Robot motion

Author: g | 2025-04-24

Many situations could benefit from mapping human arm motions to robot arm motions. For example, a person could control a robot in real-time by having the robot mimic the operator’s arm motions or a person could teach a robot by providing an effective demonstration with their own hand in the robot’s workspace. While a mapping from human arm motion to robot arm motion In Course 4 of the specialization, Robot Motion Planning and Control, you will learn key concepts of robot motion generation: planning a motion for a robot in the presence of obstacles, and real

Robot Motion Diffusion Model: Motion Generation for Robotic

DELMIA RRS I DELMIA RRS enables the integration of proprietary motion software of actual robot controllers into DELMIA V5 robotic workcell simulation in a standardized manner to provide simulated robot motion accuracy levels not achievable by the simulation system's default motion planner. Product Overview DELMIA - Realistic Robot Simulation provides accurate robot cycle time estimates and motion trajectories in the DELMIA simulation system. Because RRS interfaces with the actual robot controller software, users obtain accurate motion profiles as the simulated robot performs its task. DELMIA - Realistic Robot Simulation is targeted at the automotive industry, which typically requires cycle time estimates within a 5 percent range of actual values. RRS efficiently and accurately validates robot motion behavior. Product Highlights DELMIA Realistic Robot Simulation offers the following functions: Integrates with native robot controller software Provides accurate motion planning for cycle-time analysis and collision detection Supports concurrent simulation of multiple robots and resources, such as positioning devices and human models Displays native robot error messages on computer screen Supports the Windows NT and Unix platforms using a client/server architecture Supports robots with external axes Provides accurate reachability studies through the use of RCS-based inverse kinematics DELMIA V5 RRS Interfaces share the same source code as the proven D5 RRS interfaces Product Key Customer Benefits Integrates with native robot controller software DELMIA - Realistic Robot Simulation currently supports the ABB S4C/S4C PLUS/IRC5, COMAU C3G/C4G, DUERR ECO RC2, FANUC RJ3-iA/RJ3-iB/R-30iA, HYUNDAI Hi4A, KUKA KRC, NACHI AR/AW/AX, and YASKAWA (Motoman) XRC/NX100 robot controller software (RCS) modules. RRS communicates with the same robot vendor supplied controller software used in the physical robots. This provides a high-degree of accuracy between the robot simulation viewed on a user's computer screen and the actual robot motion. Provides accurate motion planning for cycle-time analysis and collision detection Using DELMIA - Realistic Robot Simulation, users can accurately simulate a robot s motion and resolve any potential collisions between the robot and the various production elements. Dynamics related effects of payload on motion planning can only be accurately simulated using RRS. Supports concurrent simulation of multiple robots and resources, such as positioning devices and human models DELMIA - Realistic Robot Simulation supports concurrent simulation of multiple robots in complex workcells involving other resources, such as positioning devices and human models. This helps resolve any potential collisions between multiple robots and the various production elements. Displays native robot error messages on computer screen Because Realistic Robot Simulation communicates with the native robot controller software, the user will observe the robot's actual error messages at the exact same point in the simulation as would occur with the actual robot. This allows the detection of potential robot program errors before the actual download thereby avoiding

