Dung beetles can perform impressive multiple motor behaviors using their legs. The behaviors include walking and rolling a large dung ball on different terrains, e.g., level ground and different slopes. To achieve such complex behaviors for legged robots, we propose here a modular neural controller for dung beetle-like locomotion and object transportation behaviors of a dung beetle-like robot. The modular controller consists of several modules based on three generic neural modules. The main modules include 1) a neural oscillator network module (as a central pattern generator (CPG)), 2) a neural CPG postprocessing module (PCPG), 3) a velocity regulating network module (VRN). The CPG generates basic rhythmic patterns. The patterns are first shaped by the PCPG and their amplitudes as well as phases are later modified by the VRN to obtain proper motor patterns for locomotion and object transportation. Combining all these neural modules, we can achieve different motor patterns for four different actions which are forward walking, backward walking, levelground ball rolling, and sloped-ground ball rolling. All these actions can be activated by four input neurons. The experimental results show that the simulated dung beetle-like robot can robustly perform the actions. The average forward speed is 0.058 cm/s and the robot is able to roll a large ball (about 3 times of its body height and 2 times of its weight) up different slope angles up to 25 degrees.
Many CPG-based locomotion models have a problem known as the tracking error problem, where the mismatch between the CPG driving signal and the state of the robot can cause undesirable behaviours for legged robots. Towards alleviating this problem, we introduce a mechanism that modulates the CPG signal using the robot's interoceptive information. The key concept is to generate a driving signal that is easier for the robot to follow, yet can drive the locomotion of the robot. This can be done by nudging the CPG signal in the direction of lower tracking error, which can be analytically calculated. Unlike other reactive CPG, the proposed method does not rely on any parametric learning ability to adjust the shape of the signal, making it a unique option for a biological adaptive motor control. Our experiment results show that the proposed method successfully reduces the tracking error. We also show that the CPG signal, regulated by the proposed method, is robust to perturbation and can smoothly return back to the default pattern.
Dung beetles can perform a number of versatile behaviours, including walking and dung ball rolling. While different walking and running gaits of dung beetles have been described in previous literature, little is known about their ball rolling gaits. From behavioural experiments and video recordings of the beetle Scarabaeus (Kheper) lamarcki, we analysed and identified four underlying rules for leg coordination during ball rolling. The rules describe the alternation of the front legs and protraction waves of the middle and hind legs. We found that while rolling a ball backwards, the front legs are decoupled or loosely coupled from the other legs, resulting in a non-standard gait, in contrast to previously described tripod and gallop walking gaits in dung beetles. This provides insight into the principles of leg coordination in dung beetle ball rolling behaviour and its underlying rules. The proposed rules can be used as a basis for further investigation into ball rolling behaviours on more complex terrain (e.g., uneven terrain and slopes). Additionally, the rules can also be used to guide the development of control mechanisms for bio-inspired ball rolling robots.
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