During April and May 2012 our current results were presented at the workshop "Industry-Academia collaboration in the ECHORD project: a bridge for European robotics innovation" which was held at the ICRA 2012 conference in St. Paul, Minnesota. A draft for the multimedia report was created and work on both the simulated and real demonstrator was done.  Furthermore the framework for the generation of optimal paths was created together with LAAS-CNRS.

For our demonstrator we designed a test-problem in which a glass that contains water is moved by the robot. The motion is computed using trajectory optimization that minimize acceleration of the glass in certain directions to avoid spilling of water. The acquired results were presented at the workshop.

In our demonstrator the robot has to move a glass with liquid in a complex environment without spilling or collision with the environment or with the robot itself. A sample scenario of the demonstrator was designed in the path planning tool KINEO and various real world counterparts created that allow us to recreate the virtual scenarios in reality.

Together with LAAS-CNRS we created and formulated a framework for the generation of optimal paths. The path planner is used to compute an initial path whereas the trajectory optimizer uses the collision detection facilities of the path planner to ensure collision free paths for the optimal paths.

At LAAS-CNRS the last two months were dedicated to generate feasible optimized motions with MUSCOD.  We finalized the work on the path-planning framework: once the humanoid robot model is loaded, the user can choose the support joints (right foot, left foot, or both). The constrained manifold is then built from these constraints and collision-free quasi-static paths can be planned in any kind of environment.

Once the solution path is ready, it is fed to MUSCOD. So far we have been able to sucessfully generate minimum-jerk trajectories while keeping both feet on the ground and guaranteeing the robot balance. To do so we use the jerk and external forces as control inputs, and the state variables are the concatenation of the robot configuration, velocity and acceleration. We verified that the final motion was feasible on the OpenHRP dynamic simulator.

To guarantee robot balance and actuator torques validity, we need to compute the torques from the robot configuration, velocity, acceleration and external forces using an inverse dynamics algorithm. To do so we use a fast implementation of the recursive Newton-Euler algorithm that allows fast torques and gradients computation.

We have finalized the scenario of the demonstrator. The robot will start in the resting position facing a big shelf and oter obstacles , and will be asked to retrieve an object from the lower shelf and place it on the upper shelf. This will require the robot to avoid collisions with all obstacles and between its own bodies.

We have implemented the collision avoidance constraints in MUSCOD: self-collision pairs are added using the robot model, and collision pairs are added after loading the given environment. We are currently testing those constraints.