Mehran Mehrandezh

Robotic interception of moving objects using an augmented ideal proportional navigation guidance technique

Presents an approach to online, robot-motion planning for moving-object interception. The proposed approach utilizes a navigation-guidance-based technique, that is robust and computationally efficient for the interception of fast-maneuvering objects. Navigation-based techniques were originally developed for the control of missiles tracking free-flying targets. Unlike a missile, however, the end-effector of a robotic arm is connected to the ground, via a number of links and joints, subject to kinematic and dynamic constraints. Also, unlike a missile, the velocity of the robot and the moving object must be matched for a smooth grasp, thus, a hybrid interception scheme, which combines a navigation-based interception technique with a conventional trajectory tracking method is proposed herein for intercepting fast-maneuvering objects. The implementation of the proposed technique is illustrated via numerous simulation examples.

Global path planning for robust Visual Servoing in complex environments

We incorporate sampling-based global path planning with Visual Servoing (VS) for a robotic arm equipped with an in-hand camera. The path planning accounts for a number of constraints: 1) maintaining continuous visibility of the target within the cameras field of view, 2) avoiding visual occlusion of target features caused by the workspace obstacles, robots body, or the target itself, 3) avoiding collision with physical obstacles or self collision, and 4) joint limits. Incorporating these constraints enhances the applicability of VS to significantly more complex environments/tasks, thereby making the resulting VS much more robust. The proposed planner explores the camera space, i.e. 3D Cartesian space, for permissible camera paths satisfying the aforementioned constraints by iteratively extending a search tree in camera space and simultaneously tracking these paths in the robots joint space using a local planner. The planned camera path is then projected into the image space and tracked using an image-based visual servoing scheme. The validity and effectiveness of the proposed approach in accomplishing VS tasks in complex environments are demonstrated through a number of simulations on a 6-dof robot arm moving among obstacles.

Path-Planning for Visual Servoing: A Review and Issues

In this survey we provide a comprehensive technical review of existing major approaches to path-planning for visual servoing. Visual servoing has been introduced as a promising approach for sensor-based robotic tasks. The basic visual servoing task is to guide the motion of a robot with respect to a target object based on the feedback obtained through a vision system. Amalgamation of path-planning techniques with reactive visual servoing strategies can robustify existing image-based tracking systems in robotics applications where a high disparity between the initial and desired views of a target is inevitable (e.g., target interception, space docking, reaching and grasping, etc). The planning stage does so by accounting for critical constraints and uncertainties in the system resulting in a more robust visual servoing process. We discuss different planning approaches, explain the associated set of constraints and assumptions, and discuss the underlying pathplanning techniques along with the issues regarding their integration with reactive visual servo controllers.

Dynamic analysis and human analogous control of a pipe crawling robot

In this paper the design and development of a crawling robot for inspection of live water pipes are addressed. The mechanical design of the robot is described in detail. The governing dynamics equations of the robot moving against water flow as well as gravity in a straight pipe are also derived. Specifically, the hydrodynamic forces exerted on the robot when moving in a live pressurized pipe are taken into account. Two fuzzy-logic based control strategies are adopted. The first one is to maintain a constant translational speed in robots motion when subjected to flow disturbances that are numerically modeled using step changes in flow velocity within a human-in-the-loop real-time simulation environment, and the second is to steer the real robot inside the pipe while following a numerically modeled time-varying velocity set point with no fluid present in the pipe. The controller parameters were tuned based on data obtained from a human-in-the-loop control system via an artificial neural network.

An Incremental Harmonic Function-based Probabilistic Roadmap Approach to Robot Path Planning

A new hybrid motion planning technique based on Harmonic Functions (HF) and Probabilistic Roadmaps (PRM) is presented. The proposed approach consists of incrementally building a Probabilistic Roadmap using information obtained about the workspace topology through the Fluid Dynamic (FD) paradigm based on HFs. The crux of our approach is to identify narrow passages using FD paradigm and pass the information obtained over to a PRM method to build a roadmap to capture the connectivity of free configuration space (C-space) especially in narrow regions. As an extension to our recent works on using Harmonic Function-based Probabilistic Roadmaps (HFPRM) for robotic navigation [1], we propose an Incremental HFPRM (IHFPRM) technique which is more general and can be applied to virtually any type of robot. Simulation results presented in this paper show that the combination of the HF and the PRM works better than each individual in terms of finding a collision free path in environments where narrow passages exist. This technique can be extended to the sensor-based motion planning of robots (mobile and/or articulated) which is the long-term objective in carrying out this research.

