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Effort Informed Roadmaps (EIRM*): Efficient Asymptotically Optimal Multiquery Planning by Actively Reusing Validation Effort

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Part of the Springer Proceedings in Advanced Robotics book series (SPAR,volume 27)


Multiquery planning algorithms find paths between various different starts and goals in a single search space. They are designed to do so efficiently by reusing information across planning queries. This information may be computed before or during the search and often includes knowledge of valid paths.

Using known valid paths to solve an individual planning query takes less computational effort than finding a completely new solution. This allows multiquery algorithms, such as PRM*, to outperform single-query algorithms, such as RRT*, on many problems but their relative performance depends on how much information is reused. Despite this, few multiquery planners explicitly seek to maximize path reuse and, as a result, many do not consistently outperform single-query alternatives.

This paper presents Effort Informed Roadmaps (EIRM*), an almost-surely asymptotically optimal multiquery planning algorithm that explicitly prioritizes reusing computational effort. EIRM* uses an asymmetric bidirectional search to identify existing paths that may help solve an individual planning query and then uses this information to order its search and reduce computational effort. This allows it to find initial solutions up to an order-of-magnitude faster than state-of-the-art planning algorithms on the tested abstract and robotic multiquery planning problems.


  • Sampling-based path planning
  • Optimal path planning
  • Multiquery path planning

M. P. Strub—Work performed while at the University of Oxford.

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  • DOI: 10.1007/978-3-031-25555-7_37
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    All experiments were run using OMPL 1.5, on a laptop with an Intel i7-4720HQ CPU @ 2.60GHz processor with 16GB RAM.


  1. Berenson, D., Abbeel, P., Goldberg, K.: A robot path planning framework that learns from experience. In: ICRA, pp. 3671–3678 (2012).

  2. Bohlin, R., Kavraki, L.E.: Path planning using lazy PRM. In: ICRA, pp. 521–528 (2000).

  3. Bruce, J., Veloso, M.M.: Real-time randomized path planning for robot navigation. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds.) RoboCup 2002. LNCS (LNAI), vol. 2752, pp. 288–295. Springer, Heidelberg (2003).

    CrossRef  Google Scholar 

  4. Chen, B., Dai, B., Lin, Q., Ye, G., Liu, H., Song, L.: Learning to plan in high dimensions via neural exploration-exploitation trees. In: ICLR (2020).

  5. Coleman, D., Şucan, I.A., Moll, M., Okada, K., Correll, N.: Experience-based planning with sparse roadmap spanners. In: ICRA, pp. 900–905 (2015).

  6. Dobson, A., Bekris, K.E.: Sparse roadmap spanners for asymptotically near-optimal motion planning. IJRR 33, 18–47 (2014).

  7. Dobson, A., Krontiris, A., Bekris, K.E.: Sparse roadmap spanners. In: Frazzoli, E., Lozano-Perez, T., Roy, N., Rus, D. (eds.) Algorithmic Foundations of Robotics X. STAR, vol. 86, pp. 279–296. Springer, Heidelberg (2013).

    CrossRef  Google Scholar 

  8. Elbanhawi, M., Simic, M.: Sampling-based robot motion planning: a review. IEEE Access (2014).

  9. Faust, A., et al.: PRM-RL: long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning. In: ICRA, pp. 5113–5120 (2018).

  10. Funk, N., Chalvatzaki, G., Belousov, B., Peters, J.: Learn2Assemble with Structured Representations and Search for Robotic Architectural Construction. In: CoRL (2021).

  11. Gammell, J.D., Strub, M.P.: Asymptotically optimal sampling-based motion planning methods. Ann. Rev. Control Robot. Autonom. Syst. 4, 295–318 (2021).

  12. Gammell, J.D., Barfoot, T.D., Srinivasa, S.S.: Informed sampling for asymptotically optimal path planning. T-RO 34, 966–984 (2018).

  13. Gammell, J.D., Barfoot, T.D., Srinivasa, S.S.: Batch Informed Trees (BIT*): informed asymptotically optimal anytime search. IJRR 39, 543–567 (2020).

    CrossRef  Google Scholar 

  14. Hartmann, V.N., Oguz, O.S., Driess, D., Toussaint, M., Menges, A.: Robust task and motion planning for long-horizon architectural construction planning. In: IROS, pp. 6886–6893 (2020).

