Journal of Intelligent & Robotic Systems

, Volume 93, Issue 3–4, pp 723–743 | Cite as

Employing Natural Terrain Semantics in Motion Planning for a Multi-Legged Robot

  • Dominik BelterEmail author
  • Jan Wietrzykowski
  • Piotr Skrzypczyński
Open Access


This paper considers motion planning for a six-legged walking robot in rough terrain, considering both the geometry of the terrain and its semantic labeling. The semantic labels allow the robot to distinguish between different types of surfaces it can walk on, and identify areas that cannot be negotiated due to their physical nature. The proposed environment map provides to the planner information about the shape of the terrain, and the terrain class labels. Such labels as “wall” and “plant” denote areas that have to be avoided, whereas other labels, “grass”, “sand”, “concrete”, etc. represent negotiable areas of different properties. We test popular classification algorithms: Support Vector Machine and Random Trees in the task of producing proper terrain labeling from RGB-D data acquired by the robot. The motion planner uses the A algorithm to guide the RRT-Connect method, which yields detailed motion plans for the multi-d.o.f. legged robot. As the A planner takes into account the terrain semantic labels, the robot avoids areas which are potentially risky and chooses paths crossing mostly the preferred terrain types. We report experimental results that show the ability of the new approach to avoid areas that are considered risky for legged locomotion.


Walking robot Mapping Terrain classification Motion planning 



This research is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 780883. We thank Szymon Bartoszyk and Patryk Kasprzak who worked on the environment model for the indoor experiments and provided the initial version of the terrain classifier.

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Institute of Control, Robotics and Information EngineeringPoznan University of TechnologyPoznanPoland

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