Space Gaits

Legged locomotion is an efficient solution for mobility on unknown and possibly rugged planetary terrains. Research on walking robots has seen substantial progress in the last few decades (cf. [1-6]). Studies already focus on the application of walking robots in space, e.g., [7]. The authors of [8] even draw inspiration from the jumping movements of the astronauts that landed on the moon in order to make an initial design of a jumping robot taking advantage of the moon's low-gravity environment.

While the studies mentioned above (and many more that are omitted here) are pioneering, a general approach to the design and selection of appropriate walking gaits for legged robots is lacking. A possible starting point could be to automate the gait design using evolutionary algorithms [9-11] or other methods of reinforcement learning [12] (see Figure 1). Such automatic, rather unbiased, designs of gaits would allow the solutions to be mostly determined by the problem structure (including the environment, body and controller). The optimised gaits could then be analysed by means of the classification methods popular in biological research, leading to generic insights into the relation between the environmental conditions and the emerged gaits. In this study, we are mostly interested in the relation between walking gaits and environmental conditions such as gravity level and soil type.

Milton Hildebrand pioneered the scientific analysis and classification of gaits [13]. Following his work the movement of each limb is partitioned into a stance phase, and a swing phase. In the first, the foot is in contact with the ground, while in the latter the foot is lifted and moved. Each leg completes a cycle in the same length of time in order for a steady pattern to occur. Thus, any gait can be described in terms of the beginning and end of stance phase of each limb with respect to each other (see Figure 2).

Early work on six-legged robots used the Hildebrand methodology to analyse the walking gaits [14,15]. For example, in [14], this scheme has been used to analyse the evolved walking gait of a hexapod. More complex and detailed ways of analysing gaits than the Hidebrand diagrams are used today, both in biology and robotics. For example, newly studied features include the contact forces (cf. [16]). The use of such more complex analysis methods is encouraged in the context of this project, as long as the information on the gait present in the Hildebrand diagrams is preserved. Namely, that information captures gait properties that are essential to the roject, such as whether the robot is walking or hopping.

Study objective

The goal of the Ariadna project is to systematically use Hildebrand diagrams or some equivalent methodology to analyse walking gaits emerging from an optimisation process in order to verify whether behavioral switches / bifurcations exist with respect to parameters such as gravity and soil type.

Study Participants

The study proposal with number 12-5201 has been released in the Ariadna Call 2012/01. Following the thorough evaluation of the participation candidatures, the Advanced Concepts Team selected the Control Systems Laboratory of the National Technical University of Athens. Below the list of the researchers participating in the study.

Advanced Concepts Team, ESA Guido de Croon, ACT Dario Izzo, ACT

Control Systems Laboratory of the National Technical University of Athens *Evangelos G. Papadopoulos

• Ioannis Kontolatis *Iosif S. Paraskevas

The final results can be found in the Final report of the study.

References

1. How to Keep From Falling Forward: Elementary Swing Leg Action for Passive Dynamic Walkers, by M. Wisse, A.L. Schwab, R.Q. van der Linde, and F.C.T. van der Helm, IEEE Transactions on Robotics 21 (3), pp. 393-401, (2005)
2. Footstep Planning for the Honda ASIMO Humanoid, Chestnutt, J., Lau, M., Cheung, G., Kuffner, J., Hodgins, J., and Kanade, T., in ICRA 2005, pp. 629 - 634 (2005)
3. A Bipedal Walking Robot with Efficient and Human-Like Gait. Collins, S.H., Ruina, A., ICRA 2005, pp. 1983 - 1988 (2005)
4. Online trajectory generation for omnidirectional biped walking, by Behnke, S., in ICRA 2006. pp. 1597 - 1603, (2006).
5. Human-like walking with knee stretched, heel-contact and toe-off motion by a humanoid robot, by Ogura, Y., Shimomura, K., Kondo, A., Morishima, A., Okubo, T., Momoki, S., Hun-ok Lim, and Takanishi, A., in 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3976 - 3981 (2006).
6. A brief review of bipedal robotics research, by Mattias Wahde , Jimmy Pettersson, in Proceedings of the 8th Mechatronics Forum International Conference, (2002).
7. ATHLETE: a cargo handling and manipulation robot for the moon, by B.H. Wilcox, T. Litwin, J. Biesiadecki, J. Matthews, M. Heverly, and J. Morrison. Journal of Field Robotics 24 (5), pp. 421-434, (2007).
8. Simulation study of a bipedal robot jumping motion approach on moon gravity, by A.M.M. Omer, H. Lim, and A. Takanishi. IEEE ROBIO 2010, Tianjin, China. (2010)
9. Evolving legged robots using biologically inspired optimization strategies, B. Smith, C.M. Saaj, and E. Allouis. IEEE ROBIO 2010, Tianjin, China. (2010)
10. Evolving robot gaits in hardware: the HyperNEAT generative encoding vs. parameter optimization, J. Yosinski et al. In Proceedings of the European Conference on Artificial Life (ECAL 2011).
11. Hornby, Gregory S., Lipson, Hod, and Pollack, Jordan B. Generative Representations for the Automated Design of Modular Physical Robots. IEEE Transactions on Robotics and Automation, vol. 19, no. 3, pp. 703-719 (2003).
12. The Design of LEO: a 2D Bipedal Walking Robot for Online Autonomous Reinforcement Learning, by E. Schuitema, M. Wisse, T. Ramakers, and P. Jonker, in IEEE/RSJ International Conference on Intelligent Robots and Systems (2010)
13. The Quadrupedal Gaits of Vertebrates, by Milton Hildebrand, BioScience, Vol. 39, No. 11, Animals in Motion (Dec., 1989), pp. 766-775
14. A Distributed Neural Network Architecture for Hexapod Robot Locomotion, by R.D. Beer, H.J. Chiel, R.D. Quinn, K.S. Espenschied, and P. Larsson, in Neural Computation 1992 4:3, pp. 356-365
15. Robust Agent Control of an Autonomous Robot with Many Sensors and Actuators, by C. Ferrell, 1993, Technical report AITR-1443, MIT, USA.
16. A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data, by R. Begg, J. Kamruzzaman, in Journal of Biomechanics 38, 2005, pp. 401–408

Outcome