A robot path planning framework that learns from experience

Abstract

Abstract-We propose a framework, called Lightning, for planning paths in high-dimensional spaces that is able to learn from experience, with the aim of reducing computation time. This framework is intended for manipulation tasks that arise in applications ranging from domestic assistance to robot-assisted surgery. Our framework consists of two main modules, which run in parallel: a planning-from-scratch module, and a module that retrieves and repairs paths stored in a path library. After a path is generated for a new query, a library manager decides whether to store the path based on computation time and the generated path's similarity to the retrieved path. To retrieve an appropriate path from the library we use two heuristics that exploit two key aspects of the problem: (i) A correlation between the amount a path violates constraints and the amount of time needed to repair that path, and (ii) the implicit division of constraints into those that vary across environments in which the robot operates and those that do not.We evaluated an implementation of the framework on several tasks for the PR2 mobile manipulator and a minimally-invasive surgery robot in simulation. We found that the retrieve-andrepair module produced paths faster than planning-fromscratch in over 90% of test cases for the PR2 and in 58% of test cases for the minimally-invasive surgery robot.

IoT and Robotics: a synergy

“…One example is for path planning, where previouslygenerated paths are adapted to similar environments [21] and grasp stability of finger contacts can be learned from previous grasps on an object [22].…”

Lazy validation of Experience Graphs

“…[10] present an approach to use a database of older motion plans to draw a bi-directional RRT search towards a path stored in the database that is most similar to the new motion plan request. Recent work [2] attempts to repair previous plans from a database using randomized planners. As mentioned in [15], the use of a database of motion plans is also core to Experience Graphs.…”

Manipulation and Path Planning for KUKA (LWR/ LBR 4+) Robot in a Simulated and Real Environment

“…In which selection of final goal state is defined by using interactive marker, the command plan and execution was used for movement of arm. For minimum trajectory path selection for arm OMPL (Open motion Planning Library) was used [13]. Figure 8 shows the motion planning and manipulation of bilateral KUKA LWR robot platform in MoveIt!…”