cs.ROOct 7, 2026

ClimbLab: MATLAB Simulation Platform for Legged Climbing Robotics

Authors: Kentaro Uno, Warley F. R. Ribeiro, Yusuke Koizumi, Keigo Haji, Koki Kurihara, William Jones, Kazuya Yoshida

Organizations: Space Robotics Lab (SRL), Department of Aerospace Engineering, Tohoku University, Aoba 6-6-01, Aramaki-Aza, Aoba-ku, Sendai, Miyagi 980-8579, Japan · VisionSpace Technologies GmbH, Robert-Bosch Straße 7, 64293 Darmstadt, Germany

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Abstract

This paper presents an open-sourced MATLAB simulation and analysis platform dedicated to legged climbing robots. This simulator enables the design of any limbed robotic system as an articulated multi-body with a floating base and simulates it walking and climbing in an arbitrary environment. The main variable environmental parameters are inclination, gravity, and ground stiffness, and any point cloud can be installed as the terrain map. Furthermore, the simulator employs a rigid body dynamics engine. This paper first describes the simulator structure, and the computational flow and next presents the representative simulation examples where quadrupedal robots assumed gripping on the wall or climbing on the steep slope.

Figures & tables

Figure 1: ClimbLab is a versatile simulation platform for articulated legged robots. The user can first design the robot, set the environmental conditions, and plan the gait strategy. Dynamics calculations are performed by SpaceDyn [9], and measure a variety of variables while the simulation is running. All variables are saved as a single table so that the user can smoothly perform the detailed analysis.
Figure 2: ClimbLab’s each computational phase is composed of a group of subroutine functions, which modularization of the code facilitates the extra development in this platform.
Figure 3: Snapshots of the simulation results where HubRobo (mass: 2:4 kg) holds onto the overhanging wall in a nominal stance, where inclination is (a) 100° and (b) 110°. This simulation proves the performance of the assumed gripper enables the robot to stand on the 100° inclined wall without losing stability.
Figure 4: Snapshots of the simulation results where the robot is climbing on the 45 deg inclined slope in (a) Normal gait and (b) Diagonal gait. GIA vector is drawn as a red arrow and the support polygon and the stable region for GIA [12] are illustrated by the blue-green polygon and polyhedron, respectively.
Figure 5: Time history of multiple metrics to analyze the gait. Two types of stability criterion: TSM [11] and GIA margin [12] in addition to the maximum and RMS of all joints while the simulation, are shown.
MetricNormal gaitDiagonal gait
Mean TSM [m]0.3610.359
Min. TSM [m]0.0000.104
Mean GIA margin [m/s 2 ]11.811.9
Min. GIA margin [m/s 2 ]1.105.39
Mean max. torque of the joints [N m]28.726.7
Mean RMS torque of the joints [N m]13.311.5
Table 1: Statistical metric values during the simulation case (2)
Figure 6: Snapshots of the final simulation results where the robot is moving forward to the goal point (shown in dark-blue dot at (x; y; z) = (1; 0; 0) [m]) with sensing and selecting the landable points. The sensing camera’s field of view is illustrated in light-gray pyramid and the detected footholds are shown in blue-green.

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