Human-Motion To Robot-Motion Remapping – People and Robots

China’s bionic robot replicates cheetah-like motion with innovative material technologyThe robot effectively replicated the running gait of cheetahs and demonstrated the ability to climb ramps.Updated: Feb 22, 2025 12:14 PM ESTIn a series of real-world experiments, the prototype showed promising results. (Representational image)Ociacia Roboticists and computer scientists have created a variety of systems inspired by humans and animals. The latest robot, introduced in a paper published in the Journal of Bionic Engineering, is designed using piezoelectric materials – a class of materials that generate an electric charge when exposed to mechanical stress.The piezoelectric robot achieves linear motion, turning motion, and turning motion with varying radii using a voltage differential driving method. A prototype was built, weighing 38 grams and measuring 150 × 80 × 31 mm³, researchers explained.Piezoelectric robot demonstrates linear and turning motion The new H-shaped bionic piezoelectric robot (H-BPR) consists of four legs connected by three piezoelectric beams. By harnessing the bending vibrations of the piezoelectric beams, the robot mimics the periodic leg movements of a cheetah’s running gait.The researchers analyzed the dynamics and kinematics of the piezoelectric robot to determine the trajectory of a point at the end of the robot’s leg. They then examined the motion principles of the robot, followed by modal and harmonic response analyses using finite element analysis software.“The performance test results show that the piezoelectric robot has a maximum velocity of 66.79 mm/s at an excitation voltage of 320 V and a load capacity of 55 g. In addition, the H-BPR with unequal drive legs has better climbing performance, and the obtained conclusions are informative for selecting leg heights for piezoelectric robots,” the researchers wrote in the abstract.Unlike other robots that rely on waves in piezoelectric materials for movement, the new system developed by these researchers features a simpler design, making it potentially easier to fabricate. Additionally, it offers a broader range of movements, as both its motion and turning radius can be adjusted by varying the applied voltage.New robot design opens possibilities for integrating miniature sensorsThe researchers and their colleagues have developed a basic prototype of the robot, capable of carrying small loads. In the future, the design could be adapted to incorporate miniature sensors or cameras, expanding the robot’s functionality.In a series of real-world experiments, the prototype developed by the team showed promising results. The robot effectively replicated the running gait of cheetahs and demonstrated the ability to climb ramps with various inclinations.This innovative robotic system developed by the research team could lead to the creation of similar robots using piezoelectric materials. Looking ahead, the team aims to improve the robot’s design to enable it to function effectively in extreme temperatures, harsh climates, or hazardous environments, making it ideal for

Robot Motion - Robot Basics - Universal Robots

Verification on a six-link planar robot manipulator. IEEE Trans Control Syst Technol 21(3):906–914Article Google Scholar Guo D, Zhang Y (2014) Acceleration-level inequality-based man scheme for obstacle avoidance of redundant robot manipulators. IEEE Trans Ind Electron 61(12):6903–6914Article Google Scholar Zhang Y, Li S, Gui J, Luo X (2018) Velocity-level control with compliance to acceleration-level constraints: a novel scheme for manipulator redundancy resolution. IEEE Trans Ind Inf 14(3):921–930Article Google Scholar Zhang Y, Ge SS, Lee TH (2004) A unified quadratic-programming-based dynamical system approach to joint torque optimization of physically constrained redundant manipulators. IEEE Trans Syst Man Cybern Part B (Cybern) 34(5):2126–2132Article Google Scholar Jin L, Xie Z, Liu M, Chen K, Li C, Yang C (2021) Novel joint-drift-free scheme at acceleration level for robotic redundancy resolution with tracking error theoretically eliminated. IEEE/ASME Trans Mechatron 26(1):90–101 Google Scholar Xie Z, Jin L, Luo X, Sun Z, Liu M (2020) RNN for repetitive motion generation of redundant robot manipulators: An orthogonal projection-based scheme. IEEE Transactions on Neural Networks and Learning Systems, pp. 1–14Su H, Yang C, Ferrigno G, De Momi E (2019) Improved human-robot collaborative control of redundant robot for teleoperated minimally invasive surgery. IEEE Robot Autom Lett 4(2):1447–1453Article Google Scholar Aghakhani N, Geravand M, Shahriari N, Vendittelli M, and Oriolo G (2013) Task control with remote center of motion constraint for minimally invasive robotic surgery. In: 2013 IEEE international conference on robotics and automation, pp. 5807–5812Su H, Schmirander Y, Li Z, Zhou X, Ferrigno G, De Momi E (2020) Bilateral teleoperation control of a redundant manipulator with an RCM kinematic constraint. In: 2020 IEEE International Conference on Robotics and AutomationSu H, Schmirander Y, Li Z, Zhou X, Ferrigno G, De Momi E (2020) Bilateral teleoperation control of a redundant manipulator with an rcm kinematic constraint. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 4477–4482Su H, Hu Y, Karimi HR, Knoll A, Ferrigno G, De Momi E (2020) Improved recurrent neural network-based manipulator control with remote center of motion constraints: experimental results. Neural Netw 131:291–299Article Google Scholar Boyd S, Vandenberghe L (2004) Convex Optimization. Cambridge University Press, CambridgeBook Google Scholar Chen D, Zhang Y, Li S (2018) Zeroing neural-dynamics approach and its robust and rapid solution for parallel robot manipulators against superposition of multiple disturbances. Neurocomputing 275:845–858Article Google Scholar Download references. Many situations could benefit from mapping human arm motions to robot arm motions. For example, a person could control a robot in real-time by having the robot mimic the operator’s arm motions or a person could teach a robot by providing an effective demonstration with their own hand in the robot’s workspace. While a mapping from human arm motion to robot arm motion

