Design,analysis, and neural control of a bionic parallel mechanism

Frontiers of Mechanical Engineering - Although the torso plays an important role in the movement coordination and versatile locomotion of mammals, the structural design and neuromechanical control...     

A neuroplasticity-inspired neural circuit for acoustic navigation with obstacle avoidance that learns smooth motion paths

Neural Computing and Applications - Acoustic spatial navigation for mobile robots is relevant in the absence of reliable visual information about the target that must be localised. Reactive robot...     

Bio-inspired design and movement generation of dung beetle-like legs

African ball-rolling dung beetles can use their front legs for multiple purposes that include walking, manipulating or forming a dung ball, and also transporting it. Their multifunctional legs can be used as inspiration for the design of a multifunctional robot leg. Thus, in this paper, we present the development of real robot legs based on the study of the front legs of the beetle. The leg movements of the beetle, during walking as well as manipulating and transporting a dung ball, were observed and reproduced on the robot leg. Each robot leg consists of three main segments which were built using 3D printing. The segments were combined with four active joints in total (i.e., 4 degrees of freedom) to mimic the leg movements of the beetle for locomotion as well as object manipulation and transportation. Kinematics analysis of the leg was also performed to identify its workspace. The results show that the robot leg is able to perform all the movements with trajectories comparable to the beetle leg. To this end, the study contributes not only to the design of novel multifunctional robot legs but also to the methodology for bio-inspired leg design.     

A robot leg with compliant tarsus and its neural control for efficient and adaptive locomotion on complex terrains

Insects, like dung beetles, show fascinating locomotor abilities. They can use their legs to walk on complex terrains (e.g., rocky and curved surfaces) and to manipulate objects. They also exploit their compliant tarsi, increasing the contact area between the legs and surface, to enhance locomotion, and object manipulation efficiency. Besides these biomechanical components, their neural control allows them to move at a proper frequency with respect to their biomechanical properties and to quickly adapt their movements to deal with environmental changes. Realizing these complex achievements on artificial systems remains a grand challenge. As a step towards this direction, we present here our first prototype of an artificial dung beetle-like leg with compliant tarsus by analyzing real dung beetle legs through \(\mu\)CT scans. Compliant tarsus was designed according to the so-called fin ray effect. Real robot experiments show that the leg with compliant tarsus can efficiently move on rocky and curved surfaces. We also apply neural control, based on a central pattern generator (CPG) circuit and synaptic plasticity, to autonomously generate a proper moving frequency of the leg. The controller can also adapt the leg movement to deal with environmental changes, like different treadmill speeds, within a few steps.     

A dung beetle-inspired robotic model and its distributed sensor-driven control for walking and ball rolling

A typical approach when designing a bio-inspired robot is to simplify an animal model and to enhance the functionality of interest. For hexapod robots, this often leads to a need of supplementary mechanics to become multifunctional. However, a preferable solution is to employ the embodied multifunctional capabilities of the animal as inspiration for robot design. Using this approach, we present a method for translating the kinematic chain of a dung beetle from which an accurate kinematic model and a simplified one were simulated and compared. The beetle was selected due to its multifunctional locomotory capabilities including walking as well as standing on and rolling a ball. For testing the models, we developed a distributed sensor-driven controller that can generate walking and ball-rolling behaviors. A comparison of the two modeling approaches shows a similar performance with regards to walking stability and accuracy, but differences when it comes to speed and multifunctionality. This is because the accurate model is able to use its legs to walk faster and roll a ball, which the simplified one is not. In conclusion, the accurate model of a dung beetle-inspired robot is advantageous as it, together with our novel control mechanism, is able to elicit behaviors comparable to those of the real dung beetle (i.e., walking and rolling a dung ball).     

Using efference copy and a forward internal model for adaptive biped walking

To behave properly in an unknown environment, animals or robots must distinguish external from self-generated stimuli on their sensors. The biologically inspired concepts of efference copy and internal model have been successfully applied to a number of robot control problems. Here we present an application of this for our dynamic walking robot RunBot. We use efference copies of the motor commands with a simple forward internal model to predict the expected self-generated acceleration during walking. The difference to the actually measured acceleration is then used to stabilize the walking on terrains with changing slopes through its upper body component controller. As a consequence, the controller drives the upper body component (UBC) to lean forwards/backwards as soon as an error occurs resulting in dynamical stable walking. We have evaluated the performance of the system on four different track configurations. Furthermore we believe that the experimental studies pursued here will sharpen our understanding of how the efference copies influence dynamic locomotion control to the benefit of modern neural control strategies in robots.