Name: Planning Algorithms
ISBN: 9780511240812

What Hacker News says

antegamisouApr 2023

This reply will focus mainly on the academics.

Since you've mentioned exoskeletons, knowledge of kinematics and dynamics is imperative.

Rotation Matrices, Forward/Inverse Kinematics, Denavit - Hartenberg Parameters, Lagrangian Mechanics are a few fundamental concepts one should be familiar with. Their applications mostly pertain to robotic manipulators (arms), which are what members of exoskeleton's are modeled after.

They're covered extensively in the classic in the field textbook

    Robotics Modelling, Planning and Control by Siciliano, Sciavicco, Villani, Oriolo

https://link.springer.com/book/10.1007/978-1-84628-642-1

They also require some prior knowledge of linear algebra to safely navigate through, so make sure you've achieved at least some math literacy before diving into them.

Speaking of navigation, if you're interested in motion planning i.e. how to optimally (safely and efficiently) go from point A to point B, what you read is

    Planning Algorithms by Steven M. Lavalle

http://lavalle.pl/planning/

for various ways the math people have came up with to solve this. Many cool applications in fields outside of robotics like in Computer Graphics/Animation too.

And btw, if there's one paper you'll absolutely have to read if you find yourself more interested in motion planning is

    Sampling-based Algorithms for Optimal Motion Planning by Sertac Karaman and Emilio Frazzoli

https://arxiv.org/pdf/1105.1186.pdf

in which the authors have revised two very popular path planning algorithms by making them significantly more optimal than their initial implementations were, and are part of many decision making systems that are involved in any type of mechanical movements.

Some other comments talked about more advanced disciplines in the field like State Estimation or Reinforcement Learning but I believe the aforementioned (kimenatics/dynamics/motion planning) are the bare minimum before diving into even more advanced math-heavy concepts.

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krisoftMay 2022

> Even the most sophisticated machine learning algorithm requires repetition and constant input to be optimized.

Self driving cars are not machine learning algorithms. They have machine learning parts in their programing but nobody reputable would hook up a neural network to the pedals and the steering and just let it rip.

You simply have a bad mental model of how these machines are built and it shows.

There are three big questions every self driving car has to answer: Where I am? What is around me? What to do next?

To reliably answer the “where” question you don’t just trust one sensor. You use multiple sensors. Yes, that includes a gps, but also cameras and a lidar and a radar too. It is not machine learning. If you are interested how this is done you need to look up topics like iterative closest point matching, multi-view geometry, kalman-filtering, bag-of-visual-words representation, and bayesian reasoning.

To reliably answer what is around the car you likewise use all of your sensors. Yes, this has bits of machine learning in it, but it is not only machine learning. You can and do a lot of model-free perception. For example if your laser is bouncing back from somewhere then there is something there. There is a lot of published literature on sensor fusion and tracking and prediction. These algorithms are not magic, and they are not machine learning. They are just what you also would come up with if you would think about the problem hard for 8 hours a day for tens of years.

Then there is the planning. These are not machine learning based algorithms. They usually generate a bunch of different plans, then cull the unsafe ones (for example the ones which would collide with a tracked object) and then you rate the plans according to some heuristic and choose the best, then repeat. It is not magic. There is a serious craft and engineering to it. You can read a good introduction to different approaches in Lavalle’s Planning Algorithms book if you are so interested.

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antiuniverseJun 2016

Not my field, but in case this link is helpful:

"Planning Algorithms" by Steven M. LaValle

http://planning.cs.uiuc.edu/

>This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.

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jvanderbotJun 2021

Writing a book about your research is basically expected in most academic fields. Engineering is no different.

Would you be mad at Thrun for publishing and profiting from Probablistic Robotics while publishing academic papers on the same subject? I wasn't.

Though le valle's "Planning Algorithms" was released free and has arguably done better, that model is not the state of the art in most academic publishing.

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samuellFeb 2019

Seems like an interesting complement to the (free) book on planning algorithms by Steven Lavalle: http://planning.cs.uiuc.edu/

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