Robot Motion Planning Robot Motion Planning - CMU School

More cautious “real-time” approach to robot motion control may sometime be needed. Real-time path planning is more complex than the preplanning approach of offline software as it requires continually updating in response to changes in the environment.Automation News & Resources adds that while artificial intelligence (AI) programs have always been associated with robot motion control, this is now a growing trend in robot control.Like all motion control platforms, AI is suited to very specific situations and environments, meaning robot developers must be cognizant of their specific requirements and challenges before selecting a control software.Continue reading: Robotic Motion Control SystemsReferences and Further Reading¹ Robotic surgery, Mayo Clinic, Robotics Motion Control: The Complex Relationship Between Movement and Task, Medical Design Briefs, [ 9 Types of Robotics Software You Might Consider for Your Robot, Automation News & Resources, [ Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Stochastic Tensor Optimization for Robot Motion - A GPU Robot Motion

This kind of system.Global and Local Robot ControlWhile local control systems like the Jacobian matrix have some advantages, some challenges in Robot Motion Control require a global approach.The difference between the two forms of control systems is that local control is best suited to small and well-defined movements. Global control starts with set endpoints calculating a flexible path consisting of large movements in-between these points.Of course, whether users select local or global controls depend on the type of task the robot is assigned to and the environment in which it is operating. Yet, these different control schemes aren’t mutually exclusive. Robots can be optimized by using a combination of local and global control. Designers of robotic systems have a wide range of both open-source and commercial software available to them when it comes to selecting a motion control system. This software can eliminate the years of development time that it takes to create a specialized motion-control platform.Automation News & Resources³ points out that the huge variety of different types of software available for robot motion control and path planning can lead to developers becoming overwhelmed.A major aspect of robot control is path planning, but again there are two significant approaches to moving a robot from A to B. To ensure a robot controls for aspects like vibration and jerk by controlling joint position task paths are often generated offline before motion is underway. Specific offline programming software exists and simulator and mobile robot planning software can be used to account for the complexities of real-world environments and operating spaces.The only issue with this approach is a recalculated path fails to take into account changes that can occur as a task is underway. That means to account for changes in the environment and the actions of the user, a slower and

Foundations of Robot Motion Modern Robotics

Programming robots may seem like a simple task of moving from point to point, like a really expensive game of Connect the Dots. Linear, joint, and circular motion commands affect robot movement differently, each meant to be used in certain settings. It is no secret that robots have become a common staple of the factory floor. With the ability to attach nearly unlimited custom grippers, and collaborative robots that don’t require safety guarding, robots are finding their way into more and more applications every year. Being able to program a robot to make smooth, quick, and safe movements requires an understanding of how a robot can be commanded to move from point A to point B.Figure 1. Base coordinate system (left) and tool coordinate system (right). Image used courtesy of Universal RobotsThe Cartesian Coordinate System ("Frames")Before we discuss the motion commands, we need to understand how the robot determines direction. Robots rely on the use of a cartesian system, much like the graphing systems you learned in algebra. The cartesian space is defined via two perpendicular horizontal axes, the x-axis y-axis, with the z-axis occupying the vertical space to make a 3D space, like a square cardboard box. Every robot will have a base cartesian coordinate system, then additional ones for the tool, the work environment around the robot, and the actual part being handled. Some manufacturers refer to these coordinate systems as ‘frames’.It is important to know which frame is currently being used for the move, otherwise, the final

Foundations of Robot Motion – Modern Robotics

List of the presentations for each country (presentation materials are not available) 2018 Gyeongnam Technopark - Design of 4-axis palletizing robot using technique of controller optimization 2017 Korea Institute of Robot and Convergence - Development of professional service robot with multi-degree-of-freedom using MBD for ANSYS Technische Hochschule Mittelhessen - Support for the Application Assessment of Collaborative Robot Systems by Multibody Simulation 2016 Prof. Zheng - The Design and Simulation of Assistive Robot for Disabled Prof. Huang - Walking State Analysis of Robot Control Using RecurDyn Simulation 2015 Hanwha - A study of the design and analysis method to reduce the impact vibration of a mobile robot Hanwha - A study of the design and analysis method to reduce the impact vibration of a mobile robot ITRI - Six-Axis Articulated Robot Calibration Issues NCTU - Robot Claw with Flexible Driving Simulation 2014 NCTU - Trajectory Planning with Energy Efficiency for Robotic Manipulators 2013 IDAJ Co. LTD - Multibody dynamics simulation and contact parameter of snake robot with three-dimensional motion NCTU - The Trajectory Planning and Dynamic Simulation of Robot Mitsuba - Analysis of noise and vibration of a small motor using RecurDyn 2011 TTDC - Development of partner robot using RecurDyn as a software test tool Yaskawa - Dynamic analysis for motion control 2008 KITECH - The case of dog horse robot modeling using RecurDyn. Many situations could benefit from mapping human arm motions to robot arm motions. For example, a person could control a robot in real-time by having the robot mimic the operator’s arm motions or a person could teach a robot by providing an effective demonstration with their own hand in the robot’s workspace. While a mapping from human arm motion to robot arm motion

