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Your intuition is partially correct in the sense that you ought to go with position control implemented via velocity commands resorting to a kinematic (not dynamic) model of the manipulator. This can be explained by inspecting one of the easiest policy used for inverse kinematics, $$\dot{\mathbf{q}} = \mathbf{J}^{-1} \cdot \left( \mathbf{x}_d - \mathbf{x}(\... 2 These two concepts are complementary and you use them together, the motion profile providing the input to your control scheme. At each time-step the motion profile gives you the reference values for the control loop scheme (and also some feed forward values if needed). This goes both for the acceleration and the deceleration phases. in both cases, the motion ... 2 No, this is not applicable for a car, it is just an introductory, extremely simplified example. It is one step closer to a mobile robot, then to a car, at least a mobile robot (at least some of them) is capable of moving in any direction (are holomonic), a car could not move instantaneously to the left or right (it is non holomonic). In general as a first ... 1 On the theory side, this is related to the Nyquist Sampling Rate, which is how frequently you must measure a single to get an accurate reconstruction of it's peaks / valleys. Not suprisingly, Nyquist as a name appears all over some fundamental results in optimal control like the nyquist stability theorem. I suspect the insight you are looking for is right ... 1 One example I see is animating human character on video game. On Unity game engine demo, they show there is human character that stand on big rock. The rock roll left and right while character maintain to stay on the top of rock. To achieve this, character has to control the center of mass itself by moving joint on his leg. That is by performing inverse ... 1 Is pure pursuit something like this? Here my agent is chasing a moving target that has 60-70% of its velocity. The agent is given only the information of the present position of the robot. So if you are pursuing a string of recorded waypoints, maybe my method can help. At every instant, the robot looks for its heading correction. If the heading is within a ... 1 This website was really helpful for me when learning all about robotics: https://robotacademy.net.au It has lots of info on motion planning / path planning too. I guess this isn't a direct answer to your question but for anyone coming here looking for educational resources, this is a good one. 1 I would argue, that it is not graph search. In the implementation, you keep all the nodes in a flat list and check which of the nodes is closest to the sampled point. As all nodes are checked, this might be seen as a brute-force graph search, but in the implementation is just for loop iterating though all the points in a list. The goal check is done for the ... 1 You're mentioning a period without going into any detail about it, so I'm not positive what you're asking about, but I'm guessing your concern is about a computation period - the inverse of the computation frequency. Is this right? That you mean it's possible to overshoot or undershoot a desired position because the time step required to achieve the target ... 1 1- No it is not. This example is the beginning of RL, while Self-driving cars are way much complex. In a simplified view, there are two main differences. state and action in the shown example are discrete, while for a real robotic application are continuous. 2- Despite the promising results, still RL is far from global path planning -planning to long ahead- ... 1 Extending the previous answer which describes how to compute a minimum-jerk trajectory given a consistent distance coordinate system. A simple way to do this is to treat the first coordinate as your origin then convert each other GPS point to meter distances from your first coordinate using one of the latitude and longitude equations here 1 As reported for example in https://robotics.stackexchange.com/a/21571/6941, a minimum-jerk trajectory in one dimension is coded with respect to time t as:$$ x(t) = x_i + (x_f-x_i) \cdot \left( 10\left(\frac{t}{t_f}\right)^3 -15\left(\frac{t}{t_f}\right)^4 +6\left(\frac{t}{t_f}\right)^5\right),  where $t_f$ is the final time ($2\, \text{s}$ in your case),...

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Based on your comment: if I am able to get 0 to 2pi, would be enough as well. The following code will do it: modifiedHeading = SensorOutput(); if(modifiedHeading < 0) { modifiedHeading += 360f; } But, as I've mentioned previously, you still have the jump discontinuity, but you've moved it from the 359/1 degree range from the 179/-179 degree range.

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If you're interested in doing generic motion planning the best place to start is with the MoveIt Project If you specifically want to do ground navigation, the ROS navigation stack has been a standard starting point. However if you're getting started now I'd recommend getting involved with nav2 which is an iteration on the design which is being developed for ...

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