Robotic interception of moving objects using ideal proportional navigation guidance technique

Abstract This paper presents a novel approach to on-line, robot-motion planning for moving-object interception. The proposed approach utilizes a navigation-based technique, which is robust and computationally efficient for the interception of fast-maneuvering objects. Navigation-based techniques were originally developed for the control of missiles tracking free-flying targets. Unlike a missile, however, the end-effector of a robotic arm is connected to the ground, via a number of links and joints, subject to kinematic and dynamic constraints. Also, unlike a missile, the velocity of the robot and the moving object must be matched for a smooth grasp , thus, a hybrid interception scheme, which combines a navigation-based interception technique with a conventional trajectory tracking method is proposed herein for intercepting fast-maneuvering objects. The implementation of the proposed technique is discussed via numerous simulation examples.

Path planning for image-based control of wheeled mobile manipulators

We address the problem of incorporating path planning with image-based control of a wheeled mobile manipulator (WMM) performing visually-guided tasks in complex environments. The WMM consists of a wheeled (non-holonomic) mobile platform and an on-board robotic arm equipped with a camera mounted at its end-effector. The visually-guided task is to move the WMM from an initial to a desired location while respecting image and physical constraints. We propose a kinodynamic planning approach that explores the camera state space for permissible trajectories by iteratively extending a search tree in this space and simultaneously tracking these trajectories in the WMM configuration space. We utilize weighted pseudo-inverse Jacobian solutions combined with a null space optimization technique to effectively coordinate the motion of the mobile platform and the arm. We also present the preliminary results obtained by executing the planned trajectories on a real WMM system via a decoupled control scheme where the on-board arm is servo controlled along the planned feature trajectories while the mobile platform is simultaneously controlled along its trajectory using a state feedback tracking method.

Sensor-based robot path planning using harmonic function-based probabilistic roadmaps

We present a new sensor-based robot motion planning framework for mobile robot navigation in unknown environments. The main idea of the proposed planning approach, inspired by our recent works on using harmonic function-based probabilistic roadmaps (HFPRM) for robotic navigation in known environments (model-based cases) (Kazemi et al., 2004, 2005), is to utilize a fluid dynamic (FD) paradigm based on potential flows to identify and prioritize critical regions, i.e. narrow passages and hard-to-navigate regions, at the scan planning stage of a sensor-based probabilistic roadmap (PRM). The PRM, which efficiently captures the connectivity of the free space, is incrementally expanded as the robot senses the physical workspace. Computer simulations and experimental results obtained using a mobile robot equipped with ultrasonic range finders are presented

An eye-to-hand visual servoing structure for 3D positioning of a robotic arm using one camera and a flat mirror

This paper introduces a novel visual servoing structure for 3D robot positioning under an eye-to-hand camera configuration. The proposed algorithm is based on image-based visual servoing (IBVS) using only one camera in conjunction with a flat mirror. A landmark mounted on the robot along with its mirror reflection, when viewed by the camera, provides enough information for 3D reasoning based on a 2D image. The governing equations describing the relationship between the robots velocity and rate of change in image features are fully described. Furthermore, a methodology for on-line estimation of the image Jacobian with no camera calibration is developed. Simulation and experimental results illustrate the robustness of the proposed visual servoing structure.

Robotic navigation using harmonic function-based probabilistic roadmaps

This paper presents a new hybrid motion planning technique based on harmonic functions (HF) and probabilistic roadmaps (PRM). The proposed harmonic function based probabilistic roadmap (HFPRM) method comprises three phases: in phase one, the Laplaces equation, pertinent to potential flow, in an environment cluttered with obstacles is solved. In phase two, a probabilistic roadmap with a novel sampling scheme is constructed based on information obtained about the environment topology through the HF technique developed in phase one. The roadmap is then searched for the shortest path in phase three. Simulation results presented in this paper show that the combination of the HF and the PRM works better than each individual in terms of finding a collision free path in environments where narrow passages exist. The proposed HFPRM method can be extended to sensor-based motion planning problem in environments not known a priori.