The Motion Model of robot

And sliding joints, and in guiding mobile robots to move through locomotion and steering. This enables controlled tasks that can be manipulative — such as the use of a gripper — or sensory — positioning and capturing visual data with a camera.Designing such a system requires controlling things like speed, position, and torque. Even the number of degrees of freedom — the number of ways it can move — possessed by a robot needs to be considered when it comes to motion control.A robot that has extra joints not required for a task may be more difficult to control but can also add a degree of flexibility to the performance of that task by allowing for a multitude of approaches to be adopted. But, this flexibility — which would require a great deal of brainpower for a human — consumes a lot of computing power for a robot.Amongst things that need to be considered are factors for a smooth movement like collision avoidance, workspace limits, and even ensuring the overlap of joints does not occur and the joint limits and movement speeds are respected. There are also other considerations in robot motion control, such as the speed of a task and the amount of power that would be consumed by a specific approach. Related StoriesThe Evolution of Classical Arc Welding TechnologiesMotion Control Vendors Develop Robotic Hands for Motion Control SystemsGlobal Motion Control Market Witnesses Increased Use of Motion Control Systems in Robotic ApplicationsTo address these challenges, Medical Design Briefs points out that mathematical approaches have been developed. One particular mathematical tool highlighted is the Jacobian matrix which sidesteps direct calculations of positions and can solve control problems with its simplified form.The Jacobian matrix is an example of one type of control mechanism — local control — with issues common to

Robot Tracking with Motion Capture for Robotics by Vicon

Dynamic gesture recognition using 3D trajectory. In: Proceedings of 2014 4th IEEE International Conference on Information Science and Technology, Shenzhen, Guanzhou, China, pp. 598–601, April 2014 Google Scholar Veras, E., Khokar, K., Alqasemi, R., Dubey, R.: Scaled telerobotic control of a manipulator in real time with laser assistance for adl tasks. J. Frankl. Inst. 349(7), 2268–2280 (2012)Article MATH Google Scholar Chi, P., Zhang, D.: Virtual fixture guidance for robot assisted teleoperation. In: Bulletin of advanced technology, Vol 5, No. 7, Jul 2011 Google Scholar Hayn, H., Schwarzmann, D.: Control concept for a hydraulic mobile machine using a haptic operating device. In: Proceedings of the 2009 Second International Conferences on Advances in Computer-Human Interactions, ACHI’09, pp. 348–353. IEEE (2009) Google Scholar Sansanayuth, T., Nilkhamhang, I., Tungpimolrat, K.: Teleoperation with inverse dynamics control for phantom omni haptic device. In: 2012 Proceedings of the SICE Annual Conference (SICE), pp. 2121–2126. IEEE (2012) Google Scholar Silva, A.J., Ramirez, O.A.D., Vega, V.P., Oliver, J.P.O.: Phantom omni haptic device: Kinematic and manipulability. In: Proceedings of the 2009 Electronics, Robotics and Automotive Mechanics Conference, CERMA’09, pp. 193–198. IEEE (2009) Google Scholar Mohammadi, A., Tavakoli, M., Jazayeri, A.: Phansim: a simulink toolkit for the sensable phantom haptic devices. In: Proceedings of the 23rd Canadian Congress of Applied Mechanics, pp. 787–790. Vancouver, BC, Canada (2011) Google Scholar N.W.S.: Company specialized in 3d haptic devices. (2012)Martin, S., Hillier, N.: Characterisation of the novint falcon haptic device for application as a robot manipulator. In: Proceedings of the Australasian Conference on Robotics and Automation (ACRA), pp. 291–292. Citeseer (2009) Google Scholar Distante, C., Anglani, A., Taurisano, F.: Target reaching by using visual information and q-learning controllers. Auton. Robot. 9(1), 41–50 (2000)Article Google Scholar Corke, P., et al.: A computer tool for simulation and analysis: the robotics toolbox for MATLAB. In: Proceedings of the National Conference of the Australian Robot Association, pp. 319–330 (1995) Google Scholar MATLAB and Simulink for technical computing. Chinello, F., Scheggi, S., Morbidi, F., Prattichizzo, D.: KCT: a MATLAB toolbox for motion control of kuka robot manipulators. In: Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 4603–4608. IEEE (2010) Google Scholar Elons, A.S., Ahmed, M., Shedid, H., Tolba, M.F.: Arabic sign language recognition using leap motion sensor. In: Proceedings of the 2014 9th International Conference on Computer Engineering and Systems (ICCES), Cairos, pp. 368–373, December 2014 Google Scholar Leap motion—mac and pc motion controller. Many situations could benefit from mapping human arm motions to robot arm motions. For example, a person could control a robot in real-time by having the robot mimic the operator’s arm motions or a person could teach a robot by providing an effective demonstration with their own hand in the robot’s workspace. While a mapping from human arm motion to robot arm motion In Course 4 of the specialization, Robot Motion Planning and Control, you will learn key concepts of robot motion generation: planning a motion for a robot in the presence of obstacles, and real

