ABSTRACT
This paper considers the problem of motion planning for a hybrid robotic system with complex and nonlinear dynamics in a partially unknown environment given a temporal logic specification. We employ a multi-layered synergistic framework that can deal with general robot dynamics and combine it with an iterative planning strategy. Our work allows us to deal with the unknown environmental restrictions only when they are discovered and without the need to repeat the computation that is related to the temporal logic specification. In addition, we define a metric for satisfaction of a specification. We use this metric to plan a trajectory that satisfies the specification as closely as possible in cases in which the discovered constraint in the environment renders the specification unsatisfiable. We demonstrate the efficacy of our framework on a simulation of a hybrid second-order car-like robot moving in an office environment with unknown obstacles. The results show that our framework is successful in generating a trajectory whose satisfaction measure of the specification is optimal. They also show that, when new obstacles are discovered, the reinitialization of our framework is computationally inexpensive.
- K. E. Bekris, D. K. Grady, M. Moll, and L. E. Kavraki. Safe distributed motion coordination for second-order systems with different planning cycles. Intl. J. of Robotics Research, 31(2):129--149, Feb. 2012. Google ScholarDigital Library
- K. E. Bekris and L. E. Kavraki. Greedy but safe replanning under kinodynamic constraints. In IEEE Intl. Conf. on Robotics and Automation, pages 704--710, 2007.Google ScholarCross Ref
- A. Bhatia, L. Kavraki, and M. Vardi. Motion planning with hybrid dynamics and temporal goals. In Decision and Control, IEEE Conf. on, pages 1108--1115, 2010.Google Scholar
- A. Bhatia, L. Kavraki, and M. Vardi. Sampling-based motion planning with temporal goals. In Robotics and Automation, IEEE Int. Conf. on, pages 2689--2696, May 2010.Google Scholar
- A. Bhatia, M. Maly, L. Kavraki, and M. Vardi. Motion planning with complex goals. Robotics Automation Magazine, IEEE, 18(3):55--64, Sep. 2011.Google ScholarCross Ref
- R. Bloem, K. Greimel, T. Henzinger, and B. Jobstmann. Synthesizing robust systems. In Formal Methods in Computer-Aided Design, pages 85--92, 2009.Google Scholar
- Y. Chen, J. Tumova, and C. Belta. LTL robot motion control based on automata learning of environmental dynamics. In Robotics and Automation, IEEE Int. Conf. on, pages 5177 --5182, May 2012.Google Scholar
- I. A. Şucan, M. Moll, and L. E. Kavraki. The open motion planning library. IEEE Robotics & Automation Magazine, 19:72--82, December 2012.Google ScholarCross Ref
- X. Ding, S. Smith, C. Belta, and D. Rus. MDP optimal control under temporal logic constraints. In Decision and Control and European Control Conf. (CDC-ECC), IEEE Conf. on, pages 532--538, 2011.Google ScholarCross Ref
- G. Fainekos, A. Girard, H. Kress-Gazit, and G. J. Pappas. Temporal logic motion planning for dynamic robots. Automatica, 45:343--352, 2009. Google ScholarDigital Library
- T. Fraichard. A short paper about motion safety. In Proc. 2007 IEEE Intl. Conf. on Robotics and Automation, pages 1140--1145, Apr. 2007.Google ScholarCross Ref
- D. K. Grady, M. Moll, C. Hegde, A. C. Sankaranarayanan, R. G. Baraniuk, and L. E. Kavraki. Multi-objective sensor-based replanning for a car-like robot. In IEEE Intl. Symp. on Safety, Security, and Rescue Robotics, 2012.Google ScholarCross Ref
- D. Hsu, J. Latombe, and R. Motwani. Path planning in expansive configuration spaces. Intl. J. of Computational Geometry and Applications, 9(4--5):495--512, 1999.Google ScholarCross Ref
- Karaman and Frazzoli. Sampling-based motion planning with deterministic μ-calculus specifications. In IEEE Conf. on Decision and Control, 2009.Google Scholar
- K. Kim and G. Fainekos. Approximate solutions for the minimal revision problem of specification automata. In Proc. of the IEEE Int. Conf. on Intelligent Robots and Systems, pages 265--271, 2012.Google ScholarCross Ref
- M. Kloetzer and C. Belta. A fully automated framework for control of linear systems from temporal logic specifications. Automatic Control, IEEE Transactions on, 53(1):287--297, 2008.Google Scholar
- H. Kress-Gazit, G. Fainekos, and G. Pappas. Where's waldo? Sensor-based temporal logic motion planning. In Robotics and Automation, 2007 IEEE Int. Conf. on, pages 3116--3121, Apr. 2007.Google ScholarCross Ref
