Control strategy for cooperating disparate manipulators

To manipulate large payloads typical of space construction, the concept of a small arm mounted on the end of a large arm is introduced. The main purposes of such a configuration are to increase the structural stiffness of the robot by bracing against or locking to a stationary frame, and to maintain a firm position constraint between the robot's base and workpieces by grasping them. Possible topologies for a combination of disparate large and small arms are discussed, and kinematics, dynamics, controls, and coordination of the two arms, especially when they brace at the tip of the small arm, are developed. The feasibility and improvement in performance are verified, not only with analytical work and simulation results but also with experiments on the existing arrangement Robotic Arm Large and Flexible and Small Articulated Manipulator

Nonlinear feedback control of multiple robot arms

Multiple coordinated robot arms are modeled by considering the arms: (1) as closed kinematic chains, and (2) as a force constrained mechanical system working on the same object simultaneously. In both formulations a new dynamic control method is discussed. It is based on a feedback linearization and simultaneous output decoupling technique. Applying a nonlinear feedback and a nonlinear coordinate transformation, the complicated model of the multiple robot arms in either formulation is converted into a linear and output decoupled system. The linear system control theory and optimal control theory are used to design robust controllers in the task space. The first formulation has the advantage of automatically handling the coordination and load distribution among the robot arms. In the second formulation, by choosing a general output equation, researchers can superimpose the position and velocity error feedback with the force-torque error feedback in the task space simultaneously

AWARE: Platform for Autonomous self-deploying and operation of Wireless sensor-actuator networks cooperating with unmanned AeRial vehiclEs

This paper presents the AWARE platform that seeks to enable the cooperation of autonomous aerial vehicles with ground wireless sensor-actuator networks comprising both static and mobile nodes carried by vehicles or people. Particularly, the paper presents the middleware, the wireless sensor network, the node deployment by means of an autonomous helicopter, and the surveillance and tracking functionalities of the platform. Furthermore, the paper presents the first general experiments of the AWARE project that took place in March 2007 with the assistance of the Seville fire brigades

Multi-robot cooperation

Abstract. This bachelor’s thesis familiarizes with multi-robot cooperation. The main interest is in two robot manipulators. This thesis is a literature review. The operation of the robot and the phenomena that act on them while in operation are investigated from kinematics and command architecture point of view. This thesis is based on manuals from two KUKA robots from University of Oulu, so in the future the use and understanding of their cooperation would be easier. The results gave good understanding of robot software calculations for trajectories and geometrics and what other has to consider when controlling a multi-robot system. This is a good base for deeper theoretical research for robot system software and practical testing.Usean robotin yhteiskäyttö. Tiivistelmä. Tässä opinnäytetyössä perehdytään usean robotin yhteiskäyttöön, jossa mielenkiinnon kohteena on kahden robottikäden yhteistoiminta. Työ on kirjallisuuskatsaus. Robottien toimintaa ja niihin vaikuttavia asioita tarkastellaan niin kinematiikan, kuin ohjelmisto- ja käskyarkkitehtuurin kautta. Työn pohjana käytettiin yliopistolla olevien KUKA robottikäsien oppaita, jotta jatkossa niiden yhteiskäyttö olisi helpommin ymmärrettävissä. Työn tulokset avasivat sitä, miten robottien ohjelmisto ohjaa ja laskee tarvittavat liikeradat ja geometriat ja mitä kaikkea usean robotin ohjauksessa pitää ottaa huomioon. Tämä on hyvä pohja syvemmälle teoreettiselle robottiohjelmistolle tai käytännön testaamiselle

Efficient exploration of unknown indoor environments using a team of mobile robots

Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels

INTELLIGENT CONTROLLING THE GRIPPING FORCE OF AN OBJECT BY TWO COMPUTER-CONTROLLED COOPERATIVE ROBOTS

This paper presents a Multiple Adaptive Neuro-Fuzzy Inference System (MANFIS)-based method for regulating the handling force of a common object. The foundation of this method is the prediction of the inverse dynamics of a cooperative robotic system made up of two 3-DOF robotic manipulators. Considering the no slip in contact between the tool and the object, an object is moved. to create and feed the MANFIS database, the inverse kinematics and dynamic equations of motion for the closed chain of motion for both arms are established in Matlab. Results from a SimMechanic simulation are given to demonstrate how well the suggested ANFIS controller works. Several manipulated object movements covering the shared workspace of the two manipulator arms are used to test the proposed control strategy

An Approach to Simultaneous Control of Trajectory and Interaction Forces in Dual-Arm Configurations

Multiple arm systems, multifingered grippers, and walking vehicles all have two common features. In each case, more than one actively coordinated articulation interacts with a passive object, thus forming one or more closed chains. For example, when two arms grasp an object simultaneously, the arms together with the object and the ground (base) form a closed chain. This induces kinematic and dynamic constraints and the resulting equations of motion are extremely nonlinear and coupled. Furthermore, the number of actuators exceeds the kinematic mobility of the chain in a typical case, which results in an underdetermined system of equations. An approach to control such constrained dynamic systems is described in this short paper. The basic philosophy is to utilize a minimal set of inputs to control the trajectory and the surplus inputs to control the constraint or interaction forces and moments in the closed chain. A dynamic control model is derived for the closed chain that is suitable for designing a controller, in which the trajectory as well as the interaction forces and moments are explicitly controlled. Nonlinear feedback techniques derived from differential geometry are then applied to linearize and decouple the nonlinear model. In this paper, these ideas are illustrated through a planar example in which two arms are used for cooperative manipulation. Results from a simulation are used to illustrate the efficacy of the method