Gait Transition from Pacing by a Quadrupedal Simulated Model and Robot with Phase Modulation by Vestibular Feedback

We propose a method to achieve autonomous gait transition according to speed for a quadruped robot pacing at medium speeds. We verified its effectiveness through experiments with the simulation model and the robot we developed. In our proposed method, a central pattern generator (CPG) is applied to each leg. Each leg is controlled by a PD controller based on output from the CPG. The four CPGs are coupled, and a hard-wired CPG network generates a pace pattern by default. In addition, we feed the body tilt back to the CPGs in order to adapt to the body oscillation that changes according to the speed. As a result, our model and robot achieve stable changes in speed while autonomously generating a walk at low speeds and a rotary gallop at high speeds, despite the fact that the walk and rotary gallop are not preprogramed. The body tilt angle feedback is the only factor involved in the autonomous generation of gaits, so it can be easily used for various quadruped robots. Therefore, it is expected that the proposed method will be an effective control method for quadruped robots.

Learning Neuroplastic Matching of Robot Dynamics in Closed-loop CPGs

Legged robots have the potential to show locomotion performance with reduced control effort and energy efficiency by leveraging elastic structures inspired by animals' elastic tendons and muscles. However, it remains a challenge to match the natural dynamics of complex legged robots and their control task dynamics. Here we present a framework to match control task dynamics and natural dynamics based on the neuroelasticity and neuroplasticity concept. Inspired by animals we design quadruped robot Morti with strong natural dynamics as a testing platform. It is controlled through a bioinspired closed-loop central pattern generator (CPG) that is designed to neuroelastically mitigate short term perturbations using sparse contact feedback. We use the amount of neuroelastic activity as a proxy to quantify the dynamics' mismatching. By minimizing neuroelastic activity, we neuroplastically match the control task dynamics to the robot's natural dynamics. Through matching the robot learns to walk within one hour with only sparse feedback and improves its energy efficiency without explicitly minimizing it in the cost function.

The BCM rule allows a spinal cord model to learn rhythmic movements

Animal locomotion is hypothesized to be controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However, current CPG models do not explain how a spinal cord circuitry, which has the same basic genetic plan across species, can adapt to control the different biomechanical properties and locomotion patterns existing in these species. Here we demonstrate that rhythmic and alternating movements in pendulum models can be learned by a monolayer spinal cord circuitry model using the BCM learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to CPG models, because rhythm generating neurons and genetically defined connectivity are not required in our model.

Maximizing ATPG Diagnosis Resolution on Unique Single Failing Devices

Unique single failing device is common for customer returns and reliability failures. When the initial and iterative Automatic Test Pattern Generator (ATPG) could not provide a sufficient diagnostic resolution, it can become quite challenging for the analyst to determine the failure mechanism in an efficient and effective way. Fault isolation could be performed in combination with the diagnosis results but there are cases with mismatch between the results (location, fault type, suspect nets). When the diagnostic resolution is low, the probability for such mismatches are high. This paper proposes an approach to increase the diagnostic resolution by utilizing a high-resolution targeted pattern (HRT) and single shot logic (SSL) patterns. Two cases will be discussed in the paper to highlight this approach and show in detail how it was utilized on actual failing units.

Bio-inspired central pattern generator for development of Cartesian motor skills for a Quadruped Robot

Central Pattern Generator (CPG) plays a significant role in the generation of diverse and stable gaits patterns for animals as well as controlling their locomotion. The main contributions of this paper are the ability to develop the Cartesian motor skills and coordinating legs of the quadruped robot for gait adaption and its nominal characteristics with CPG approach. Primary, a predefined relationship between an excitation signal and essential parameters of the CPG design is programmed. Next, the coordinated oscillator's rhythmic patterns by CPG and accordingly output gait diagrams for each foot of the robot are attained. Then, these desirable features such as predictive modulation and programming the gait event sequences including leg-lifting sequences and step length, duration of the time of each footstep within a gait, coordination of swing and stance phases of all legs are calculated in terms of different spatio_temporal vectors. Furthermore, a novel Cartesian footstep basis function is designed based on the robot characteristics and consequently, the associated spatio-temporal vectors can be inserted to it, which caused to spanning the space of possible gait timing in Cartesian space. Next, Cartesian footstep planner can be computed the swing foot trajectories in workspace along movement axes and then according to these footholds and feet placement, ZMP (Zero Moment Point) reference trajectory will be calculated and obtained. Therefore, COG (Center of Gravity) trajectory can be computed by designing a preview controller on the basis of the desired ZMP trajectory. Finally, to demonstrate the effectiveness of the proposed algorithm, it is implemented on a quadruped robot on both simulation or experimental implementations and the results are compared and discussed with other references.

A New, Fast Pseudo-Random Pattern Generator for Advanced Logic Built-In Self-Test Structures

Digital cores that are currently incorporated into advanced Systems on Chip (SoC) frequently include Logic Built-In Self-Test (LBIST) modules with the Self-Test Using MISR/Parallel Shift Register Sequence Generator (STUMPS) architecture. Such a solution always comprises a Pseudo-Random Pattern Generator (PRPG), usually designed as a Linear Feedback Shift Register (LFSR) with a phase shifter attached to the register and arranged as a network of XOR gates. This study discloses an original and innovative structure of such a PRPG unit referred to as the DT-LFSR-TPG module that needs no phase shifter. The module is designed as a set of identical linear registers of the DT-LFSR type with the same primitive polynomial. Each register has a form of a ring made up exclusively of D and T flip-flops. This study is focused on the investigation of those parameters of DT-LFSR registers that are essential to use these registers as components of PRPG modules. The investigated parameters include phase shifts and the correlation between sequences of bits appearing at outputs of T flip-flops, implementation cost, and the maximum frequency of the register operation. It is demonstrated that PRPG modules of the DT‑LFSR‑TPG type enable much higher phase shifts and substantially higher operation frequencies as compared to competitive solutions. Such modules can also drive significantly more scan paths than other PRPGs described in reference studies and based on phase shifters. However, the cost of the foregoing advantages of DT-LFSR-TPG modules is the larger hardware overhead associated with the implementation of the solution proposed.