![]() ![]() ![]() (2016), from the perspective of game theory, and a distributed recurrent neural network (RNN)-based dynamic controller was proposed for the coordination control of multi-robot system. Series of related products have been reported, e.g., in Li et al. Due to its degrees of freedom (DOF) exceeding ones required by the robot to complete the given tasks, a redundant manipulator shows better flexibility, multifunction, and wide universality than the traditional non-redundant robot.Īs a fundamental problem in robotic control, the kinematic motion planning problem of the redundant manipulator has already been widely investigated in recent years. Simultaneously, the high demand on the execution abilities of a robot manipulator working in complicated environment also poses a challenge to robotic control. ![]() With the advances of society, ranging from industry to military, home furnishing, service, medical treatment, etc., robot technology has already become gradually mature. Then, simulation experiments performing on a four-link planar manipulator validate the feasibility and effectiveness of the proposed scheme. To solve the resultant QP minimization problem, a recurrent neural network based neural dynamic solver is proposed. Considering the physical structure of robot manipulators, inherent joint angle, speed, and acceleration limits are also incorporated. From the perspective of optimization, therefore, an acceleration level quadratic programming (QP) problem is eventually formulated. To avoid the joint drift phenomenon of the manipulator, bi-criteria performance indices integrating joint-acceleration-norm minimization and repetitive motion planning is adopted by assigning a weighing factor. The distance between the manipulator and an obstacle is described as the point-to-point distance, and the collision avoidance strategy is formulated as an inequality. Specifically, the manipulator and obstacle are abstracted as mathematical geometries, based on the vector relationship between geometric elements, and the Cartesian coordinate of the nearest point to an obstacle on a manipulator can be found. In this paper, an improved obstacle-avoidance-scheme-based kinematic control problem in acceleration level for a redundant robot manipulator is investigated. 2Guangdong Key Laboratory of Modern Control Technology, Guangdong Institute of Intelligent Manufacturing, Guangzhou, China.1Foshan Longshen Robotics LTD., Foshan, China.BasiliskII_src_ Source tarball, Release 0.Weifeng Zhao 1, Xiaoxiao Li 2, Xin Chen 1, Xin Su 1 and Guanrong Tang 2 *.Basilisk II package for Solaris 10 SPARC, provided by Luc Pauwels.Other prepackaged versions of Basilisk II that I am aware of: Uses UAE 68k emulation or (under AmigaOS and NetBSD/m68k) real 68k processorĭownload Basilisk II Precompiled binariesįor announcements of prebuilt binaries for Linux, Mac OS X, and Windows,.Emulates extended ADB keyboard and 3-button mouse.Easy file exchange with the host OS via a "Host Directory Tree" icon on the Mac desktop.CD-ROM driver with basic audio functions.Driver for HFS partitions and hardfiles.Floppy disk driver (only 1.44MB disks supported).Emulates either a Mac Classic (which runs MacOS 0.x thru 7.5) or a Mac II series machine (which runs MacOS 7.x, 8.0 and 8.1), depending on the ROM being used.If you are interested in learning how Basilisk II works internally, there isĪvailable (knowledge about programming and computer architecture is required).īasilisk II has been ported to the following systems: The terms of the GNU General Public License (GPL).įor more information, see the README file. However, you still need a copy of MacOS andĪ Macintosh ROM image to use Basilisk II. You to run 68k MacOS software on your computer, even if you are using aĭifferent operating system. Basilisk II is an Open Source 68k Macintosh emulator. ![]()
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