A Multi-Sensorial Hybrid Control for Robotic Manipulation in Human-Robot Workspaces

Autonomous manipulation in semi-structured environments where human operators can interact is an increasingly common task in robotic applications. This paper describes an intelligent multi-sensorial approach that solves this issue by providing a multi-robotic platform with a high degree of autonomy and the capability to perform complex tasks. The proposed sensorial system is composed of a hybrid visual servo control to efficiently guide the robot towards the object to be manipulated, an inertial motion capture system and an indoor localization system to avoid possible collisions between human operators and robots working in the same workspace, and a tactile sensor algorithm to correctly manipulate the object. The proposed controller employs the whole multi-sensorial system and combines the measurements of each one of the used sensors during two different phases considered in the robot task: a first phase where the robot approaches the object to be grasped, and a second phase of manipulation of the object. In both phases, the unexpected presence of humans is taken into account. This paper also presents the successful results obtained in several experimental setups which verify the validity of the proposed approach

Randomized Planning and Control Strategy for Whole-Arm Manipulation of a Slippery Polygonal Object

The present paper introduces a planning and control strategy for whole-arm manipulation of a slippery polygonal object. Randomized planning methods are first proposed in order to generate contact state transitions, which help not only to reduce the amount of calculation required, but also to handle a hybrid system composed of a continuous system and a discrete system, which has a large search space and complicated constraints. Second, a novel control strategy, which can switch manipulation modes among quasi-static, dynamic, and caging manipulation depending on the situation, is proposed. This strategy not only can cope with changes in the mechanics of the system caused by contact state transitions, but also can increase the manipulation feasibility and reliability. The validity of the proposed methods is verified through simulations and experiments

Szenenabhängige Online-Adaption von Manipulationssequenzen für einen Serviceroboter

Um in alltäglichen Umgebungen agieren zu können, muss ein Serviceroboter in der Lage sein, seine Handlung zielgerichtet an neue Szenen anzupassen. In dieser Arbeit werden dazu Handlungspläne zur Manipulation durch symbolische Planung erzeugt. Damit diese konsistent mit der realen Umgebung sind, wird aufgrund von sensorischer Wahrnehmung ein symbolisches Modell der Umgebung erzeugt. Dieses erlaubt es dem Roboter, die Effekte einer Aktion, bzw. Aktionsfolge auf seine Umwelt vorherzusagen