Thema: Probabilistic Motion planning

Goal: Model based planning via message passing and Kalman Smoothing(KS)
(planning as Inference)

Experiments:

1) 1-D cart+sensor-simulation – proof of concept( physics+dynamics sim, planning and KS)
2) 2-D robot sim as kinematic chain with beam sensor and orientations constraints
3) CopelliaSim (+ ROS with own planning algorithm)
4) Franka Panda

Roadmap:
1) Toy task – 1-D physics and dynamics simulation of the cart
2) Toy task – baseline planning (positioning via Euler-distance)
3) Toy task – implementing Kalman Smoother
4) Toy task 2 – 2-D Robot arm (2 links with endeffector)
5) Toy task 2 – Planning in Cartesian space – Inverse kinematic + Kalman Smoother
6) CoppeliaSim of Franka with 3-D printed tool – simple tool guiding (orientation!!)
7) Real world testing with Franka
optional: ROS Implementation of the planning algoritm

Literature: Bayesian Modeling and Machine Learning (Chapter 23, p 493 – KS)