Masses and inertias can, in some instances, have a very dramatic and negative impact on performance. Important to note - try to model weight and bulk as accurately as you can. Re-run your simulation and verify you're getting the performance you want.Replace your ideal multibody joints with your detailed actuator models.For example, how are you expecting to have the actuators in real life track joint angle? PID control of angle error? That's what you model. This model includes whatever actuator dynamics there are along with whatever input conditioning you're expecting to use. Generate a model for the actuators you selected.Choose the peak force/torque for each axis of motion, add some performance margin, and choose actuators that at least provide that performance.Use the joint sensors 50k4 mentioned to trend/plot the joint forces and torques required to achieve your motion profile.Calculate the inverse kinematics to get the required joint angles to achieve the motion profile.Create a motion profile that you deem is representative of the tasks the robot is supposed to perform.The process I would use for designing would go something like this: If you choose to provide joint position, then Simscape calculates the force/torque required to achieve that motion, then applies that to the system dynamics. If you choose to provide joint torque, then the resulting output motion is calculated by the dynamics of the system. Joints in the package generally allow for one of two inputs to be used - force/torque or position. The trajectory of end-effector of the crane-type manipulator can be transformed into the trajectory of wire length and trolley position by the calculation method. You will build on a library of robotics software in the language of your choice (among Python, Mathematica, and MATLAB) and use the free cross-platform robot simulator V-REP, which allows you to work with state-of-the-art robots in the comfort of your own home and with zero financial investment.You can use Simscape Multibody (the new name for what was SimMechanics) as a two-step process. Based on the dynamical model with wire force vector, inverse dynamics problem is analytically solved by a linear equation in terms of the wire force vector. You can purchase the book or use the free preprint pdf. Toward clarifying the biomechanics and neural mechanisms underlying coordinated control of the complex hand musculoskeletal system, we constructed an anatomically based musculoskeletal model of the Japanese macaque (Macaca fuscata) hand, and then estimated the muscle force of all the hand muscles during a precision grip task using inverse dynamic calculation. This course follows the textbook "Modern Robotics: Mechanics, Planning, and Control" (Lynch and Park, Cambridge University Press 2017). You will also learn how to plan robot trajectories subject to dynamic constraints. The former is useful for simulation, and the latter is useful for robot control. In Course 3 of the specialization, Robot Dynamics, you will learn efficient numerical algorithms for forward dynamics (calculating the robot's acceleration given its configuration, velocity, and joint forces and torques) and inverse dynamics (calculating the required joint forces and torques given the robot's configuration, velocity, and acceleration). This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study. If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics?
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