Vijayakumar, Sethu

Herrmann, Michael

Yang, Yiming

2018-06-22T10:59:33Z

2018-06-22T10:59:33Z

2018-07-02

The use of robotics has recently seen significant growth in various domains such as unmanned ground/underwater/aerial vehicles, smart manufacturing, and humanoid robots. However, one of the most important and essential capabilities required for long term autonomy, which is the ability to operate robustly and safely in real-world environments, in contrast to industrial and laboratory setup is largely missing. Designing robots that can operate reliably and efficiently in cluttered and changing environments is non-trivial, especially for high degree-of-freedom (DoF) systems, i.e. robots with multiple actuators. On one hand, the dexterity offered by the kinematic redundancy allows the robot to perform dexterous manipulation tasks in complex environments, whereas on the other hand, such complex system also makes controlling and planning very challenging. To address such two interrelated problems, we exploit robot motion synthesis from three perspectives that feed into each other: end-pose planning, motion planning and motion adaptation. We propose several novel ideas in each of the three phases, using which we can efficiently synthesise dexterous manipulation motion for fixed-base robotic arms, mobile manipulators, as well as humanoid robots in cluttered and potentially changing environments. Collision-free inverse kinematics (IK), or so-called end-pose planning, a key prerequisite for other modules such as motion planning, is an important and yet unsolved problem in robotics. Such information is often assumed given, or manually provided in practice, which significantly limiting high-level autonomy. In our research, by using novel data pre-processing and encoding techniques, we are able to efficiently search for collision-free end-poses in challenging scenarios in the presence of uneven terrains. After having found the end-poses, the motion planning module can proceed. Although motion planning has been claimed as well studied, we find that existing algorithms are still unreliable for robust and safe operations in real-world applications, especially when the environment is cluttered and changing. We propose a novel resolution complete motion planning algorithm, namely the Hierarchical Dynamic Roadmap, that is able to generate collision-free motion trajectories for redundant robotic arms in extremely complicated environments where other methods would fail. While planning for fixed-base robotic arms is relatively less challenging, we also investigate into efficient motion planning algorithms for high DoF (30 - 40) humanoid robots, where an extra balance constraint needs to be taken into account. The result shows that our method is able to efficiently generate collision-free whole-body trajectories for different humanoid robots in complex environments, where other methods would require a much longer planning time. Both end-pose and motion planning algorithms compute solutions in static environments, and assume the environments stay static during execution. While human and most animals are incredibly good at handling environmental changes, the state-of-the-art robotics technology is far from being able to achieve such an ability. To address this issue, we propose a novel state space representation, the Distance Mesh space, in which the robot is able to remap the pre-planned motion in real-time and adapt to environmental changes during execution. By utilizing the proposed end-pose planning, motion planning and motion adaptation techniques, we obtain a robotic framework that significantly improves the level of autonomy. The proposed methods have been validated on various state-of-the-art robot platforms, such as UR5 (6-DoF fixed-base robotic arm), KUKA LWR (7-DoF fixed-base robotic arm), Baxter (14-DoF fixed-base bi-manual manipulator), Husky with Dual UR5 (15-DoF mobile bi-manual manipulator), PR2 (20-DoF mobile bi-manual manipulator), NASA Valkyrie (38-DoF humanoid) and many others, showing that our methods are truly applicable to solve high dimensional motion planning for practical problems.

http://hdl.handle.net/1842/31236

en

The University of Edinburgh

Y. Yang, W. Merkt, V. Ivan, Z. Li, and S. Vijayakumar. HDRM: A Resolution Complete Dynamic Roadmap for Realtime Motion Planning in Complex Environments. In IEEE Robotics and Automation Letter (RA-L), 3(1):551–558, 2018.

V. Ivan, Y. Yang, W. Merkt, M. Camilleri, and S. Vijayakumar. EXOTica: a library for easy creation of tools for optimisation and planning. In Robot Operating System (ROS) Volume 3, Springer, 2018.

Y. Yang, W. Merkt, H. Ferrolho, V. Ivan, and S.Vijayakumar. Efficient Humanoid Motion Planning on Uneven Terrain Using Paired Forward-Inverse Dynamic Reachability Maps. In IEEE Robotics and Automation Letter (RA-L), 2(4):2279–2286, 2017.

W. Merkt, Y. Yang, T. Stouraitis, C. E. Mower, M. Fallon, S. Vijayakumar. Robust Shared Autonomy for Mobile Manipulation with Continuous Scene Monitoring. In Proceeding of IEEE Conference on Automation Science and Engineering (CASE), pages 130- 137, Xi’an, China, 2017.

Q. Li, I. Chatzinikolaidis, Y. Yang, S. Vijayakumar and Z. Li, Robust foot placement control for dynamic walking using online parameter estimation. In Proceeding of IEEE/RAS International Conference on Humanoid Robotics (Humanoids), pages 165-170, Birmingham, UK, 2017.

Y. Yang, V. Ivan, Z. Li, M. Fallon and S. Vijayakumar. iDRM: Humanoid motion planning with realtime end-pose selection in complex environments. In Proceeding of IEEE/RAS International Conference on Humanoid Robots (Humanoids), pages 271–278, Cancun, Mexico, 2016.

Y. Yang, V. Ivan, W. Merkt and S. Vijayakumar. Scaling sampling-based motion planning to humanoid robots. In Proceeding of IEEE International Conference on Robotics and Biomimetics (ROBIO), pages 1448–1454, Qingdao, China, 2016.

Y. Yang, V. Ivan and S. Vijayakumar. Real-time motion adaptation using relative distance space representation. In Proceeding of IEEE International Conference on Advanced Robotics (ICAR), pages 21–27, Istanbul, Turkey, 2015.

A. Orthey, V. Ivan, M. Naveau, Y. Yang, O. Stasse, and S. Vijayakumar. Homotopic particle motion planning for humanoid robotics. Rapport LAAS n° 15107, 2015.

robotics

long term autonomy

degree-of-freedom systems

DoF systems

robot motion synthesis

end-pose planning

motion planning

motion adaptation

collision-free inverse kinematics

UR5

KUKA LWR

Motion synthesis for high degree-of-freedom robots in complex and changing environments

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy