Torsten Kröger

A depth space approach to human-robot collision avoidance

In this paper a real-time collision avoidance approach is presented for safe human-robot coexistence. The main contribution is a fast method to evaluate distances between the robot and possibly moving obstacles (including humans), based on the concept of depth space. The distances are used to generate repulsive vectors that are used to control the robot while executing a generic motion task. The repulsive vectors can also take advantage of an estimation of the obstacle velocity. In order to preserve the execution of a Cartesian task with a redundant manipulator, a simple collision avoidance algorithm has been implemented where different reaction behaviors are set up for the end-effector and for other control points along the robot structure. The complete collision avoidance framework, from perception of the environment to joint-level robot control, is presented for a 7-dof KUKA Light-Weight-Robot IV using the Microsoft Kinect sensor. Experimental results are reported for dynamic environments with obstacles and a human.

Online Trajectory Generation: Basic Concepts for Instantaneous Reactions to Unforeseen Events

This paper introduces a new method for motion-trajectory generation of mechanical systems with multiple degrees of freedom (DOFs). The key feature of this new concept is that motion trajectories are generated online, i.e., within every control cycle, typically every millisecond. This enables systems to react instantaneously to unforeseen and unpredictable (sensor) events at any time instant and in any state of motion. As a consequence, (multi)sensor integration in robotics, in particular the development of control systems enabling sensor-guided and sensor-guarded motions, becomes greatly simplified. We introduce a class of online trajectory-generation algorithms and present the mathematical basics of this new approach. The algorithms presented here consist of three steps: calculation of the minimum synchronization time for all DOFs, synchronization of all DOFs, and calculation of output values. The theory is followed by real-world experimental results indicating new possibilities in robot-motion control.

Error-tolerant execution of complex robot tasks based on skill primitives

This paper presents a general approach to specify and execute complex robot tasks considering uncertain environments. Robot tasks are defined by a precise definition of so-called skill primitive nets, which are based on Masons hybrid force/velocity and position control concept, but it is not limited to force/velocity and position control. Two examples are given to illustrate the formally defined skill primitive nets. We evaluated the controller and the trajectory planner by several experiments. Skill primitives suite very well as interface to robot control systems. The presented hybrid control approach provides a modular, flexible, and robust system; stability is guaranteed, particularly at transitions of two skill primitives. With the interface explained here, the results of compliance motion planning become possible to be examined in real work cells. We have implemented an algorithm to search for mating directions in up to three-dimensional configuration-spaces. Thereby, on one hand we have released compliant motion control concepts and on the other hand we can provide solutions for fine motion and assembly planning. This paper shows, how these two fields can be combined by the general concept of skill primitive nets introduced here, in order to establish a powerful system, which is able to automatically execute prior calculated assembly plans based on CAD-data in uncertain environments.

Executing assembly tasks specified by manipulation primitive nets

Numerous scientific publications in the open literature show approaches for automatic assembly planning, automated robot programming, notations for the task frame formalism, robot control architectures for hybrid control methods, and respective experimental results in these areas. But there are still significant gaps between these individual fields. Considering the whole chain, from assembly planning via autonomous robot programming to the execution of complex robot tasks, the latter part of it is discussed in this paper: manipulation of primitive nets as output of task planning systems are decomposed into single manipulation primitives, which are subsequently used to generate parameters for hybrid control. A hybrid controller computes set-points for a joint position controller. Our aim is to define versatile interfaces between the mentioned disciplines, in order to close the gaps between them. Derived from the task-frame formalism, manipulation primitives constitute the base interface in this sense. After its description, the composition of manipulation primitive nets is described. Regarding the control architecture, the interpretation of manipulation primitives as atomic commands and the setting of unambiguous low level control parameters is discussed. Subsequently, the software architecture necessary to realize the complex control structure for compliant motion, is introduced. To highlight the meaning for practical implementations, several experimental results of sample assembly tasks are shown.

Opening the door to new sensor-based robot applications—The Reflexxes Motion Libraries

This paper introduces the Reflexxes Motion Libraries and describes, how they open doors for next generation robot motion controllers. When robots become capable to perform sensor-guided and sensor-guarded motions, there is no predefined path anymore, and motions have to be calculated on-line, that is, during the motion. The Reflexxes Motion Libraries calculate jerk-limited motions within one control cycle only (typically 1ms or less). This way, robots can instantaneously react to unforeseen sensor events, which opens the door to a huge number of new robot capabilities and fundamentally new motion control features. For instance: unforeseen switchings of coordinate frames, unforeseen switchings of control state spaces, deterministic and instantaneous reactions to sensor signals, safe and stable reactions to sensor failures, simple visual servo control, and stable switched-system control. All these features are important for the execution of sensor-based robot motions and to realize new applications as will be outlined in this paper.

