Online optimization of modular robot locomotion

Abstract

-Adaptive locomotion in unstructured and unpredictable environments is one of the most advertised features of modular robots in the literature. Autonomous modular robots are expected to adapt in the face of a dynamic environment, unexpected tasks and/or module failures. There are two levels of adaptation: Within a static configuration, a chain-type modular robot can adapt its locomotion gait using its many degrees of freedom and the inherent redundancy. In addition, the robot may self-reconfigure to adapt also its morphology. Online optimization of locomotion in a self-organizing manner is mandatory within this context.The contribution of this paper is three-fold: i) Inspired by Central Pattern Generators (CPGs) found in vertebrates, we propose a distributed locomotion controller based on coupled nonlinear oscillators; ii) For offline optimization, a genetic algorithm that co-evolves the CPG with the configuration of the modular robot is presented; iii) The ultimate goal of our research being autonomous locomotion, the focus of the paper lies on a novel, fast online adaptation method for coupled nonlinear oscillators. The algorithm allows fast online optimization (adaptation) of locomotion gaits in the face of module failures or new, previously unknown configurations. A realistic simulation of our hardware prototype YaMoR is used for the experiments.

Engineering entrainment and adaptation in limit cycle systems

“…In (Marbach & Ijspeert, 2005) Powell's method is used to optimize the parameters of an oscillator network. Many other optimization methods could be used for similar tasks.…”

“…It would of course be interesting to exploit such substrates for engineering applications. algorithmic dynamic algorithmic dynamic (Ermentrout & Kopell, 1994) R 1a (Williamson, 1998) R 1a R 1a (Matsuoka, 1985(Matsuoka, , 1987 R 1a (Schöner, Jiang, & Kelso, 1990) R 1a (Schöner & Kelso, 1988) R 1a (Endo et al, 2005) R 1a (Morimoto et al, 2006) R 1a (Righetti & Ijspeert, 2006a) R 1b (Taga, 1994(Taga, , 1995b(Taga, , 1995a R 1a (Buchli & Ijspeert, 2004a) R 1a (22, 2003 R 1a (Schöner & Santos, 2001;Santos, 2003Santos, , 2004 R 1a (Collins & Richmond, 1994) R 1a (Ijspeert, 2001) R 1a/2 (Zegers & Sundareshan, 2003) R 2b (Okada, Tatani, & Nakamura, 2002;Okada, Nakamura, & Nakamura, 2003) R 2a (Ijspeert, Nakanishi, & Schaal, 2002) R 2b (Ruiz, Owens, & Townley, 1998) R 2 (Galicki, Leistritz, & Witte, 1999;Leistritz, Galicki, Witte, & Kochs, 2002) R 2 (Righetti & Ijspeert, 2006b) R 1a/2b (Marbach & Ijspeert, 2005) A 1a (Nishii, 1999) A 1c/1a (Ermentrout, 1991) A 1c (Large, 1994(Large, , 1996 A 1a 1c (Buchli & Ijspeert, 2004b;Righetti, Buchli, & Ijspeert, 2006;Buchli, Righetti, & Ijspeert, 2005;Buchli et al, 2006) A 1c Table 3 Table classifying …”

HyperNEAT for Locomotion Control in Modular Robots

“…We based the modules on the YaMoR [6] oscillators. These consist of a solid body and an oscillating arm, offering one degree of freedom.…”

“…Consequently, we seek controllers that put sensor information -specifically, obstacle detection-to use: they should implement reactive control in addition to habitual motion patterns such as found in [4,6].…”

YaMoR and Bluemove — An Autonomous Modular Robot with Bluetooth Interface for Exploring Adaptive Locomotion

“…We have extensively used models of CPGs for the control of locomotion in other projects [9,10]. In particular, simulation experiments have demonstrated they are ideally suited for implementing distributed control of locomotion in simulated YaMoR units [11].…”