Publications

@inproceedings{Reimann2010b,

title = {Generating collision free reaching movements for redundant manipulators using dynamical systems},

author = {H Reimann and I Iossifidis and G Schöner},

url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5650603},

doi = {10.1109/IROS.2010.5650603},

isbn = {978-1-4244-6674-0},

year  = {2010},

date = {2010-10-01},

booktitle = {2010 IEEE/RSJ International Conference on Intelligent Robots and Systems},

pages = {5372–5379},

publisher = {IEEE},

abstract = {For autonomous robots to manipulate objects in unknown environments, they must be able to move their arms without colliding with nearby objects, other agents or humans. The simultaneous avoidance of multiple obstacles in real time by all link segments of a manipulator is still a hard task both in practice and in theory. We present a systematic scheme for the generation of collision free movements for redundant manipulators in scenes with arbitrarily many obstacles. Based on the dynamical systems approach to robotics, constraints are formulated as contributions to a dynamical system that erect attractors for targets and repellors for obstacles. These contributions are formulated in terms of variables relevant to each constraint and then transformed into vector fields over the manipulator joint velocity vector as an embedding space in which all constraints are simultaneously observed. We demonstrate the feasibility of the approach by implementing it on a real anthropomorphic 8-degrees-of-freedom redundant manipulator. In addition, performance is characterized by detecting failures in a systematic simulation experiment in randomized scenes with varying numbers of obstacles.},

keywords = {attractor dynamics approach, central nervous system, collision avoidance, dynamical systems, manipulator dynamics, obstacle avoidance, redundant robot arm},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2009,

title = {Reaching while avoiding obstacles: a neuronally inspired attractor dynamics approach},

author = {Ioannis Iossifidis and Gregor Schöner},

doi = {10.3389/conf.neuro.10.2009.14.007},

year  = {2009},

date = {2009-01-01},

booktitle = {Bernstein Conference on Computational Neuroscience (BCCN 2009)},

keywords = {central nervous system, collision avoidance, dynamical systems, manipulator dynamics, obstacle avoidance, redundant robot arm},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2006a,

title = {Dynamical Systems Approach for the Autonomous Avoidance of Obstacles and Joint-limits for an Redundant Robot Arm},

author = {Ioannis Iossifidis and Gregor Schöner and Gregor Schoner},

doi = {10.1109/IROS.2006.282468},

isbn = {1-4244-0258-1},

year  = {2006},

date = {2006-10-01},

booktitle = {2006 IEEE/RSJ International Conference on Intelligent Robots and Systems},

pages = {580–585},

publisher = {IEEE},

abstract = {We extend the attractor dynamics approach to generate goal-directed movement of a redundant, anthropomorphic arm while avoiding dynamic obstacles and respecting joint limits. To make the robot's movements human-like, we generate approximately straight-line trajectories by using two heading direction angles of the tool-point quite analogously to how movement is represented in the primate central nervous system. Two additional angles control the tool's spatial orientation so that it follows the tool-point's collision-free path. A fifth equation governs the redundancy angle, which controls the elevation of the elbow so as to avoid obstacles and respect joint limits. These variables make it possible to generate movement while sitting in an attractor (or, in the language of the potential field approach, in a minimum). We demonstrate the approach on an assistant robot, which interacts with human users in a shared workspace},

keywords = {attractor dynamics approach, collision avoidance, manipulator dynamics, obstacle avoidance, primate central nervous system, redundant robot arm, telerobotics},

pubstate = {published},

tppubtype = {inproceedings}

}