Online sensor information and redundancy resolution based obstacle avoidance for high DOF mobile manipulator teleoperation

High degrees of freedom (DOF) mobile manipulators provide more flexibility than conventional manipulators. They also provide manipulation operations with a mobility capacity and have potential in many applications. However, due to high redundancy, planning and control become more complicated and difficult, especially when obstacles occur. Most existing obstacle avoidance methods are based on off-line algorithms and most of them mainly focus on planning a new collision-free path, which is not appropriate for some applications, such as teleoperation and uses many system resources as well. Therefore, this paper presents an online planning and control method for obstacle avoidance in mobile manipulators using online sensor information and redundancy resolution. An obstacle contour reconstruction approach employing a mobile manipulator equipped with an active laser scanner system is also introduced in this paper. This method is implemented using a mobile manipulator with a seven-DOF manipulator and a four-wheel drive mobile base. The experimental results demonstrate the effectiveness of this method. © 2013 Zhang et al.; licensee InTech.Link_to_subscribed_fulltex

Contact aware robust semi-autonomous teleoperation of mobile manipulators

In the context of human-robot collaboration, cooperation and teaming, the use of mobile manipulators is widespread on applications involving unpredictable or hazardous environments for humans operators, like space operations, waste management and search and rescue on disaster scenarios. Applications where the manipulator's motion is controlled remotely by specialized operators. Teleoperation of manipulators is not a straightforward task, and in many practical cases represent a common source of failures. Common issues during the remote control of manipulators are: increasing control complexity with respect the mechanical degrees of freedom; inadequate or incomplete feedback to the user (i.e. limited visualization or knowledge of the environment); predefined motion directives may be incompatible with constraints or obstacles imposed by the environment. In the latter case, part of the manipulator may get trapped or blocked by some obstacle in the environment, failure that cannot be easily detected, isolated nor counteracted remotely. While control complexity can be reduced by the introduction of motion directives or by abstraction of the robot motion, the real-time constraint of the teleoperation task requires the transfer of the least possible amount of data over the system's network, thus limiting the number of physical sensors that can be used to model the environment. Therefore, it is of fundamental to define alternative perceptive strategies to accurately characterize different interaction with the environment without relying on specific sensory technologies. In this work, we present a novel approach for safe teleoperation, that takes advantage of model based proprioceptive measurement of the robot dynamics to robustly identify unexpected collisions or contact events with the environment. Each identified collision is translated on-the-fly into a set of local motion constraints, allowing the exploitation of the system redundancies for the computation of intelligent control laws for automatic reaction, without requiring human intervention and minimizing the disturbance of the task execution (or, equivalently, the operator efforts). More precisely, the described system consist in two different building blocks. The first, for detecting unexpected interactions with the environment (perceptive block). The second, for intelligent and autonomous reaction after the stimulus (control block). The perceptive block is responsible of the contact event identification. In short, the approach is based on the claim that a sensorless collision detection method for robot manipulators can be extended to the field of mobile manipulators, by embedding it within a statistical learning framework. The control deals with the intelligent and autonomous reaction after the contact or impact with the environment occurs, and consist on an motion abstraction controller with a prioritized set of constrains, where the highest priority correspond to the robot reconfiguration after a collision is detected; when all related dynamical effects have been compensated, the controller switch again to the basic control mode

Control of an anthropomorphic manipulator involved in physical human-robot interaction

Dissertação de mestrado em Engenharia MecânicaThe objective of the dissertation is to flexibly control the end effector velocity of a redundant 7-DOF manipulator by using a differential kinematics approach, while ensuring the safety of the robotic arm from exceeding the physical limits of joints in terms of position, velocity and acceleration. The thesis also contributes with a real-time obstacle avoidance strategy for controlling anthropomorphic robotic arms in dynamic obstacle environments, taking account of sudden appearances or disappearances of mobile obstacles. A method for compensating force errors due to changes in the orientation of end effectors, independent from structures of force sensors, is developed to achieve high accuracy in force control applications. A novel method, the Virtual Elastic System, is proposed to control mobile manipulators for physical Human-Robot Interaction (pHRI) tasks in dynamic environments, which enables the combination of an Inverse Differential Kinematics for redundant robotic arms and a Dynamical Systems approach for nonholonomic mobile platforms. Experiments with a 7-DOF robotic arm, side-mounted on a nonholonomic mobile platform, are presented with the whole robot obstacle avoidance, proving the efficiency of the developed method in pHRI scenarios, more specifically, cooperative human-robot object transportation tasks in dynamic environments. Extensions of the method for other mobile manipulators with holonomic mobile platforms or higher degrees of freedom manipulators are also demonstrated through simulations

Repeatable Motion Planning for Redundant Robots over Cyclic Tasks

We consider the problem of repeatable motion planning for redundant robotic systems performing cyclic tasks in the presence of obstacles. For this open problem, we present a control-based randomized planner, which produces closed collision-free paths in configuration space and guarantees continuous satisfaction of the task constraints. The proposed algorithm, which relies on bidirectional search and loop closure in the task-constrained configuration space, is shown to be probabilistically complete. A modified version of the planner is also devised for the case in which configuration-space paths are required to be smooth. Finally, we present planning results in various scenarios involving both free-flying and nonholonomic robots to show the effectiveness of the proposed method

Challenges and Solutions for Autonomous Robotic Mobile Manipulation for Outdoor Sample Collection

In refinery, petrochemical, and chemical plants, process technicians collect uncontaminated samples to be analyzed in the quality control laboratory all time and all weather. This traditionally manual operation not only exposes the process technicians to hazardous chemicals, but also imposes an economical burden on the management. The recent development in mobile manipulation provides an opportunity to fully automate the operation of sample collection. This paper reviewed the various challenges in sample collection in terms of navigation of the mobile platform and manipulation of the robotic arm from four aspects, namely mobile robot positioning/attitude using global navigation satellite system (GNSS), vision-based navigation and visual servoing, robotic manipulation, mobile robot path planning and control. This paper further proposed solutions to these challenges and pointed the main direction of development in mobile manipulation

Selected topics in robotics for space exploration

Papers and abstracts included represent both formal presentations and experimental demonstrations at the Workshop on Selected Topics in Robotics for Space Exploration which took place at NASA Langley Research Center, 17-18 March 1993. The workshop was cosponsored by the Guidance, Navigation, and Control Technical Committee of the NASA Langley Research Center and the Center for Intelligent Robotic Systems for Space Exploration (CIRSSE) at RPI, Troy, NY. Participation was from industry, government, and other universities with close ties to either Langley Research Center or to CIRSSE. The presentations were very broad in scope with attention given to space assembly, space exploration, flexible structure control, and telerobotics

Visual Control with Adaptive Dynamical Compensation for 3D Target Tracking by Mobile Manipulators

In this paper an image-based dynamic visual feedback control for mobile manipulators is presented to solve the target tracking problem in the 3D-workspace. The design of the whole controller is based on two cascaded subsystems: a minimum norm visual kinematic controller which complies with the 3D target tracking objective, and an adaptive controller that compensates the dynamics of the mobile manipulator. Both the kinematic controller and the adaptive controller are designed to prevent from command saturation. Robot commands are defined in terms of reference velocities. Stability and robustness are proved by using Lyapunov’s method. Finally, experimental results are presented to confirm the effectiveness of the proposed visual feedback controller.Fil: Andaluz, Víctor. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Salinas, Lucio Rafael. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Toibero, Juan Marcos. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Roberti, Flavio. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

\u3cem\u3eGRASP News\u3c/em\u3e, Volume 8, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory. Edited by Thomas Lindsay

GRASP News Volume 9, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory