Trajectory Optimization for High-Power Robots with Motor Temperature Constraints
Modeling heat transfer is an important problem in high-power electrical robots as the increase of motor temperature leads to both lower energy efficiency and the risk of motor damage. Power consumption itself is a strong restriction in these robots especially for battery-powered robots such as those used in disaster-response. In this paper, we propose to reduce power consumption and temperature for robots with high-power DC actuators without cooling systems only through motion planning. We first propose a parametric thermal model for brushless DC motors which accounts for the relationship between internal and external temperature and motor thermal resistances. Then, we introduce temperature variables and a thermal model constraint on a trajectory optimization problem which allows for power consumption minimization or the enforcing of temperature bounds during motion planning. We show that the approach leads to qualitatively different motion compared to typical cost function choices, as well as energy consumption gains of up to 40%.
KeywordsTrajectory optimization Motion planning Legged robots Temperature Thermal models
This work was supported by ImPACT TRC Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan). Martim Brandão is supported by the EPSRC RAIN Hub, grant EP/R026084/1. Finally, we would like to thank N. Sakai for help with the open loop experiments and K. Kumagai for help with initially setting up the temperature experiments.
- 1.Hashimoto, K., et al.: WAREC-1 - a four-limbed robot having high locomotion ability with versatility in locomotion styles. In: IEEE International Symposium on Safety, Security and Rescue Robotics, pp. 172–178, October 2017Google Scholar
- 2.Urata, J., Nakanishi, Y., Okada, K., Inaba, M.: Design of high torque and high speed leg module for high power humanoid. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4497–4502, October 2010Google Scholar
- 3.Trujillo, S., Cutkosky, M.: Thermally constrained motor operation for a climbing robot. In: 2009 IEEE International Conference on Robotics and Automation, pp. 2362–2367, May 2009Google Scholar
- 5.Noda, S., Murooka, M., Nozawa, S., Kakiuchi, Y., Okada, K., Inaba, M.: Online maintaining behavior of high-load and unstable postures based on whole-body load balancing strategy with thermal prediction. In: 2014 IEEE International Conference on Automation Science and Engineering, pp. 1166–1171, August 2014Google Scholar
- 6.Kumagai, I., Noda, S., Nozawa, S., Kakiuchi, Y., Okada, K., Inaba, M.: Whole body joint load reduction control for high-load tasks of humanoid robot through adapting joint torque limitation based on online joint temperature estimation. In: IEEE-RAS International Conference on Humanoid Robots, pp. 463–468, November 2014Google Scholar
- 7.Brandao, M., Shiguematsu, Y.M., Hashimoto, K., Takanishi, A.: Material recognition CNNs and hierarchical planning for biped robot locomotion on slippery terrain. In: 16th IEEE-RAS International Conference on Humanoid Robots, pp. 81–88, November 2016Google Scholar
- 9.Bergman, T.L., Incropera, F.P.: Fundamentals of Heat and Mass Transfer. Wiley, Hoboken (2011)Google Scholar
- 10.Fussell, B.K.: Thermal effects on the torque-speed performance of a brushless DC motor. In: Proceedings of Electrical/Electronics Insulation Conference, pp. 403–411, October 1993Google Scholar
- 11.Sekalala, S.: Performance of a three-phase permanent magnet motor operating as a synchronous motor and a brushless dc motor. Master’s thesis, Louisiana State University (2006)Google Scholar
- 12.Brandao, M., Hashimoto, K., Santos-Victor, J., Takanishi, A.: Optimizing energy consumption and preventing slips at the footstep planning level. In: 15th IEEE-RAS International Conference on Humanoid Robots, pp. 1–7, November 2015Google Scholar
- 14.Mei, Y., Lu, Y.H., Hu, Y.C., Lee, C.G.: A case study of mobile robot’s energy consumption and conservation techniques. In: Proceedings of the 12th International Conference on Advanced Robotics, ICAR 2005, pp. 492–497. IEEE (2005)Google Scholar