  15. Hartmann, V.N., Orthey, A., Driess, D., Oguz, O.S., Toussaint, M.: Long-horizon multi-robot rearrangement planning for construction assembly. To Appear in T-RO (2021)

    Google Scholar 

  16. Hauser, K.: Lazy collision checking in asymptotically-optimal motion planning. In: ICRA, pp. 2951–2957 (2015)

  17. Ichter, B., Harrison, J., Pavone, M.: Learning sampling distributions for robot motion planning. In: ICRA, pp. 7087–7094 (2018).

  18. Ichter, B., Schmerling, E., Lee, T.W.E., Faust, A.: Learned critical probabilistic roadmaps for robotic motion planning. In: ICRA, pp. 9535–9541 (2020).

  19. Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. IJRR 30, 846–894 (2011).

    CrossRef  MATH  Google Scholar 

  20. Kavraki, L.E., Svestka, P., Latombe, J.C., Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. T-RO 12, 566–580 (1996).

  21. Kiesel, S., Gu, T., Ruml, W.: An effort bias for sampling-based motion planning. In: IROS, pp. 2864–2871 (2017).

  22. Lagriffoul, F., Dantam, N.T., Garrett, C., Akbari, A., Srivastava, S., Kavraki, L.E.: Platform-independent benchmarks for task and motion planning. RA-L 3, 3765–3772 (2018).

  23. Lavalle, S.M.: Rapidly-exploring random trees: a new tool for path planning. Technical Report (1998)

    Google Scholar 

  24. Li, T.Y., Shie, Y.C .: An incremental learning approach to motion planning with roadmap management. In: ICRA, pp. 3411–3416 (2002).

  25. Pan, J., Chitta, S., Manocha, D.: FCL: a general purpose library for collision and proximity queries. In: ICRA, pp. 3859–3866 (2012).

  26. Penrose, M.: Random Geometric Graphs. OUP Oxford, Oxford (2003)

    CrossRef  MATH  Google Scholar 

  27. Phillips, M., Cohen, B.J., Chitta, S., Likhachev, M.: E-graphs: bootstrapping planning with experience graphs. In: R:SS, p. 110 (2012).

  28. Phillips, M., Dornbush, A., Chitta, S., Likhachev, M.: Anytime incremental planning with e-graphs. In: ICRA, pp. 2444–2451 (2013).

  29. Solovey, K., Kleinbort, M.: The critical radius in sampling-based motion planning. IJRR 39, 266–285 (2020).

    CrossRef  Google Scholar 

  30. Stewart, B.S., White, C.C.: Multiobjective A*. J. ACM 38, 775–814 (1991).

    CrossRef  MATH  Google Scholar 

  31. Strub, M.P.: Leveraging multiple sources of information to search continuous spaces. PhD thesis, University of Oxford (2021).

  32. Strub, M.P., Gammell, J.D.: AIT* and EIT*: asymmetric bidirectional sampling-based path planning. To appear in IJRR (2022).

  33. Şucan, I.A., Moll, M., Kavraki, L.E.: The open motion planning library. RAM, 72–82 (2012).

  34. Sánchez, G., Latombe, J.C.: A single-query bi-directional probabilistic roadmap planner with lazy collision checking. In: ISRR (2001).

  35. Thayer, J., Benton, J., Helmert, M.: Better parameter-free anytime search by minimizing time between solutions. In: SOcS (2012)

    Google Scholar 

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This research has been supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2120/1 - 390831618 and UK Research and Innovation and EPSRC through ACE-OPS: From Autonomy to Cognitive assistance in Emergency OPerationS [EP/S030832/1].

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Correspondence to Valentin N. Hartmann .

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Hartmann, V.N., Strub, M.P., Toussaint, M., Gammell, J.D. (2023). Effort Informed Roadmaps (EIRM*): Efficient Asymptotically Optimal Multiquery Planning by Actively Reusing Validation Effort. In: Billard, A., Asfour, T., Khatib, O. (eds) Robotics Research. ISRR 2022. Springer Proceedings in Advanced Robotics, vol 27. Springer, Cham.

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