In recent decades, the only impact of robotics on real-world applications has been con- fined to the execution of predetermined, repetitive tasks in controlled industrial environ- ments. Although recent advances in all fields of robotics research have led to the devel- opment of a first generation of highly actuated, multi-sensory equipped machines, they still fall short of the range of activities humans are capable of. With the goal of having robots operate autonomously in everyday domestic environments, it is certainly neces- sary that human-like dynamics can be performed to a certain degree. To foster research in this direction, it is therefore often proposed to engage robots in sporting benchmark activities as these dynamic tasks are demanding for the robot’s mechanical, sensory and computational capabilities and also require a high quality of integration.
This dissertation is part of the work in making a humanoid robot perform such a dy- namic task, namely enabling DLR’s mobile humanoid robot Rollin’ Justin to catch up to two simultaneously thrown balls, where each ball is caught with one of its hands. To be more specific, this thesis is concerned with the perception system. Despite being a clearly defined task with easily assessable performance even for non-specialists, it is still demanding and underlines the challenges for realizing dynamic tasks in general. The challenges are: Obtain the trajectory of the thrown balls with the necessary accuracy to move the arms to the right position at the right time; handle unmodeled shaking of the robot caused by the dynamic nature of the task; avoid computational latencies while pro- cessing sensor signals to ensure proper execution within the short duration of the ball flight.
From a perception point of view, this requires solving two separate problems. Firstly, for meaningful evaluation of the input data, the geometric relationships between all sens- ing and actuation components of the robot have to be determined through calibration. Secondly, detection, tracking, and prediction of the ball during flight have to be per- formed in an accurate manner while considering that the robot’s cameras also move. Of course, this has to be performed in real-time.
Based on these requirements, this thesis contributes an automatic and self-contained method for calibrating all relevant sensors involved in the task. The highlights of the developed procedure are that it requires no external tools and no human assistance while achieving an accurate calibration. Furthermore, besides implementation of state-of-the- art approaches for tracking balls, a general tracking scheme is proposed that integrates detection and tracking in a fully probabilistic manner. Finally, besides contributions to the task of robotic catching, this thesis further covers the work of porting the obtained methods to a ball playing entertainment robot and additional calibration problems.
All presented methods and algorithms have been evaluated on the respective robots and were presented at trade fairs, public institute events and numerous lab demonstra- tions. Thus the methods have contributed to the development of sporting activities with humanoid robots and in doing so have extended the state of the art in service robotics.