Coming soon- robots that drive
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Self-driving cars are in the news a lot lately. An alternative way to think of such cars is as robots that carry people.
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Self-driving cars are in the news a lot lately. An alternative way to think of such cars is as robots that carry people.
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Is it acceptable that self-driving cars will cause accidents if they kill fewer people than human drivers? When an accident occurs is it a moral or a legal issue?
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Vision is useful to us and to almost all forms of life on the planet, perhaps robots could do more if they could also see. Robots could mimic human stereo vision or use cameras with superhuman capability such as wide angle or panoramic views.
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Let’s look at numerical approaches to inverse kinematics for a couple of different robots and learn some of the important considerations. For RTB10.x please note that the mask value must be explicitly preceded by the ‘mask’ keyword. For example: >> q = p2.ikine(T, [-1 -1], ‘mask’, [1 1 0 0 0 0])
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An alternative for smooth motion between poses is Cartesian interpolated motion which leads to straight line motion in 3D space.
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More people means more travel. Will our roads cope and are we doomed to a future of increased road rage. Let’s talk about transport and how robots might help.
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At what point does it become unethical to replace a human carer with a robot carer for an elderly parent or a child?
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We will introduce resolved-rate motion control which is a classical Jacobian-based scheme for moving the end-effector at a specified velocity without having to compute inverse kinematics.
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We combine what we’ve learnt about smoothly varying position and orientation to create smoothly varying pose, often called Cartesian interpolation.
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We revisit the fundamentals of geometry that you would have learned at school: Euclidean geometry, Cartesian or analytic geometry, coordinate frames, points and vectors.