Mobile manipulators collision-free trajectory planning with regard to end-effector vibrations elimination

A sub-optimal point-to-point trajectory planning method for mobile manipulators operating in the workspace including obstacles taking into account the damping of the end-effector vibrations is presented. The proposed solution is based on extended Jacobian approach and redundancy resolution at the acceleration level. Fulfilment of the condition stopping the mobile manipulator at the destination point is guaranteed, which leads to elimination of the end-effector vibrations and significantly increases positioning accuracy. The effectiveness of the presented method is shown and compared to the classical Jacobian pseudo inverse approach. A computer example involving a mobile manipulator consisting of a nonholonomic platform (2, 0) class and SCARA-type holonomic manipulator operating in two-dimensional task space including obstacle is also presented

Handling of large and heavy objects using a single mobile manipulator in combination with a roller board

This paper presents a method for autonomous loading, transportation, and unloading of large objects using a nonholonomic mobile manipulator. Here, the size of the transported object is considerably larger than the size of the mobile platform, which is made possible through the use of a roller board. In this way, the mobile manipulator can handle objects that exceed the manipulator's payload. The robot can load and unload the object onto its platform using the differential kinematics of the system for a null space motion to maintain the object's position in space. In order to localise the object, we apply 3D-perception using a depth-camera. While transporting the object to its destination, the robot is considered a tractor-trailer-wheeled system and can navigate using SLAM. Kinematic modelling and practical evaluation prove that the system can potentially take over arduous transportation tasks

Model predictive control and trajectory optimization of large vehicle-manipulators

In this paper, a model predictive control (MPC) is developed for on- and off-road mid-sized heavy duty vehicle-manipulator systems with a hydraulic working arm. The proposed concept for the control model is also new in the sense of working only within a local reference coordinate-system relative to the reference trajectory (so-called Fren´et-System). The control model only needs the errors to the reference trajectory. In contrast to other state-of-the-art approaches, there is no global localization method necessary. The control model is kept as simple as possible, to allow real-time motion prediction of the real system. For this reason, a kinematic model is used in the MPC which consists of a bicycle model and a planar robotic arm with two control variables. The dynamics of the overall system are considered as optimization constraints, assuming that the optimized system inputs and states are kinetically and dynamically feasible. Through this control method, the dual-trajectories are also optimized and they provide smooth motions for the overall system. The underlying control of the robotic arm is realized with a proportional–integral–derivative (PID) controller with feedback linearization and gravity compensation. The control algorithm is tested and validated in a MATLAB/Simulink simulation environment

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

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

Control of Cooperating Mobile Manipulators

We describe a framework and control algorithms for coordinating multiple mobile robots with manipulators focusing on tasks that require grasping, manipulation and transporting large and possibly flexible objects without special purpose fixtures. Because each robot has an independent controller and is autonomous, the coordination and synergy are realized through sensing and communication. The robots can cooperatively transport objects and march in a tightly controlled formation, while also having the capability to navigate autonomously. We describe the key aspects of the overall hierarchy and the basic algorithms, with specific applications to our experimental testbed consisting of three robots. We describe results from many experiments that demonstrate the ability of the system to carry flexible boards and large boxes as well as the system’s robustness to alignment and odometry errors

Control of Cooperating Mobile Manipulators

We describe a framework and control algorithms for coordinating multiple mobile robots with manipulators focusing on tasks that require grasping, manipulation and transporting large and possibly flexible objects without special purpose fixtures. Because each robot has an independent controller and is autonomous, the coordination and synergy are realized through sensing and communication. The robots can cooperatively transport objects and march in a tightly controlled formation, while also having the capability to navigate autonomously. We describe the key aspects of the overall hierarchy and the basic algorithms, with specific applications to our experimental testbed consisting of three robots. We describe results from many experiments that demonstrate the ability of the system to carry flexible boards and large boxes as well as the system’s robustness to alignment and odometry errors

Energy-saver mobile manipulator based on numerical methods

The work presents the kinematic and dynamic control of a mobile robotic manipulator system based on numerical methods. The proposal also presents the curvature analysis of a path not parameterized in time, for the optimization of energy consumption. The energy optimization considers two aspects: the velocity of execution in curves and the amount of movements generated by the robotic system. When a curve occurs on the predefined path, the execution velocity is analyzed throughout the system in a unified method to prevent skid e ects from a ecting the mobile manipulator, while the number of movements is limited by the redundancy presented by the robotic system to optimize energy use. The experimental results are shown to validate the mechanical and electronic construction of the system, the proposed controllers, and the saving of energy consumptionThis research was funded by Corporación Ecuatoriana para el Desarrollo de la Investigación y Academia–CEDI