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You ask several questions, but I will focus mostly on the localization part. Is it possible or even clever to use a particle filter out of robot sensor data? Yes. You can use a particle filter, but I don't understand what part of your system is collaborative. Instead, you have a robot operating in dynamic environment (with a remote sensor). See next ...


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The process you are referring to is called point cloud registration (or point matching). The goal of point cloud registration is find the spatial transformation that aligns two point clouds (i.e., sets of points). One of the most popular methods is iterative closest point (ICP), and many variants of ICP exist. Other methods exist as well such as robust point ...


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You can do various Bad Things to a motor by doing this -- but whether it's bad for your motor in your machine depends on what you're doing. When you short a motor like that, it'll generate a lot of current, and in addition to heating up the armature, that current can damage the commutator or brushes. On older motors it could demagnetize the magnets, but ...


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The two approaches you mention are not as separated as they seem. Both kinematic models and dynamic models are not 100% accurate. This is the reason closed loop control is needed (well probably only one of the reasons if you ask controls experts). You should not think of kinematic and dynamic models of being completely separated. They are all models ...


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It is a bit of both. The repulsive potentials help create a way to represent an environment that is conducive to planning. In the area of repulsive potentials, a simple gradient descent from a point will take you to the minimum, i.e., some collision free point. At the same time, the attractive potential increases as you move away from the goal, so ...


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