This paper presents the design of a new lightweight, full-size bipedal robot developed in the Humanoid Robotics Laboratory at Nazarbayev University. The robot, equipped with 12 degrees of freedom (DOFs), stands at 1.1 m tall and weighs only 15 kg (excluding the battery). Through the implementation of a simple mechanical design and the utilization of off-the-shelf components, the overall prototype cost remained under USD 5000. The incorporation of high-performance in-house-developed servomotors enables the robot’s actuation system to generate up to 2400 W of mechanical power, resulting in a power-to-weight ratio of 160 W/kg. The details of the mechanical and electrical design are presented alongside the formalization of the forward kinematic model using the successive screw displacement method and the solution of the inverse kinematics. Tests conducted in both a simulation environment and on the real prototype demonstrate that the robot is capable of accurately following the reference joint trajectories to execute a quasi-static gait, achieving an average power consumption of 496 W.

One of the most challenging aspects of designing a humanoid robot is ensuring stable walking. To achieve this, the kinematic architecture must support 3D motion and maintain equilibrium, particularly during single-foot support. Without proper configuration, the robot may experience unbalanced weight distribution, significantly increasing the risk of falling while walking. While adding redundant degrees of freedom (DOFs) can enhance adaptability, it also raises the system’s complexity and cost and the need for more sophisticated control strategies and higher energy consumption. This paper explores a reduced-DOF bipedal robot, which, despite its limited number of DOFs, is capable of performing 3D motion. It features an inverted pendulum and elastic ankles made of thermoplastic polyurethane (TPU), enabling effective balance control and attenuation of disturbances. The robot’s electromechanical design is introduced alongside the kinematic model. Momentum equilibrium in a pseudo-static mode is considered in both the frontal and sagittal planes, taking into account the pendulum and the swinging leg during the single support phase. The TPU ankle’s performance is assessed based on its ability to resist external bending forces, highlighting challenges related to the robot’s weight equilibrium stability and ankle inversion. Experimental results from both Finite Element Analysis (FEA) and real-world tests are compared. Lastly, the joint movements of the inverted pendulum-based biped robot are evaluated in both a virtual environment and a physical prototype while performing lateral tilting and various gait sequences.

This paper proposes a neural network-based model of a novel hybrid Series-Parallel Elastic Actuator (SEA-PEA) architecture for high-performance robotic applications. The system combines a Brushless DC (BLDC) motor, a planetary gearbox, and a rotary pneumatic actuator serving as an elastic element. While the gearbox offers advantages such as low cost and back-drivability, it suffers from significant backlash due to manufacturing tolerances. To address this, the actuator connects the gearbox output shaft directly to that of the pneumatic actuator. For a parallel configuration, both stators are mounted on an aluminum holder supporting the entire assembly. Within the backlash region, the system behaves as a Series Elastic Actuator (SEA), with the pneumatic actuator’s elasticity influencing stiffness, damping, and inertia. Outside this region, the system transitions to a Parallel Elastic Actuator (PEA), rigidly coupling the motor and gearbox shafts. A hybrid modeling approach is introduced to capture the actuator’s dynamic response. The SEA behavior is modeled using a black-box Artificial Neural Network (ANN), specifically an Echo State Network (ESN), which showed the lowest mean squared error (MSE) among the tested architectures. The PEA behavior is modeled using a gray-box approach, combining a linear dynamic block with a nonlinear static block represented by an ANN (Wiener model). This hybrid framework, integrating an ESN, achieved lower prediction error and better performance than other ANN architectures, as confirmed by experimental results.

This paper presents an innovative hybrid cable-driven shoulder joint for humanoid robotics application. A blend of a flexible central limb and three rigid lateral limbs, form a 2-degree-of-freedom (2 DOF) mechanism that connects the mobile platform to the fixed base. This design leverages both good mechanical stability and the integration of an elastic element, which mitigate vibrations up to 40% and allow the storage and release of elastic potential energy up to 1.9 J. Initially, three alternative and incremental shoulder joint designs are presented and evaluated across two distinct experiments: 1) Energy storage analysis of the parallel mechanism during single cable motion and 2) Dynamic response and vibration damping. Following these experiments, a detailed analysis is performed on the stiffness properties of the proposed prototype, as it outperforms the other two designs. The dimension of the central elastic limb, made out of Thermo-Plastic Polyurethane (TPU), is optimized using Finite Element Analysis (FEA). The kinematic behavior of the proposed mechanism is approximated as a combination of linkages equipped with two universal joints, and its motion evaluated through numerical simulations and real experiments.