Vijayakumar, Sethu

Komura, Taku

Ramamoorthy, Subramanian

Ivan, Vladimir

Engineering and Physical Sciences Research Council (EPSRC)

2015-09-02T14:03:45Z

2015-09-02T14:03:45Z

2015-06-29

Robot motion can be described in several alternative representations, including joint configuration or end-effector spaces. These representations are often used for manipulation or navigation tasks but they are not suitable for tasks that involve close interaction with the environment. In these scenarios, collisions and relative poses of the robot and its surroundings create a complex planning space. To deal with this complexity, we exploit several representations that capture the state of the interaction, rather than the state of the robot. Borrowing notions of topology invariances and homotopy classes, we design task spaces based on winding numbers and writhe for synthesizing winding motion, and electro-static fields for planning reaching and grasping motion. Our experiments show that these representations capture the motion, preserving its qualitative properties, while generalising over finer geometrical detail. Based on the same motivation, we utilise a scale and rotation invariant representation for locally preserving distances, called interaction mesh. The interaction mesh allows for transferring motion between robots of different scales (motion re-targeting), between humans and robots (teleoperation) and between different environments (motion adaptation). To estimate the state of the environment we employ real-time sensing techniques utilizing dense stereo tracking, magnetic tracking sensors and inertia measurements units. We combine and exploit these representations for synthesis and generalization of motion in dynamic environments. The benefit of this method is on problems where direct planning in joint space is extremely hard whereas local optimal control exploiting topology and metric of these novel representations can efficiently compute optimal trajectories. We formulate this approach in the framework of optimal control as an approximate inference problem. This allows for consistent combination of multiple task spaces (e.g. end-effector, joint space and the abstract task spaces we investigate in this thesis). Motion generalization to novel situations and kinematics is similarly performed by projecting motion from abstract representations to joint configuration space. This technique, based on operational space control, allows us to adapt the motion in real time. This process of real-time re-mapping generates robust motion, thus reducing the amount of re-planning.We have implemented our approach as a part of an open source project called the Extensible Optimisation library (EXOTica). This software allows for defining motion synthesis problems by combining task representations and presenting this problem to various motion planners using a common interface. Using EXOTica, we perform comparisons between different representations and different planners to validate that these representations truly improve the motion planning.

http://hdl.handle.net/1842/10520

en

The University of Edinburgh

Ivan, V., Zarubin, D., Toussaint, M., Komura, T., and Vijayakumar, S. (2013). Topology-based Representations for Motion Planning and Generalisation in Dynamic Environments with Interactions. The International Journal of Robotics Research, 32(9-10):1151–1163.

Llamazares, Á., Ivan, V., Molinos, E., Ocaña, M., and Vijayakumar, S. (2013). Dynamic Obstacle Avoidance Using Bayesian Occupancy Filter and Approximate Inference. Sensors, 13(3):2929–2944.

Llamazares, Á., Ivan, V., Ocaña, M., and Vijayakumar, S. (2012). Dynamic obstacle avoidance minimizing energy consumption. In Intelligent Vehicles Workshop on Perception in Robotics, Alcalá de Henares, Spain.

Pauwels, K., Ivan, V., Ros, E., and Vijayakumar, S. (2014a). Real-time Object Pose Recognition and Tracking with an Imprecisely Calibrated Moving RGB-D Camera. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, Illinois, USA.

Pauwels, K., Rubio, L., Ivan, V., Vijayakumar, S., and Ros, E. (2014b). Real-time RGB-D-based Object and Manipulator Pose Estimation. Workshop on RGB-D: Advanced Reasoning with Depth Cameras in conjunction with Conference on Intelligent Robots and Systems (IROS).

Sandilands, P., Ivan, V., Komura, T., and Vijayakumar, S. (2013). Dexterous Reaching, Grasp Transfer and Planning Using Electrostatic Representations. In Proceedings of IEEE-RAS International Conference on Humanoid Robots (Humanoids), Atlanta, Georgia, USA.

Zarubin, D., Ivan, V., Toussaint, M., Komura, T., and Vijayakumar, S. (2012). Hierarchical Motion Planning in Topological Representations. In Proceedings of Robotics: Science and Systems (R:SS), Sydney, Australia.

robot motion

synthesizing winding motion

interaction mesh

motion re-targeting

teleoperation

Topology based representations for motion synthesis and planning

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy