Efficient customisable dynamic motion planning for assistive robots in complex human environments

People with impaired physical and mental ability often find it challenging to negotiate crowded or unfamiliar envi- ronments, leading to a vicious cycle of deteriorating mobility and sociability. To address this issue we present a novel motion planning algorithm that is able to intelligently deal with crowded areas, permanent or temporary anomalies in the environment (e.g., road blocks, wet floors) as well as hard and soft constraints (e.g., "keep a toilet within reach of 10 meters during the jour- ney", "always avoid stairs"). Constraints can be assigned a priority tailored on the user's needs. The planner has been validated by means of simulations and experiments with elderly people within the context of the DALi European project.

[1]  Dorothea Wagner,et al.  Time-Dependent Route Planning , 2009, Encyclopedia of GIS.

[2]  Emilio Frazzoli,et al.  Minimum-violation LTL planning with conflicting specifications , 2013, 2013 American Control Conference.

[3]  Benjamin Kuipers,et al.  A framework for planning comfortable and customizable motion of an assistive mobile robot , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Julie Fleury,et al.  Interventions for promoting mobility in community‐dwelling older adults , 2009, Journal of the American Academy of Nurse Practitioners.

[5]  Xulei Wang,et al.  A hierarchical pedestrians motion planning model for heterogeneous crowds simulation , 2009, 2009 International Conference on Information and Automation.

[6]  Subhash Suri,et al.  On the Complexity of Time-Dependent Shortest Paths , 2011, Algorithmica.

[7]  A. Weightman,et al.  Community wide interventions for increasing physical activity , 2011, The Cochrane database of systematic reviews.

[8]  Marina L. Gavrilova,et al.  Voronoi diagram in optimal path planning , 2007, 4th International Symposium on Voronoi Diagrams in Science and Engineering (ISVD 2007).

[9]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[10]  Emilio Frazzoli,et al.  Sampling-based algorithms for optimal motion planning , 2011, Int. J. Robotics Res..

[11]  Steven M. LaValle,et al.  Robot motion planning in a changing, partially predictable environment , 1994, Proceedings of 1994 9th IEEE International Symposium on Intelligent Control.

[12]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[13]  Rachid Alami,et al.  Human-aware robot navigation: A survey , 2013, Robotics Auton. Syst..

[14]  Roberto Passerone,et al.  Embedded soundscape rendering for the visually impaired , 2013, 2013 8th IEEE International Symposium on Industrial Embedded Systems (SIES).

[15]  Axel Legay,et al.  Motion planning in crowds using statistical model checking to enhance the social force model , 2013, 52nd IEEE Conference on Decision and Control.

[16]  Christos H. Papadimitriou,et al.  The Euclidean Traveling Salesman Problem is NP-Complete , 1977, Theor. Comput. Sci..

[17]  Luigi Palopoli,et al.  Unicycle steering by brakes: A passive guidance support for an assistive cart , 2013, 52nd IEEE Conference on Decision and Control.

[18]  Ilse de Bourdeaudhuij,et al.  Environmental factors influencing older adults’ walking for transportation: a study using walk-along interviews , 2012, International Journal of Behavioral Nutrition and Physical Activity.

[19]  D. Nemet,et al.  Health-related knowledge and preferences in low socio-economic kindergarteners , 2012, International Journal of Behavioral Nutrition and Physical Activity.

[20]  Norihiro Hagita,et al.  Human-comfortable navigation for an autonomous robotic wheelchair , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  Yoshiaki Shirai,et al.  Hierarchical Vision-motion Planning With Uncertainty: Local Path Planning And Global Route Selection , 1992, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Wolfram Burgard,et al.  MINERVA: a second-generation museum tour-guide robot , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[23]  Charles R. Dyer,et al.  The space efficiency of quadtrees , 1982, Comput. Graph. Image Process..

[24]  Farnoush Banaei Kashani,et al.  A case for time-dependent shortest path computation in spatial networks , 2010, GIS '10.

[25]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[26]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[27]  Charles W. Warren,et al.  Global path planning using artificial potential fields , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[28]  Danny Ziyi Chen,et al.  A framed-quadtree approach for determining Euclidean shortest paths in a 2-D environment , 1997, IEEE Trans. Robotics Autom..

[29]  Domenico Prattichizzo,et al.  Cooperative human-robot haptic navigation , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[30]  S. LaValle.  Rapidly-exploring random trees : a new tool for path planning , 1998 .

[31]  Fernando Arámbula Cosío,et al.  Autonomous robot navigation using adaptive potential fields , 2004, Math. Comput. Model..

[32]  Leo Joskowicz,et al.  Motion planning in crowded planar environments , 1999, Robotica.

[33]  Jon Louis Bentley,et al.  Quad trees a data structure for retrieval on composite keys , 1974, Acta Informatica.

[34]  Lydia E. Kavraki,et al.  This Time the Robot Settles for a Cost: A Quantitative Approach to Temporal Logic Planning with Partial Satisfaction , 2015, AAAI.

[35]  Andrew Coles,et al.  LPRPG-P: Relaxed Plan Heuristics for Planning with Preferences , 2011, ICAPS.

[36]  Jeffrey Xu Yu,et al.  Finding time-dependent shortest paths over large graphs , 2008, EDBT '08.

[37]  L. Hawkley,et al.  Perceived social isolation and cognition , 2009, Trends in Cognitive Sciences.

[38]  Yiannis Demiris,et al.  Collaborative Control for a Robotic Wheelchair: Evaluation of Performance, Attention, and Workload , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).