Abstract
The study of combined task and path planning has mainly focused on feasibility planning for high-dimensional, complex manipulation problems. Yet the integration of symbolic reasoning capabilities with geometric knowledge can address optimal planning in lower dimensional problems. This paper presents a dynamic, anytime task and path planning approach that enables mobile robots to autonomously adapt to changes in the environment. The planner consists of a path planning layer that adopts a multi-tree extension of the optimal Transition-based Rapidly-Exploring Random Tree algorithm to simultaneously find optimal paths for all movement actions. The corresponding path costs, derived from a cost space function, are incorporated into the symbolic representation of the problem to guide the task planning layer. Anytime planning provides continuous path quality improvements, which subsequently updates the high-level plan. Geometric knowledge of the environment is preserved to efficiently re-plan both at the task and path planning level. The planner is evaluated against existing methods for static planning problems, showing that it is able to find higher quality plans without compromising planning time. Simulated deployment of the planner in a partially-known environment demonstrates the effectiveness of the dynamic, anytime components.
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This paper is an invited extension to the work presented in [10].
References
Wong, C., Yang, E., Yan, X.-T., Gu, D.: Autonomous robots for harsh environments: a holistic overview of current solutions and ongoing challenges. Syst. Sci. Control Eng. 6(1), 213–219 (2018)
Gasparetto, A., Boscariol, P., Lanzutti, A., Vidoni, R.: Path planning and trajectory planning algorithms: a general overview. In: Carbone, G., Gomez-Bravo, F. (eds.) Motion and Operation Planning of Robotic Systems. MMS, vol. 29, pp. 3–27. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14705-5_1
Nguyen, T.T., Kayacan, E., De Baedemaeker, J., Saeys, W.: Task and motion planning for apple harvesting robot. IFAC Proc. 46(18), 247–252 (2013)
Friedrich, C., Csiszar, A., Lechler, A., Verl, A.: Efficient task and path planning for maintenance automation using a robot system. IEEE Trans. Autom. Sci. Eng. 15(3), 1205–1215 (2018)
Garrett, C.R., Lozano-Pérez, T., Kaelbling, L.P.: FFRob: an efficient heuristic for task and motion planning. In: Akin, H.L., Amato, N.M., Isler, V., van der Stappen, A.F. (eds.) Algorithmic Foundations of Robotics XI. STAR, vol. 107, pp. 179–195. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16595-0_11
Dantam, N.T., Chaudhuri, S., Kavraki, L.E.: The task-motion kit: an open source, general-purpose task and motion- planning framework. IEEE Robot. Autom. Mag. 25(3), 61–70 (2018)
Muñoz, P., R-Moreno, M.D., Barrero, D.F.: Unified framework for path-planning and task-planning for autonomous robots. Robot. Auton. Syst. 82, 1–14 (2016)
Woosley, B., Dasgupta, P.: Integrated real-time task and motion planning for multiple robots under path and communication uncertainties. Robotica 36(3), 353–373 (2018)
Wong, C., Yang, E., Yan, X.-T., Gu, D.: Optimal path planning based on a multi-tree T-RRT* approach for robotic task planning in continuous cost spaces. In: 12th France-Japan and 10th Europe-Asia Congress on Mechatronics, pp. 242–247 (2018)
Wong, C., Yang, E., Yan, X.-T., Gu, D.: Dynamic anytime task and path planning for mobile robots. In: UK-RAS19 Conference on Embedded Intelligence: Enabling & Supporting RAS Technologies, pp. 36–39 (2019)
McDermott, D.: The PDDL planning domain definition language. In: AIPS-98 Planning Competition Community (1998)
Gerevini, A., Saetti, A., Serina, I., Toninelli, P.: LPG-TD: a fully automated planner for PDDL2.2 domains. In: 14th International Conference on Automated Planning and Scheduling International Planning Competition (2004)
Hoomann, J.O.: The Metric-FF planning system: translating ‘ignoring delete lists’ to numeric state variables. J. Artif. Intell. Res. 20, 291–341 (2003)
Ferguson, D., Stentz, A.: Anytime RRTs. In: Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5369–5375 (2006)
Otte, M., Frazzoli, E.: RRT\(^X\): asymptotically optimal single- query sampling-based motion planning with quick replanning. Int. J. Rob. Res. 35(7), 797–822 (2016)
Daniel, K., Nash, A., Koenig, S., Felner, S.: Theta*: any-angle path planning on grids. J. Artif. Intell. Res. 39, 533–579 (2010)
Karaman, S., Walter, M.R., Perez, A., Frazzoli, E., Teller, S.: Anytime motion planning using the RRT*. In: IEEE International Conference on Robotics and Automation, pp. 1478–1483 (2011)
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Funded by the Engineering and Physical Sciences Research Council (EPSRC) under its Doctoral Training Partnership Programme (DTP 2016–2020 University of Strathclyde, Glasgow, UK).
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Wong, C., Yang, E., Yan, XT., Gu, D. (2019). An Optimal Approach to Anytime Task and Path Planning for Autonomous Mobile Robots in Dynamic Environments. In: Althoefer, K., Konstantinova, J., Zhang, K. (eds) Towards Autonomous Robotic Systems. TAROS 2019. Lecture Notes in Computer Science(), vol 11650. Springer, Cham. https://doi.org/10.1007/978-3-030-25332-5_14
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