Abstract
The velocity obstacle (VO) method is one of local path generation method considering a velocity of obstacles. By dividing an available velocity region into collision and collision-free area, a robot can avoid collisions using the VO. However, if there are numerous obstacles near a robot, the robot will have very few velocity candidates. In this paper, a method to choose an optimal velocity by introducing a cost function about safety of the velocity, and the cost function consists of a pass-time and a clearance. By latticizing available velocity map of a robot, each velocity can be evaluated from the cost function and a robot can select better velocity among collision-free velocity candidates. A performance of introduced method is compared to other VO method using simulation, and experiments are conducted to verify the results of simulation.
Similar content being viewed by others
References
Abe, Y., & Matsuo, Y. (2001). Collision avoidance method for multiple autonomous mobile agents by implicit cooperation. In Intelligent robots and systems, 2001. Proceedings. 2001 IEEE/RSJ international conference (Vol. 3).
Betts, J. T. (1998). Survey of numerical methods for trajectory optimization. Journal of Guidance, Control, and Dynamics, 21.2, 193–207.
Choi, D., Kim, M., & Jun-Ho, O. (2012). Development of a rapid mobile robot with a multi-degree-of-freedom inverted pendulum using the model-based Zero-Moment Point stabilization method. Advanced Robotics, 26(5–6), 515–535.
Choset, H., et al. (2000). Sensor-based exploration: Incremental construction of the hierarchical generalized Voronoi graph. The International Journal of Robotics Research, 19.2, 126–148.
Fiorini, P., & Shiller, Z. (1993). Motion planning in dynamic environments using the relative velocity paradigm. In IEEE conference on robotics and automation (pp. 560–565)
Fiorini, P., & Shiller, Z. (1998). Motion planning in dynamic environments using velocity obstacles. The International Journal of Robotics Research, 17(7), 760–772.
Fod, A., Howard, A., & Maja J. M. (2002). A laser-based people tracker. In Robotics and automation, 2002. Proceedings. ICRA’02. IEEE international conference (Vol. 3).
Fox, D., Burgard, W., & Thrun, S. (1997). The dynamic window approach to collision avoidance. IEEE Robotics and Automation Magazine, 4(1), 23–33.
Fulgenzi, C., Spalanzani, A., & Laugier, C. (2007). Dynamic obstacle avoidance in uncertain environment combining PVOs and occupancy grid. In Robotics and automation, 2007 IEEE international conference.
Guy, S. J., et al. (2009). Clearpath: Highly parallel collision avoidance for multiagent simulation. In Proceedings of the 2009 ACM SIGGRAPH/eurographics symposium on computer animation. ACM.
Kanda, T., et al. (2008). Who will be the customer?: a social robot that anticipates people’s behavior from their trajectories. In Proceedings of the 10th international conference on Ubiquitous computing. ACM.
Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research, 5(1), 90–98.
Kim, J., Kim, M., & Kim, D. (2011). Variants of the quantized visibility graph for efficient path planning. Advanced Robotics, 25(18), 2341–2360.
Koren, Y. (1991). Potential field methods and their inherent limitations for mobile robot navigation. In Proceedings of the IEEE conference on robotics and automation, Sacramento, CA (pp. 1398–1404).
Kwak, D. J., Choi, B., & Jin Kim, H. (2013). Trajectory optimization using virtual motion camouflage and particle swarm optimization. Berlin: Intelligent Robotics and Applications. Springer.
Large, F., et al. (2002). Using non-linear velocity obstacles to plan motions in a dynamic environment. In Control, automation, robotics and vision, 2002. ICARCV 2002. 7th international conference (Vol. 2).
Sedighi, K. H., et al. (2004). Autonomous local path planning for a mobile robot using a genetic algorithm. In Evolutionary computation, 2004. CEC2004. Congress (Vol. 2).
Snape, J., et al. (2009). Independent navigation of multiple mobile robots with hybrid reciprocal velocity obstacles. In Intelligent robots and systems, 2009. IROS 2009. IEEE/RSJ international conference.
Van den Berg, J., Lin, M., & Manocha, D. (2008). Reciprocal velocity obstacles for real-time multi-agent navigation. In Robotics and automation, 2008. ICRA 2008. IEEE international conference.
von Stryk, O., & Roland, B. (1992). Direct and indirect methods for trajectory optimization. Annals of Operations Research, 37.1, 357–373.
Wilkie, D., van den Berg, J., & Manocha, D. (2009). Generalized velocity obstacles. In Intelligent robots and systems, 2009. IROS 2009. IEEE/RSJ international conference.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kim, M., Oh, JH. Study on optimal velocity selection using velocity obstacle (OVVO) in dynamic and crowded environment. Auton Robot 40, 1459–1470 (2016). https://doi.org/10.1007/s10514-015-9520-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10514-015-9520-6