Planning and control

By Thomas L. Dean, Michael P. Wellman. Operations Management, 2019

408 citations recorded by the source corpus.

The starter code is written in Python and depends on NumPy and Matplotlib. This section gives a brief overview. • run.py Contains the main function. Note the command-line arguments that you can provide. • MapEnvironment.py Environment-Specific functions. Some of them have to be filled in by you. • map1/2.txt Maps that you will work with. The numbers denote the collision status of the cell (1 collision, 0 collision-free). • AStarPlanner.py A* Planner. Logic to be filled in by you. • RRTPlanner.py RRT planner. Logic to be filled in by you. • RRTStarPlanner.py RRT* planner. Logic to be filled in by you. • RRTTree.py Contains datastructure taht can be useful for your implementation of RRT and RRT*.

Subfields: Management Science and Operations Research, Organizational Behavior and Human Resource Management, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/f7ad88a9e18237abf09efa8465aaa00ee2cad2a2

How researchers see this paper

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In Management Science and Operations Research

1. Hector J. Levesque
2. Vaishak Belle

In Organizational Behavior and Human Resource Management

1. Hector J. Levesque
2. Vaishak Belle

In Public Health, Environmental and Occupational Health

1. Hector J. Levesque
2. Vaishak Belle

Citations in the corpus

• Reasoning about Probabilities in Dynamic Systems using Goal Regression (2013, methodology): “At the other extreme, revising beliefs after noisy observations over rich error profiles is effortlessly addressed using probabilistic techniques such as Kalman filtering and Dynamic Bayesian Networks (Dean and Kanazawa, 1989; Dean and Wellman, 1991).”