Combined workspace monitoring and collision avoidance for mobile manipulators

For safe human-robot interaction and co-existence, collision avoidance is a fundamental prerequisite. Therefore, in this contribution a Nonlinear Model Predictive Control approach for fixed-base and mobile manipulators is presented that allows for avoiding self-collisions and collisions with static and dynamic obstacles while performing tasks defined in the Cartesian space. The collision avoidance takes not only the end-effector but the complete robot consisting of both platform and manipulator into account and relies on a 3D obstacle representation obtained by fusing information from multiple depth sensors. The obstacle representation is applicable to all kinds of objects. It considers occlusions behind the obstacles and the robot to make a conservative assumption on the obstacle size. In order to achieve realtime reactions to obstacles, the obstacle information used in one control step is restricted to the most relevant obstacles determined by distance computation. The method is validated by means of simulation and by application to an omnidirectional mobile manipulator with 10 degrees of freedom.

[1]  Yoshifumi Kitamura,et al.  Real-time path planning in a dynamic 3-D environment , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[2]  Lihui Wang.  Collaborations towards adaptive manufacturing , 2012, Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[3]  Matteo Parigi Polverini,et al.  Real-time collision avoidance in human-robot interaction based on kinetostatic safety field , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Christian Frey,et al.  A 3D Representation of Obstacles in the Robots Reachable Area Considering Occlusions , 2014, ISR 2014.

[5]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[6]  Wolfram Burgard,et al.  OctoMap: an efficient probabilistic 3D mapping framework based on octrees , 2013, Autonomous Robots.

[7]  Li-Chen Fu,et al.  Grasping the object with collision avoidance of wheeled mobile manipulator in dynamic environments , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Oussama Khatib,et al.  Springer Handbook of Robotics , 2007, Springer Handbooks.

[9]  Oussama Khatib,et al.  A depth space approach to human-robot collision avoidance , 2012, 2012 IEEE International Conference on Robotics and Automation.

[10]  Kostas J. Kyriakopoulos,et al.  Nonholonomic navigation and control of cooperating mobile manipulators , 2003, IEEE Trans. Robotics Autom..

[11]  Reid G. Simmons,et al.  Voxel-based motion bounding and workspace estimation for robotic manipulators , 2012, 2012 IEEE International Conference on Robotics and Automation.

[12]  Aiguo Song,et al.  Obstacle avoidance for mobile manipulation by real-time sensor-based redundancy resolution , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[13]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..

[14]  Angelika Zube.  Cartesian nonlinear model predictive control of redundant manipulators considering obstacles , 2015, 2015 IEEE International Conference on Industrial Technology (ICIT).