The application of local optimal control is a promising paradigm for manipulative robot motion generation.In practice this involves instantaneous formulations of convex optimization problems depending on the current joint configuration of the robot and the environment.To be effective, however, constraints have to be carefully constructed as this kind of motion generation approach has a trade-off of completeness.Local optimal solvers, which are greedy in a temporal sense, have proven to be significantly more effective computationally than classical grid-based or sampling-based planning approaches. In this thesis we investigate how a local optimal control approach, namely the task function approach, can be implemented to grant high usability, extendibility and effectivity.This has resulted in the HiQP control framework, which is compatible with ROS, written in C++.The framework supports geometric primitives to aid in task customization by the user.It is also modular as to what communication system it is being used with, and to what optimization library it uses for finding optimal controls. We have evaluated the software quality of the framework according to common quantitative methods found in the literature.We have also evaluated an approach to perform tasks using minimal jerk motion generation with promising results.The framework also provides simple translation and rotation tasks based on six rudimentary geometric primitives.Also, task definitions for specific joint position setting, and velocity limitations were implemented.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-133224 |
| Date | January 2016 |
| Creators | Johansson, Marcus |
| Publisher | Linköpings universitet, Artificiell intelligens och integrerade datorsystem |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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