ABSTRACT
Enabling natural and intuitive communication with robots calls for the design of intelligent user interfaces. As robots are introduced into applications with novice users, the information obtained from such users may not always be reliable. This paper describes a user interface approach to process and correct intended paths for robot navigation as sketched by users on a touchscreen. Our approach demonstrates that by processing video frames from an overhead camera and by using composite Bézier curves to interpolate smooth paths from a small set of significant points, low-resolution occupancy grid maps (OGMs) with numeric potential fields can be continuously updated to correct unsafe user-drawn paths at interactive speeds. The approach generates sufficiently complex paths that appear to bend around static and dynamic obstacles. The results of an evaluation study show that our approach captures the user intent while relieving the user from being concerned about her path-drawing abilities.
- Bresenham, J. E. Algorithm for computer control of a digital plotter. IBM Sys. J., 4(1):25--30, 1965. Google ScholarDigital Library
- Burke, D. et al. Multimodal interaction for human-robot teams. In Proc. SPIE Defense, Security, and Sensing, pp. 87410E--87410E, 2013.Google Scholar
- Choi, J.-W., Curry, R., and Elkaim, G. Path planning based on Bezier curve for autonomous ground vehicles. In Proc. World Cong. on Eng. and Computer Science, pp. 158--166, 2008. Google ScholarDigital Library
- Cincotti, F. et al. Non-invasive brain-computer interface system: Towards its application as assistive technology. Brain Research Bulletin, 75(6):796--803, 2008.Google ScholarCross Ref
- Delsart, V. and Fraichard, T. Navigating dynamic environments with trajectory deformation. J. of Computing and Information Tech., 17(1):27--36, 2009.Google ScholarCross Ref
- Forlizzi, J. and DiSalvo, C. Service robots in the domestic environment: A study of the roomba vacuum in the home. In Proc. ACM SIGCHI/SIGART Conf. Human-Robot Interaction, pp. 258--265, 2006. Google ScholarDigital Library
- Frank, J., Sahasrabudhe, Y., and Kapila, V. An augmented reality approach for reliable autonomous path navigation of mobile robots. In Proc. Indian Control Conf., 2015.Google Scholar
- Hart, P. E., Nilsson, N. J., and Raphael, B. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Sys. Science and Cybernetics, 4(2):100--107, 1968.Google ScholarCross Ref
- Huq, R. et al. Qbot: An educational mobile robot controlled in Matlab Simulink environment. In Proc. Canadian Conf. Electrical and Computer Eng., pp. 350--353, 2009.Google Scholar
- Hwang, J.-H., Arkin, R. C., and Kwon, D.-S. Mobile robots at your fingertip: Bezier curve on-line trajectory generation for supervisory control. In Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Sys., pp. 1444--1449, 2003.Google Scholar
- Khatib, O. Real-time obstacle avoidance for manipulators and mobile robots. The Int. J. of Robotics Research, 5(1):90--98, 1986. Google ScholarDigital Library
- Koenig, S. and Likhachev, M. Fast replanning for navigation in unknown terrain. IEEE Trans. Robotics, 21(3):354--363, 2005. Google ScholarDigital Library
- Kurniawati, H. and Fraichard, T. From path to trajectory deformation. In Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Sys., pp. 159--164, 2007.Google ScholarCross Ref
- Lamiraux, F., Bonnafous, D., and Lefebvre, O. Reactive path deformation for nonholonomic mobile robots. IEEE Trans. Robotics, 20(6):967--977, 2004. Google ScholarDigital Library
- Latombe, J. Robot Motion Planning. Kluwer Academic Publishers, New York, NY, 1991. Google ScholarCross Ref
- Likhachev, M. et al. Anytime dynamic A*: An anytime, replanning algorithm. In Int. Conf. Automated Planning and Scheduling, pp. 262--271, 2005.Google Scholar
- Linquan, Y. et al. Path planning algorithm for mobile robot obstacle avoidance adopting Bezier curve based on genetic algorithm. In Proc. Chinese Control and Decision Conf., pp. 3286--3289, 2008.Google Scholar