On-line trajectory generation in robotic systems : basic concepts for instantaneous reactions to unforeseen (sensor) events

Literature Survey: Trajectory Generation in and Control of Robotic Systems.- Mathematical Conventions and Problem Formulation.- Solution for One Degree of Freedom.- Solution in Multi-dimensional Space.- On-Line Generation of Homothetic Trajectories.- Hybrid Switched-System Control for Robotic Systems.- Experimental Results and Applications.- Further Discussion.- Summary, Future Work, and Conclusion.

Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards

This paper presents the Dexterity Network (Dex-Net) 1.0, a dataset of 3D object models and a sampling-based planning algorithm to explore how Cloud Robotics can be used for robust grasp planning. The algorithm uses a Multi- Armed Bandit model with correlated rewards to leverage prior grasps and 3D object models in a growing dataset that currently includes over 10,000 unique 3D object models and 2.5 million parallel-jaw grasps. Each grasp includes an estimate of the probability of force closure under uncertainty in object and gripper pose and friction. Dex-Net 1.0 uses Multi-View Convolutional Neural Networks (MV-CNNs), a new deep learning method for 3D object classification, to provide a similarity metric between objects, and the Google Cloud Platform to simultaneously run up to 1,500 virtual cores, reducing experiment runtime by up to three orders of magnitude. Experiments suggest that correlated bandit techniques can use a cloud-based network of object models to significantly reduce the number of samples required for robust grasp planning. We report on system sensitivity to variations in similarity metrics and in uncertainty in pose and friction. Code and updated information is available at http://berkeleyautomation.github.io/dex-net/.

Towards On-Line Trajectory Computation

This paper proposes a new way of trajectory generation for industrial manipulators. A real-time algorithm for the interpolation of synchronized and time-optimal manipulator trajectories with arbitrary input values is presented. The method has been developed and implemented for multi-sensor systems, where sensor events can abruptly change desired target positions and trajectory constraints from one control cycle to another (i.e. maximum velocities, accelerations, and jerks). This work mainly presents a simple on-line second-order-trajectory generator and gives an outlook to a third-order-trajectory generator. Both algorithms generate time-optimal position progressions and require computational three steps: A. determination of the degree of freedom that requires the longest execution time, B. synchronization of all degrees of freedom by adapting maximum velocities and accelerations, C. calculation of new output values (position, velocity, and acceleration). Experimental results as well as a description of how to integrate this approach into manipulation control architectures are presented

Manipulation Primitives — A Universal Interface between Sensor-Based Motion Control and Robot Programming

This paper introduces a generic framework for sensor-based robot motion control. The key contribution is the introduction of an adaptive selection matrix for sensor-based hybrid switched-system control. The overall control system consists of multiple sensors and open- and closed-loop controllers, in-between which the adaptive selection matrix can switch discretely in order to supply command variables for low-level controllers of robotic manipulators. How control signals are chosen, is specified by Manipulation Primitives, which constitute the interface to higher-level applications. This programming paradigm is formally specified in order to establish the possibility of executing sensor-guided and sensor-guarded motion commands simultaneously and in a very open way, such that any kind and any number of sensors can be addressed. A further key feature of this generic approach is, that the control structure can be directly mapped to a corresponding software architecture. The resulting control system is freely scalable depending on the performance requirements of the desired system.

On-line rigid object recognition and pose estimation based on inertial parameters

This paper proposes an object recognition and gripping pose estimation approach based on on-line estimation of the complete set of inertial parameters, i.e. the mass, the coordinates of the center of mass, and the elements of the inertia matrix, of an object gripped by or attached to a manipulator. A multi-sensor fusion approach combining 6D force/torque, 6D acceleration, 3D angular velocity, and joint angle data to estimate these parameters is presented. In order to facilitate practical implementation, approaches to handling force/torque sensor offsets and to compensating the forces/torques caused by the distal mounting plate of the force/torque sensor and the gripper are incorporated. Regarding the joint angle signals, preprocessing steps to derive the angular velocity, linear acceleration and angular acceleration vector w.r.t. the sensor frame are addressed. The estimation of the complete set of inertial parameters employing the recursive instrumental variables (RIV) method is discussed. The extraction of features that are invariant w.r.t. translation and rotation, i.e. the mass and the principal moments of inertia, as well as a recognition approach based on the Kullback-Leibler divergence are presented. Experimental results show very low errors in the estimates of the inertial parameters, good pose estimation accuracy, and the viability of the recognition approach.