Robotics: Computational Motion Planning

Start Date: 07/05/2020

Course Type: Common Course

Course Link: https://www.coursera.org/learn/robotics-motion-planning

About Course

Robotic systems typically include three components: a mechanism which is capable of exerting forces and torques on the environment, a perception system for sensing the world and a decision and control system which modulates the robot's behavior to achieve the desired ends. In this course we will consider the problem of how a robot decides what to do to achieve its goals. This problem is often referred to as Motion Planning and it has been formulated in various ways to model different situations. You will learn some of the most common approaches to addressing this problem including graph-based methods, randomized planners and artificial potential fields. Throughout the course, we will discuss the aspects of the problem that make planning challenging.

Course Syllabus

Welcome to Week 1! In this module, we will introduce the problem of planning routes through grids where the robot can only take on discrete positions. We can model these situations as graphs where the nodes correspond to the grid locations and the edges to routes between adjacent grid cells. We present a few algorithms that can be used to plan paths between a start node and a goal node including the breadth first search or grassfire algorithm, Dijkstra’s algorithm and the A Star procedure.

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Course Introduction

Robotics: Computational Motion Planning This course introduces the basic concepts of robotics, and their application to modeling, planning, and tracking of robots in motion. It builds upon the prior course “Robotics: Computational Motion Planning”, by introducing alternative dynamics and nonlinear motion planning techniques. The course also focuses on the application of robotics to modeling and tracking in general. It will cover robotics as an option in the domain of planning of enterprise mobility, and as a last resort for tracking and tracking down tracking problems in specific applications. In this course, you will first learn the basic concepts. You will then focus on two alternative tracking methods. You will then apply robotics to the modeling and planning of specific tracking and motion problems. Lastly, you will go through the steps for an enterprise mobility solution. Learners will produce a high-quality model of a robot based on a specific set of parameters, and the model will also discuss the problem and solution. The model must be prepared for tracking and motion planning--specifically, it must have the following dimensions and parameters: - the x, y, and z axes relative to the center of the motor system, and the track and track loops in the motor - the y axis relative to the track loops, and the armature and thruster motors - the volume and torque vectors in order to obtain the vectorized motion and velocity vorticity - the orientation of the thruster motors relative to the motor and the orientation

Course Tag

Motion Planning Automated Planning And Scheduling A* Search Algorithm Matlab

Related Wiki Topic

Article Example
Computational geometry Other important applications of computational geometry include robotics (motion planning and visibility problems), geographic information systems (GIS) (geometrical location and search, route planning), integrated circuit design (IC geometry design and verification), computer-aided engineering (CAE) (mesh generation), computer vision (3D reconstruction).
Motion planning Motion planning (also known as the navigation problem or the piano mover's problem) is a term used in robotics for the process of breaking down a desired movement task into discrete motions that satisfy movement constraints and possibly optimize some aspect of the movement.
Motion planning Motion planning has several robotics applications, such as autonomy, automation, and robot design in CAD software, as well as applications in other fields, such as animating digital characters, video game artificial intelligence, architectural design, robotic surgery, and the study of biological molecules.
Nancy M. Amato In 2010, she was named an IEEE Fellow " For contributions to the algorithmic foundations of motion planning in robotics and computational biology."
Motion planning A basic motion planning problem is to produce a continuous motion that connects a start configuration S and a goal configuration G, while avoiding collision with known obstacles. The robot and obstacle geometry is described in a 2D or 3D "workspace", while the motion is represented as a path in (possibly higher-dimensional) configuration space.
Rational motion These CAD methods for motion design find applications in animation in computer graphics (key frame interpolation), trajectory planning in robotics (taught-position interpolation), spatial navigation in virtual reality, computer-aided geometric design of motion via interactive interpolation, CNC tool path planning, and task specification in mechanism synthesis.
Motion planning Target space is a linear subspace of free space which we want robot to go there. In global motion planning, target space is observable by the robot's sensors. However, in local motion planning, the robot cannot observe the target space in some states. To solve this problem, the robot goes through several virtual target spaces, each of which is located within the observable area (around the robot). A virtual target space is called a sub-goal.
Motion planning A motion planner is said to be complete if the planner in finite time either produces a solution or correctly reports that there is none. Most complete algorithms are geometry-based. The performance of a complete planner is assessed by its computational complexity.
Motion planning For example, consider navigating a mobile robot inside a building to a distant waypoint. It should execute this task while avoiding walls and not falling down stairs. A motion planning algorithm would take a description of these tasks as input, and produce the speed and turning commands sent to the robot's wheels. Motion planning algorithms might address robots with a larger number of joints (e.g., industrial manipulators), more complex tasks (e.g. manipulation of objects), different constraints (e.g., a car that can only drive forward), and uncertainty (e.g. imperfect models of the environment or robot).
John Reif In the area of robotics, he gave the first hardness proofs for robotic motion planning as well as efficient algorithms for a wide variety of motion planning problems.
Real-time path planning Path planning and navigation play a significant role in robot motion planning and simulated virtual environments. Computing collision-free paths, addressing clearance, and designing dynamic representations and re-planning strategies are examples of important problems with roots in computational geometry and discrete artificial intelligence search methods, and which are being re-visited with innovative new perspectives from researchers in computer graphics and animation.
Distance transform Applications are digital image processing (e.g., blurring effects, skeletonizing), motion planning in robotics, and even pathfinding.
Motion planning Exact motion planning for high-dimensional systems under complex constraints is computationally intractable. Potential-field algorithms are efficient, but fall prey to local minima (an exception is the harmonic potential fields). Sampling-based algorithms avoid the problem of local minima, and solve many problems quite quickly.
Motion planning Sampling-based algorithms are currently considered state-of-the-art for motion planning in high-dimensional spaces, and have been applied to problems which have dozens or even hundreds of dimensions (robotic manipulators, biological molecules, animated digital characters, and legged robots).
Nonholonomic system In robotics, nonholonomy has been particularly studied in the scope of motion planning and feedback linearization for mobile robots. Refer to holonomic robotics for a more detailed description.
Theoretical computer science Other important applications of computational geometry include robotics (motion planning and visibility problems), geographic information systems (GIS) (geometrical location and search, route planning), integrated circuit design (IC geometry design and verification), computer-aided engineering (CAE) (mesh generation), computer vision (3D reconstruction).
Robotics The Robotics Certification Standards Alliance (RCSA) is an international robotics certification authority that confers various industry- and educational-related robotics certifications.
Robotics Universities offer bachelors, masters, and doctoral degrees in the field of robotics. Vocational schools offer robotics training aimed at careers in robotics.
The International Journal of Robotics Research The International Journal of Robotics Research is a peer-reviewed scientific journal that covers the field of robotics on topics from sensors and sensory interpretations to kinematics in motion planning. Its editor-in-chief is John M. Hollerbach (University of Utah). The journal was established in 1982 and is published by Sage Publications.
Robotics Many schools across the country are beginning to add robotics programs to their after school curriculum. Some major programs for afterschool robotics include FIRST Robotics Competition, Botball and B.E.S.T. Robotics. Robotics competitions often include aspects of business and marketing as well as engineering and design.