Neural Body Bending Control with Temporal Delays for Millipede-Like Turning Behaviour of a Multi-segmented, Legged Robot

Leg-body coordination strategies for obstacle avoidance and narrow space navigation of multi-segmented, legged robots

“…motors (Oldham et al, 2009;Hoffman and Wood, 2011;Avirovik and Priya, 2013;Avirovik et al, 2014), and magnetic fields (Venkiteswaran et al, 2019(Venkiteswaran et al, , 2020Wang et al, 2022). On the other hand, other studies have used neural mechanisms to implement reactive and adaptive locomotion control of millipede-inspired robots (Kano et al, 2017;Miguel-Blanco and Manoonpong, 2020;Ambe et al, 2022;Mingchinda et al, 2022). While these studies evaluated the robot locomotion behaviors in various environments, the leg and body coordination of such robots for obstacle avoidance and narrow space navigation in complex environments remains unclear.…”

“…2002; Aoi et al, 2016;Mingchinda et al, 2022;Shao et al, 2022). However, previous studies have not investigated the underlying control mechanism and strategies that efficiently enable the millipede robots to perform a coordinated and smooth turning behavior, especially in complex environments such as narrow spaces.…”

“…Both dimensions require a control system that properly uses the sensory input information from the environment to produce the desired adaptive behavior of the robot in response to the obstacle. A previous attempt has been made to control the turning behavior of multi-segmented robots at the segment level by passively following the movement of the first segment (Aoi et al, 2016) and by actively controlling the body segments with temporal delays (Mingchinda et al, 2022). Nevertheless, both studies were limited in determining how robots can effectively coordinate the leg and body adaptations to deal with obstacles within the environment.…”

“…The hysteresis effect can act as a short-term memory of a neural control system, where the maintenance of output signals can induce temporal delays between each neural activity. In robotics, the hysteresis effect has been utilized in neural-based control in legged robots to control locomotion and sensory response to environmental stimuli (Hülse et al, 2007;Manoonpong et al, 2008Manoonpong et al, , 2010Manoonpong et al, , 2012Grinke et al, 2015;Mingchinda et al, 2022). In our previous study, the dynamics of single recurrent neurons (hysteresis effect) was exploited to generate turning behaviors with different turning angles depending on the weight of the recurrent neurons (Mingchinda et al, 2022).…”

“…In robotics, the hysteresis effect has been utilized in neural-based control in legged robots to control locomotion and sensory response to environmental stimuli (Hülse et al, 2007;Manoonpong et al, 2008Manoonpong et al, , 2010Manoonpong et al, , 2012Grinke et al, 2015;Mingchinda et al, 2022). In our previous study, the dynamics of single recurrent neurons (hysteresis effect) was exploited to generate turning behaviors with different turning angles depending on the weight of the recurrent neurons (Mingchinda et al, 2022). Further, single recurrent neurons can be connected in series with excitatory connections between each recurrent neuron to produce the gradual turning behavior seen in millipedes.…”