### 2020　Planning Algorithm

Font size  SML

Graduate major in Systems and Control Engineering
Instructor(s)
Kurabayashi Daisuke
Class Format
Lecture    (ZOOM)
Media-enhanced courses
Day/Period(Room No.)
Mon3-4(S222)
Group
-
Course number
SCE.A503
Credits
1
2020
Offered quarter
1Q
Syllabus updated
2020/9/18
Lecture notes updated
-
Language used
English
Access Index

### Course description and aims

This course will provide a comprehensive overview of planning algorithms. Concept of planning, tools for planning, and consideration of uncertainty will be introduced. Graph theory, Graph search, sampling based planning, combinatorial planning, configuration space, decision theoretic planning, and planning using potential will be discussed.

Planning algorithm are used to derive target trajectory for control systems. For example, path planning for mobile robots, production planning for manufacturers, and collision avoidance of multiple manipulators, etc. Students should be familiar with abstracted models of problems in the world.

### Student learning outcomes

By the end of the course, students will be able to explain the model and solutions for planning problems and will be able to select suitable methods for planning problems.

### Keywords

Planning, graph, configuration space, sampling, combination, uncertaity, complexity

### Competencies that will be developed

 ✔ Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

### Class flow

In the class, selected issues about planning algorithms will be presented. The instructor presents single topic in each class. Discussions or quiz will be asked to students in each class.

### Course schedule/Required learning

Course schedule Required learning
Class 1 Introduction to Planning Algorithms Students must be able to express the concept of planning algorithms and be able to measure computational complexity of a problem.
Class 2 Topology and Configuration Students must be able to explain the topology and the configuration space of a state space of a planning problem.
Class 3 Sampling based Algorithms Students should become familiar with algorithms based on sampling.
Class 4 Combinatorial Planning Students should become familiar with algorithms based on combinatorial methods.
Class 5 Decision-theoretic Planning Students must be able to explain uncertainty of planning problems.
Class 6 Feedback Motion Planning Students must be able to explain models and expressions of planning methods as feedback systems.
Class 7 Complexity of Motion Planning Students should become familiar with the complexity of planning problems and solutions by using soft-computing techniques.

### Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

### Textbook(s)

Steven M. LaValle (2006) Planning Algorithms. Cambridge University Press.

### Reference books, course materials, etc.

Handouts will be distributed at the beginning of class when necessary.

### Assessment criteria and methods

Quiz at lecture: 20%, final report : 80%

### Related courses

• Stochastic Systems

### Prerequisites (i.e., required knowledge, skills, courses, etc.)

Students are expected to have knowledge of the basis of linear control systems and stochastic systems