow transitions between states that satisfy a fixed distance. The method overcomes some limitations of reinforcement learning techniques when they are employed in applications with continuous non-linear systems, such as car-like vehicles. In particular, a good approximation to the optimal behaviour is obtained by a small look-up table. The algorithm is tested within an image-based visual servoing framework on a docking task. The training time was less than 1 hour on the real vehicle. In experiments, we show the satisfactory performance of the algorithm

Effective Target Aware Visual Navigation for UAVs

In this paper we propose an effective vision-based navigation method that allows a multirotor vehicle to simultaneously reach a desired goal pose in the environment while constantly facing a target object or landmark. Standard techniques such as Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) in some cases (e.g., while the multirotor is performing fast maneuvers) do not allow to constantly maintain the line of sight with a target of interest. Instead, we compute the optimal trajectory by solving a non-linear optimization problem that minimizes the target re-projection error while meeting the UAV's dynamic constraints. The desired trajectory is then tracked by means of a real-time Non-linear Model Predictive Controller (NMPC): this implicitly allows the multirotor to satisfy both the required constraints. We successfully evaluate the proposed approach in many real and simulated experiments, making an exhaustive comparison with a standard approach.Comment: Conference paper at "European Conference on Mobile Robotics" (ECMR) 201

Visual servoing of a car-like vehicle - an application of omnidirectional vision

In this paper, we develop the switching controller presented by Lee et al. for the pose control of a car-like vehicle, to allow the use of an omnidirectional vision sensor. To this end we incorporate an extension to a hypothesis on the navigation behaviour of the desert ant, cataglyphis bicolor, which leads to a correspondence free landmark based vision technique. The method we present allows positioning to a learnt location based on feature bearing angle and range discrepancies between the robot's current view of the environment, and that at a learnt location. We present simulations and experimental results, the latter obtained using our outdoor mobile platform

Automated pick-up of suturing needles for robotic surgical assistance

Robot-assisted laparoscopic prostatectomy (RALP) is a treatment for prostate cancer that involves complete or nerve sparing removal prostate tissue that contains cancer. After removal the bladder neck is successively sutured directly with the urethra. The procedure is called urethrovesical anastomosis and is one of the most dexterity demanding tasks during RALP. Two suturing instruments and a pair of needles are used in combination to perform a running stitch during urethrovesical anastomosis. While robotic instruments provide enhanced dexterity to perform the anastomosis, it is still highly challenging and difficult to learn. In this paper, we presents a vision-guided needle grasping method for automatically grasping the needle that has been inserted into the patient prior to anastomosis. We aim to automatically grasp the suturing needle in a position that avoids hand-offs and immediately enables the start of suturing. The full grasping process can be broken down into: a needle detection algorithm; an approach phase where the surgical tool moves closer to the needle based on visual feedback; and a grasping phase through path planning based on observed surgical practice. Our experimental results show examples of successful autonomous grasping that has the potential to simplify and decrease the operational time in RALP by assisting a small component of urethrovesical anastomosis

Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary

The complex physical properties of highly deformable materials such as clothes pose significant challenges fanipulation systems. We present a novel visual feedback dictionary-based method for manipulating defoor autonomous robotic mrmable objects towards a desired configuration. Our approach is based on visual servoing and we use an efficient technique to extract key features from the RGB sensor stream in the form of a histogram of deformable model features. These histogram features serve as high-level representations of the state of the deformable material. Next, we collect manipulation data and use a visual feedback dictionary that maps the velocity in the high-dimensional feature space to the velocity of the robotic end-effectors for manipulation. We have evaluated our approach on a set of complex manipulation tasks and human-robot manipulation tasks on different cloth pieces with varying material characteristics.Comment: The video is available at goo.gl/mDSC4