- Micire, M. et al. Multi-touch interaction for robot control. In Proc. Int. Conf. Intelligent User Interfaces, pp. 425--428, 2009. Google ScholarDigital Library
- Nilsson, N. J. Principles of Artificial Intelligence. Springer, 1982. Google ScholarCross Ref
- Perzanowski, D. et al. Building a multimodal human-robot interface. IEEE Intelligent Sys., 16(1):16--21, 2001. Google ScholarDigital Library
- Sakamoto, D. et al. Sketch and run: A stroke-based interface for home robots. In Proc. SIGCHI Conf. Human Factors in Computing Sys., pp. 197--200, 2009. Google ScholarDigital Library
- Sato, N., Kon, K., and Matsuno, F. Navigation interface for multiple autonomous mobile robots with grouping function. In Proc. IEEE Int. Symp. Safety, Security, and Rescue Robotics, pp. 32--37, 2011.Google ScholarCross Ref
- Škrjanc, I. and Klančar, G. Optimal cooperative collision avoidance between multiple robots based on bernstein-bézier curves. Robotics and Autonomous Sys., 58(1):1--9, 2010. Google ScholarDigital Library
- Skubic, M., Bailey, C., and Chronis, G. A sketch interface for mobile robots. In Proc. IEEE Int. Conf. Sys., Man and Cybernetics, pp. 919--924, 2003.Google ScholarCross Ref
- Skubic, M. et al. Using a hand-drawn sketch to control a team of robots. Autonomous Robots, 22(4):399--410, 2007. Google ScholarDigital Library
- Stentz, A. Optimal and efficient path planning for partially-known environments. In Proc. IEEE Int. Conf. Robotics and Automation, pp. 3310--3317, 1994.Google ScholarCross Ref
- Stentz, A. The focussed D* algorithm for real-time replanning. In Proc. Int. Joint Conf. Artificial Intelligence, pp. 1652--1659, 1995. Google ScholarDigital Library
- Sugihara, K. and Smith, J. Genetic algorithms for adaptive motion planning of an autonomous mobile robot. In Proc. IEEE Int. Symp. Computational Intelligence in Robotics and Automation, pp. 138--143, 1997. Google ScholarDigital Library
- Yanco, H. A. Wheelesley: A robotic wheelchair system: Indoor navigation and user interface. In Proc. Assistive Tech. and Artificial Intelligence, pp. 256--268. 1998. Google ScholarDigital Library
- Yang, K. and Sukkarieh, S. 3D smooth path planning for a UAV in cluttered natural environments. In Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Sys., pp. 794--800, 2008.Google Scholar
- Yang, S. X. and Meng, M. An efficient neural network method for real-time motion planning with safety consideration. Robotics and Autonomous Sys., 32(2):115--128, 2000.Google ScholarCross Ref
- Zelinsky, A. and Yuta, S. Reactive path planning for a mobile robot using numeric potential fields. In Proc. Int. Conf. Intelligent Autonomous Sys., pp. 84--93, 1993.Google Scholar
Index Terms
- Path Bending: Interactive Human-Robot Interfaces With Collision-Free Correction of User-Drawn Paths
Recommendations
A modular user interface of robots
Proceedings of the 2007 conference on Human interface: Part IThis paper discusses development of a control system for home robots. When a user requests a user interface for home robot, the home robot sends this robot's user interface to the user's PDA. Then, the robot can be efficiently controlled from this user ...
Vision Aided Path Planning for Mobile Robot
ICCCE '14: Proceedings of the 2014 International Conference on Computer and Communication EngineeringPath planning is very important for autonomous mobile robots to navigate from the beginning to the ending position. Vision aided path planning for mobile robot system is discussed in this paper. The paper reveals the accounts from a historical overview ...
Vision-Based Humanoid Robot Navigation in a Featureless Environment
MCPR 2015: Proceedings of the 7th Mexican Conference on Pattern Recognition - Volume 9116One of the most basic tasks for any autonomous mobile robot is that of safely navigating from one point to another e.g. service robots should be able to find their way in different kinds of environments. Typically, vision is used to find landmarks in ...
Comments