Motion Planning And Coordinated Control For Mobile Manipulators

Gao, Chunyan; Zhang, Minglu; Sun, Liang

doi:10.1109/icarcv.2006.345182

Cited by 3 publications

(3 citation statements)

References 10 publications

(8 reference statements)

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“…Without using standard redundancy resolution techniques, there are a variety of alternative methods of controlling a MMS. Research has been done to prove the possibility of using neural networks, [29][30][31] fuzzy logic, 32 distributed controllers, 33 resolved acceleration control, 34 or virtual impedance walls 35 to control a redundant system like a MMS. Some of these methods utilize the unique properties of a MMS to optimize its performance.…”

Section: Introductionmentioning

confidence: 99%

An automatic switching approach to teleoperation of mobile-manipulator systems using virtual fixtures

Wrock¹,

Nokleby²

2016

Robotica

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SUMMARYThis work presents a novel command strategy developed to improve operator performance and minimize difficulties in teleoperation tasks for mobile-manipulator systems with a holonomic base. Aimed specifically at novice operators, virtual fixtures are introduced as a means to minimize collisions and assist in navigation. Using the 6-degree-of-freedom (DOF) Omnibot mobile-manipulator system (MMS), a command strategy is implemented such that the operator need only control a 3-DOF haptic joystick to achieve full control of the Omnibot MMS. The command strategy is used to coordinate control between the arm and the base of the system, prevent collisions with known obstacles, and alert the operator of proximity to those obstacles with haptic forces. Through experimental testing it is shown that operator performance improved with the use of virtual fixtures.

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Section: Introductionmentioning

confidence: 99%

An automatic switching approach to teleoperation of mobile-manipulator systems using virtual fixtures

Wrock¹,

Nokleby²

2016

Robotica

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“…The RBF network used for motion planning in unstructured environment possesses the following salient features [ 27 – 29 ]: (1) It is a universal approximator and possess the best approximation property. It is capable of approximating any nonlinear functions with high precision.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning for Autonomous Vehicle Based on Radial Basis Function Neural Network in Unstructured Environment

Chen

Zhao

Liang

et al. 2014

Sensors

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The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for autonomous vehicles, due to the irregular shape of road, the requirement of real-time planning, and the nonholonomic constraints of vehicle. This paper presents a motion planning method, based on the Radial Basis Function (RBF) neural network, to guide the autonomous vehicle in unstructured environments. The proposed algorithm extracts the drivable region from the perception grid map based on the global path, which is available in the road network. The sample points are randomly selected in the drivable region, and a gradient descent method is used to train the RBF network. The parameters of the motion-planning algorithm are verified through the simulation and experiment. It is observed that the proposed approach produces a flexible, smooth, and safe path that can fit any road shape. The method is implemented on autonomous vehicle and verified against many outdoor scenes; furthermore, a comparison of proposed method with the existing well-known Rapidly-exploring Random Tree (RRT) method is presented. The experimental results show that the proposed method is highly effective in planning the vehicle path and offers better motion quality.

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“…Pin and Culioli 13 discuss a methodology that involves multi-criteria optimization. Gao et al 14 proposed a controller based on neural networks and implemented it on a mobile manipulator consisting of a 5-DOF arm. Lin and Goldenberg 15 proposed and implemented the use of two neural network controllers to control the manipulator and mobile base separately.…”

Section: Introductionmentioning

confidence: 99%

A Methodology for tele-operating mobile manipulators with an emphasis on operator ease of use

Frejek

Nokleby

2012

Robotica

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An algorithm for the tele-operation of mobile-manipulator systems with a focus on ease of use for the operator is presented. The algorithm allows for unified, intuitive, and coordinated control of mobile manipulators. It consists of three states. In the first state, a single 6-degrees-offreedom (DOF) joystick is used to control the manipulator's position and orientation. The second state occurs when the manipulator approaches a singular configuration, resulting in the mobile base moving in a manner so as to keep the end-effector travelling in its last direction of motion. This is done through the use of a constrained optimization routine. The third state is entered when the operator returns the joystick to the home position. Both the mobile base and manipulator move with respect to one another keeping the end-effector stationary and placing the manipulator into an ideal configuration. The algorithm has been implemented on an 8-DOF mobile manipulator and the test results show that it is effective at moving the system in an intuitive manner.

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Motion Planning And Coordinated Control For Mobile Manipulators

Cited by 3 publications

References 10 publications

An automatic switching approach to teleoperation of mobile-manipulator systems using virtual fixtures

An automatic switching approach to teleoperation of mobile-manipulator systems using virtual fixtures

Motion Planning for Autonomous Vehicle Based on Radial Basis Function Neural Network in Unstructured Environment

A Methodology for tele-operating mobile manipulators with an emphasis on operator ease of use

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