PPT-Search-based Path Planning with

Homotopy Class Constraints Subhrajit Bhattacharya Vijay Kumar Maxim Likhachev University of Pennsylvania GRASP L ABORATORY Addendum For the simple cases in 2dimensions we have not distinguished between . CS 659. Kris Hauser. Agenda. Skimming through . Principles. Ch. 2, 5.1, 6.1 . Fundamental question of motion planning. Are the two given points connected by a path?. Forbidden region. Feasible space. Wesley Chu. With slides taken from Armin . Hornung. Humanoid Path Planning. Humanoids have large number of DOF. Planning full body movements not computationally feasible. Alternative: plan for footstep locations, and use predefined motions to execute on these footsteps. For the simple cases in 2-dimensions we have not distinguished between . homotopy. and homology. The distinction however does exist even in 2-d. See our more recent [AURO 2012] paper or [RSS 2011] paper for a comprehensive discussion on the distinction between . A . search strategy . is defined by picking the order of node . expansion. Uninformed. . search strategies use only the information available in the problem . definition. Breadth-first search. Depth-first search. A . search strategy . is defined by picking the order of node . expansion. Uninformed. . search strategies use only the information available in the problem . definition. Breadth-first search. Depth-first search. Mechanical Engineering Department. IIT Patna. ME512: Mobile Robotics. Path Planning Algorithms. Path Planning Problem. Given. Robot state. Obstacle positions. Robot capabilities. Compute collision free optimal path to a goal. A . search strategy . is defined by picking the order of node . expansion. Uninformed. . search strategies use only the information available in the problem . definition. Breadth-first search. Depth-first search. Armin Hornung, Daniel Maier, Maren Bennewitz. Presentation by Dominique Gordon. Introduction. Humanoid Robots vs. Wheeled Robots. Step over obstacles. Many degrees of freedom. Not yet feasible to plan whole body motions in real world. Decision-making and planning. Spring 2018. CS 599.. Instructor: Jyo Deshmukh. Decision-making hierarchy. Motion planning. Overview. 2. Decision-making hierarchy. 3. Route Planning. Behavioral Planning. Two Challenges for Optimal Path planning. Classic Two-Layered Architecture for Mobile Robots. Dynamic Window Approach (DWA). Dynamic Window Approach. Admissible Velocities. Reachable Velocities. DWA Search Space. I400/B659: Intelligent Robotics. Kris Hauser. Agenda. Breeze through . Principles. Ch. 2, 5.1, 6.1 . Fundamental question of motion planning. Are the two given points connected by a path?. Forbidden region. Search Review Path Planning Search: Our Role We have to define the problem space State Representation Initial State Operators Goal State (a test to see if a node is the goal) (If possible) A heuristic A*. It . applies to . path-planning problems on known finite graphs whose edge costs increase or . decrease over . time. (Such cost changes can also be used to model edges or vertices that are . added or . What is . Pathfinding. ?. Find a path between two given locations on a map with obstacles. A Broad Area with Many Types of .... A Challenging Search Problem. Need solution in real time. CPU and memory resources are often limited.