Reactive Planning for Assistive Robots

We consider a vehicle consisting of a robotic walking assistant pushed by a user. The robot can guide the person along a path and suggest a velocity by various means. The vehicle moves in a crowded environment and can detect other pedestrians in the surroundings. We propose a reactive planner that modifies the path in order to avoid pedestrians in the surroundings. The algorithm relies on a very accurate model to predict the motion of each pedestrian, i.e., the headed social force model. The possible trajectories for both the vehicle and the pedestrians are modeled as clothoid curves, which are efficient to manage from the numeric point of view and are very comfortable to follow for the user. Probabilistic techniques are used to account for the variability of the motion of each pedestrian. The path is efficient to generate, is collision free (up to a certain probability), and is comfortable to follow. Simulations and comparisons with a state-of-the-art planner using real data as well as experiments are reported to prove the effectiveness of the method.

[1]  Antonis A. Argyros,et al.  Navigation assistance and guidance of older adults across complex public spaces: the DALi approach , 2015, Intell. Serv. Robotics.

[2]  Joel W. Burdick,et al.  Probabilistic Collision Checking With Chance Constraints , 2011, IEEE Transactions on Robotics.

[3]  Gonzalo Ferrer,et al.  Proactive kinodynamic planning using the Extended Social Force Model and human motion prediction in urban environments , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Han-Pang Huang,et al.  Robot Motion Planning in Dynamic Uncertain Environments , 2011, Adv. Robotics.

[5]  Luigi Palopoli,et al.  Semi-analytical minimum time solutions with velocity constraints for trajectory following of vehicles , 2017, Autom..

[6]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[7]  Daniele Fontanelli,et al.  Walking Ahead: The Headed Social Force Model , 2017, PloS one.

[8]  E. Bertolazzi,et al.  G1 fitting with clothoids , 2015 .

[9]  Kai Oliver Arras,et al.  People tracking with human motion predictions from social forces , 2010, 2010 IEEE International Conference on Robotics and Automation.

[10]  Wolfram Burgard,et al.  Learning Motion Patterns of People for Compliant Robot Motion , 2005, Int. J. Robotics Res..

[11]  Luc Van Gool,et al.  You'll never walk alone: Modeling social behavior for multi-target tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[12]  Jean-Paul Laumond,et al.  From human to humanoid locomotion—an inverse optimal control approach , 2010, Auton. Robots.

[13]  Christian Laugier,et al.  Probabilistic navigation in dynamic environment using Rapidly-exploring Random Trees and Gaussian processes , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  E. Bertolazzi,et al.  Interpolating clothoid splines with curvature continuity , 2018 .

[15]  Luigi Palopoli,et al.  Path planning maximising human comfort for assistive robots , 2016, 2016 IEEE Conference on Control Applications (CCA).

[16]  P. Tsiotras,et al.  Minimum-Time Travel for a Vehicle with Acceleration Limits: Theoretical Analysis and Receding-Horizon Implementation , 2008 .

[17]  Axel Legay,et al.  Efficient customisable dynamic motion planning for assistive robots in complex human environments , 2015, J. Ambient Intell. Smart Environ..

[18]  Martin Buss,et al.  Safety assessment of robot trajectories for navigation in uncertain and dynamic environments , 2011, Autonomous Robots.

[19]  F. Large,et al.  Avoiding cars and pedestrians using velocity obstacles and motion prediction , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[20]  Andreas Krause,et al.  Robot navigation in dense human crowds: Statistical models and experimental studies of human–robot cooperation , 2015, Int. J. Robotics Res..

[21]  Luigi Palopoli,et al.  Trajectory planning for car-like vehicles: A modular approach , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[22]  Qiuming Zhu,et al.  Hidden Markov model for dynamic obstacle avoidance of mobile robot navigation , 1991, IEEE Trans. Robotics Autom..

[23]  Li-Chen Fu,et al.  Human-Centered Robot Navigation—Towards a Harmoniously Human–Robot Coexisting Environment , 2011, IEEE Transactions on Robotics.

[24]  S. LaValle,et al.  Randomized Kinodynamic Planning , 2001 .

[25]  Jean-Paul Laumond,et al.  An Optimality Principle Governing Human Walking , 2008, IEEE Transactions on Robotics.

[26]  Jean-Paul Laumond,et al.  On the nonholonomic nature of human locomotion , 2008, Auton. Robots.

[27]  Antonis A. Argyros,et al.  Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform , 2014, ECCV Workshops.

[28]  Robert Fitch,et al.  Bootstrapping navigation and path planning using human positional traces , 2013, 2013 IEEE International Conference on Robotics and Automation.

[29]  Masaki Takahashi,et al.  Human-centered X-Y-T-space path planning for mobile robot in dynamic environments , 2015, Robotics Auton. Syst..

[30]  Antonella De Angeli,et al.  Behavioural templates improve robot motion planning with social force model in human environments , 2013, 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA).

[31]  Luigi Palopoli,et al.  Indoor Positioning of a Robotic Walking Assistant for Large Public Environments , 2015, IEEE Transactions on Instrumentation and Measurement.