- H. Kress-Gazit, G. Fainekos, and G. Pappas. Temporal-logic-based reactive mission and motion planning. Robotics, IEEE Transactions on, 25(6):1370--1381, Dec. 2009. Google ScholarDigital Library
- H. Kress-Gazit, T. Wongpiromsarn, and U. Topcu. Correct, reactive, high-level robot control. Robotics Automation Magazine, IEEE, 18(3):65--74, Sep. 2011.Google ScholarCross Ref
- O. Kupferman and Y. Lustig. Lattice automata. In Proc. 8th Int. Conf. on Verification, Model Checking, and Abstract Interpretation, volume 4349 of Lecture Notes in Computer Science, pages 199--213, 2007. Google ScholarDigital Library
- O. Kupferman and M. Y. Vardi. Model checking of safety properties. Formal Methods in System Design, 19:291 -- 314, 2001. Google ScholarDigital Library
- M. Lahijanian, S. B. Andersson, and C. Belta. Temporal logic motion planning and control with probabilistic satisfaction guarantees. IEEE Transactions on Robotics, 28(2):396--409, Apr. 2012.Google ScholarDigital Library
- M. Lahijanian, J. Wasniewski, S. Andersson, and C. Belta. Motion planning and control from temporal logic specifications with probabilistic satisfaction guarantees. In IEEE Int. Conf. on Robotics and Automation, pages 3227--3232, Alaska, 2010.Google ScholarCross Ref
- T. Latvala. Efficient model checking of safety properties. In Model Checking Software, pages 74--88. Springer, 2003. Google ScholarDigital Library
- S. C. Livingston, R. M. Murray, and J. W. Burdick. Backtracking temporal logic synthesis for uncertain environments. In IEEE Intl. Conf. on Robotics and Automation, pages 5163--5170, 2012.Google ScholarCross Ref
- E. Plaku, L. Kavraki, and M. Vardi. Motion planning with dynamics by a synergistic combination of layers of planning. IEEE Trans. on Robotics, 26(3):469--482, Jun. 2010. Google ScholarDigital Library
- E. Plaku, L. E. Kavraki, and M. Y. Vardi. Falsification of LTL safety properties in hybrid systems. In Proc. of the Conf. on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2009), York, UK, 2009. Google ScholarDigital Library
- V. Raman and H. Kress-Gazit. Analyzing unsynthesizable specifications for high-level robot behavior using LTLMoP. In Proc. of the 23rd Int. Conf. on Computer Cided Verification, CAV'11, pages 663--668, Berlin, Heidelberg, 2011. Springer-Verlag. Google ScholarDigital Library
- S. Sarid, B. Xu, and H. Kress-Gazit. Guaranteeing high-level behaviors while exploring partially known maps. In Proc. of Robotics: Science and Systems, Sydney, Australia, July 2012.Google ScholarCross Ref
- J. Shewchuk. Triangle: Engineering a 2D quality mesh generator and Delaunay triangulator. In Applied Computational Geometry Towards Geometric Engineering, volume 1148 of Lecture Notes in Computer Science, chapter 23, pages 203 -- 222. Springer-Verlag, Berlin/Heidelberg, 1996. Google ScholarDigital Library
- P.vCerný, S. Gopi, T. A. Henzinger, A. Radhakrishna, and N. Totla. Synthesis from incompatible specifications. In Proc. of the tenth ACM Int. Conf. on Embedded software, EMSOFT '12, pages 53--62, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- T. Wongpiromsarn, U. Topcu, and R. Murray. Receding horizon control for temporal logic specifications. In Proc. of the 13th ACM Int. Conf. on Hybrid Systems: Computation and Control, pages 101--110. ACM, 2010. Google ScholarDigital Library
Index Terms
- Iterative temporal motion planning for hybrid systems in partially unknown environments
Recommendations
Real-time motion planning for robot manipulators in unknown environments using infrared sensors
This paper deals with sensor-based motion planning method for a robot arm manipulator operating among unknown obstacles of arbitrary shape. It can be applied to online collision avoidance with no prior knowledge of the obstacles. Infrared sensors are ...
Temporal-Logic-Based Reactive Mission and Motion Planning
This paper provides a framework to automatically generate a hybrid controller that guarantees that the robot can achieve its task when a robot model, a class of admissible environments, and a high-level task or behavior for the robot are provided. The ...
VCS-based motion planning for distributed mobile robots: collision avoidance and formation
This paper addresses decentralized motion planning among a homogeneous set of feedback-controlled mobile robots. It introduces the velocity obstacle, which describes the collision between robot and obstacle, and the hybrid interactive velocity obstacles ...
Comments