Human-robot collaboration while sharing production activities in dynamic environment: SPADER system

Interactive robot doing collaborative work in hybrid work cell need adaptive trajectory planning strategy. Indeed, systems must be able to generate their own trajectories without colliding with dynamic obstacles like humans and assembly components moving inside the robot workspace. The aim of this paper is to improve collision-free motion planning in dynamic environment in order to insure human safety during collaborative tasks such as sharing production activities between human and robot. Our system proposes a trajectory generating method for an industrial manipulator in a shared workspace. A neural network using a supervised learning is applied to create the waypoints required for dynamic obstacles avoidance. These points are linked with a quintic polynomial function for smooth motion which is optimized using least-square to compute an optimal trajectory. Moreover, the evaluation of human motion forms has been taken into consideration in the proposed strategy. According to the results, the proposed approach is an effective solution for trajectories generation in a dynamic environment like a hybrid workspace.

[1]  E Dombre,et al.  Chapter 13 – Trajectory generation , 2002 .

[2]  S. Sastry,et al.  Autonomous Exploration in Unknown Urban Environments for Unmanned Aerial Vehicles , 2005 .

[3]  Aude Billard,et al.  A dynamical system approach to realtime obstacle avoidance , 2012, Autonomous Robots.

[4]  Claudio Melchiorri,et al.  Online trajectory planning and filtering for robotic applications via B-spline smoothing filters , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Christian Wurll,et al.  Point-to-point and multi-goal path planning for industrial robots , 2001, J. Field Robotics.

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

[7]  Huang Xiang,et al.  Trajectory Planning Algorithm Based on Quaternion for 6-DOF Aircraft Wing Automatic Position and Pose Adjustment Method , 2010 .

[8]  Napsiah Ismail,et al.  A Review on Robot Motion Planning Approaches , 2012 .

[9]  Marek Balazinski,et al.  A hierarchical neuro-fuzzy system to near optimal-time trajectory planning of redundant manipulators , 2008, Eng. Appl. Artif. Intell..

[10]  Kamal K. Gupta,et al.  Safety Hierarchy for Planning With Time Constraints in Unknown Dynamic Environments , 2014, IEEE Transactions on Robotics.

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

[12]  Andrew Y. C. Nee,et al.  Interactive robot trajectory planning and simulation using Augmented Reality , 2012 .

[13]  Philippe Cardou,et al.  Safer hybrid workspace using human-robot interaction while sharing production activities , 2014, 2014 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Proceedings.

[14]  S. Rajasekaran,et al.  A Neural Network based Path Planning Algorithm for Extinguishing Forest Fires , 2012 .

[15]  José Antonio Cruz-Ledesma,et al.  Modelling, Design and Robust Control of a Remotely Operated Underwater Vehicle , 2014 .

[16]  John J. Craig Zhu,et al.  Introduction to robotics mechanics and control , 1991 .

[17]  Gheorghe Leonte Mogan,et al.  Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning , 2012 .

[18]  C. Lin,et al.  Formulation and optimization of cubic polynomial joint trajectories for industrial robots , 1983 .

[19]  Alexander Verl,et al.  Cooperation of human and machines in assembly lines , 2009 .

[20]  Bernardete Ribeiro,et al.  MONODA: a neural modular architecture for obstacle avoidance without knowledge of the environment , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[21]  Aude Billard,et al.  Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction , 2014, IEEE Transactions on Haptics.

[22]  Gunther Reinhart,et al.  Approaches of Applying Human-Robot-Interaction-Technologies to Assist Workers with Musculoskeletal Disorders in Production , 2012, ICIRA.

[23]  Michael Negnevitsky,et al.  Artificial Intelligence: A Guide to Intelligent Systems , 2001 .

[24]  Clément Gosselin,et al.  Interference estimated time of arrival on a 6-DOF cable-driven haptic foot platform , 2009, 2009 IEEE International Conference on Robotics and Automation.

[25]  Ingrid Moerman,et al.  A survey on wireless body area networks , 2011, Wirel. Networks.

[26]  Gamini Dissanayake,et al.  Collision-Free Trajectory Planning for Manipulator Using Virtual Force based Approach , 2007 .

[27]  Francisco Rubio,et al.  Industrial robot efficient trajectory generation without collision through the evolution of the optimal trajectory , 2016, Robotics Auton. Syst..

[28]  Jiong Tang,et al.  Development of a configuration space motion planner for robot in dynamic environment , 2009 .

[29]  Martin J.-D. Otis,et al.  Measuring Operator's Pain: Toward Evaluating Musculoskeletal Disorder at Work , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[30]  Hsien-I Lin,et al.  A Fast and Unified Method to Find a Minimum-Jerk Robot Joint Trajectory Using Particle Swarm Optimization , 2013, Journal of Intelligent & Robotic Systems.

[31]  Akira Abe.  Trajectory planning for flexible Cartesian robot manipulator by using artificial neural network: numerical simulation and experimental verification , 2011, Robotica.

[32]  Wisama Khalil,et al.  4. Trajectory Generation , 2010 .

[33]  Alessandro Gasparetto,et al.  Trajectory Planning in Robotics , 2012, Mathematics in Computer Science.

[34]  C. M. Vélez,et al.  Transition Management for the Smooth Flight of a Small Autonomous Helicopter , 2010, J. Intell. Robotic Syst..

[35]  Boubaker Daachi,et al.  Adaptive neural controller for redundant robot manipulators and collision avoidance with mobile obstacles , 2012, Neurocomputing.

[36]  Heinz Wörn,et al.  Online path planning with optimal C-space discretization , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[37]  Syamsiah Mashohor,et al.  Predicting the Motion of a Robot Manipulator with Unknown Trajectories Based on an Artificial Neural Network , 2014 .

[38]  Jun Kinugawa,et al.  Dynamic collision avoidance method for co-worker robot using time augmented configuration-space , 2016, 2016 IEEE International Conference on Mechatronics and Automation.

[39]  Philippe Cardou,et al.  A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection , 2014, 2014 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Proceedings.

[40]  Inna Sharf,et al.  Sampling-based A* algorithm for robot path-planning , 2014, Int. J. Robotics Res..

[41]  Jan Peters,et al.  Learning interaction for collaborative tasks with probabilistic movement primitives , 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.

[42]  Huashan Liu,et al.  Time-optimal and jerk-continuous trajectory planning for robot manipulators with kinematic constraints , 2013 .

[43]  Uk-Youl Huh,et al.  A Collision-Free G2 Continuous Path-Smoothing Algorithm Using Quadratic Polynomial Interpolation , 2014 .

[44]  Milad Geravand,et al.  Human-robot physical interaction and collaboration using an industrial robot with a closed control architecture , 2013, 2013 IEEE International Conference on Robotics and Automation.