Approaches to Robot Motion Approaches to desigining robot

ReferencesBaxter Product Datasheet. Mohan, V., Morasso, P., Zenzeri, J., Metta, G., Chakravarthy, V.S., Sandini, G.: Teaching a humanoid robot to draw shapes. Auton. Robot. 31(1), 21–53 (2011)Article Google Scholar Fumagalli, M., Randazzo, M., Nori, F., Natale, L., Metta, G., Sandini, G.: Exploiting proximal f/t measurements for the icub active compliance. In: Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1870–1876. IEEE (2010) Google Scholar Fabian, J., Young, T., Peyton Jones, J.C., Clayton, G.M.: Integrating the microsoft kinect with simulink: real-time object tracking example. IEEE/ASME Trans. Mech. 19(1), 249–257 (2014)Article Google Scholar Colvin, C.E., Babcock, J.H., Forrest, J.H., Stuart, C.M., Tonnemacher, M.J., Wang, W.-S.: Multiple user motion capture and systems engineering. In: Proceedings of the 2011 IEEE Systems and Information Engineering Design Symposium (SIEDS), pp. 137–140. IEEE (2011) Google Scholar Chye, C., Nakajima, T.: Game based approach to learn martial arts for beginners. In: Proceedings of the 2012 IEEE 18th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), pp. 482–485. IEEE (2012) Google Scholar Soltani, F., Eskandari, F., Golestan, S.: Developing a gesture-based game for deaf/mute people using microsoft kinect. In: Proceedings of the 2012 Sixth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), pp. 491–495. IEEE (2012) Google Scholar Borenstein, G.: Making Things See: 3D Vision with Kinect, Processing, Arduino, and MakerBot, vol. 440. O’Reilly, Sebastopol (2012) Google Scholar Cruz, L., Lucio, D., Velho, L.: Kinect and rgbd images: challenges and applications. In: Proceedings of the 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), pp. 36–49. IEEE (2012) Google Scholar Yang, C., Amarjyoti, S., Wang, X., Li, Z., Ma, H., Su, C.-Y.: Visual servoing control of baxter robot arms with obstacle avoidance using kinematic redundancy. In: Proceedings of the Intelligent Robotics and Applications, pp. 568–580. Springer (2015) Google Scholar The principle of leap motion. Chen, S., Ma, H., Yang, C., Fu, M.: Hand gesture based robot control system using leap motion. In: Proceedings of the Intelligent Robotics and Applications, pp. 581–591. Springer (2015) Google Scholar Xu, C.B., Zhou, M.Q., Shen, J.C., Luo, Y.L., Wu, Z.K.: A interaction technique based on leap motion. J. Electron. Inf. Technol. 37(2), 353–359 (2015) Google Scholar Pan, S.Y.: Design and feature discussion of MIDI. controller based on leap motion. Sci. Technol. China’s Mass Media 10, 128–129 (2014) Google Scholar Wang, Q.Q., Xu, Y.R., Bai, X., Xu, D., Chen, Y.L., Wu, X.Y.: