Abstract Maintaining stability while walking on arbitrary surfaces or dealing with external perturbations is of great interest in humanoid robotics research. Increasing the system’s autonomous robustness to a variety of postural threats during locomotion is the key despite the need to evaluate noisy sensor signals. The equations of motion are the foundation of all published approaches. In contrast, we propose a more adequate evaluation of the equations of motion with respect to an arbitrary moving reference point in a non-inertial reference frame. Conceptual advantages are, e.g., getting independent of global position and velocity vectors estimated by sensor fusions or calculating the imaginary zero-moment point walking on different inclined ground surfaces. Further, we improve the calculation results by reducing noise-amplifying methods in our algorithm and using specific characteristics of physical robots. We use simulation results to compare our algorithm with established approaches and test it with experimental robot data. 

/pdf/robust-inverse-dynamics-by-evaluating-newton-euler-equations-3rn4pc2a.pdf

Robust inverse dynamics by evaluating Newton–Euler equations with respect to a moving reference and measuring angular acceleration

This comprehensive review delves into the realm of bipedal wheel-legged robots, focusing on their design, control, and applications in assistive technology and disaster mitigation. Drawing insights from various fields such as robotics, computer science, and biomechanics, it offers a holistic understanding of these robots' stability, adaptability, and efficiency. The analysis encompasses optimization techniques, sensor integration, machine learning, and adaptive control methods, evaluating their impact on robot capabilities. Emphasizing the need for adaptable, terrain-aware control algorithms, the review explores the untapped potential of machine learning and soft robotics in enhancing performance across diverse operational scenarios. It highlights the advantages of hybrid models combining legged and wheeled mobility while stressing the importance of refining control frameworks, trajectory planning, and human-robot interactions. The concept of integrating soft and compliant mechanisms for improved adaptability and resilience is introduced. Identifying gaps in current research, the review suggests future directions for investigation in the fields of robotics and control engineering, addressing the evolution and terrain adaptability of bipedal wheel-legged robots, control, stability, and locomotion, as well as integrated sensory and perception systems, microcontrollers, cutting-edge technology, and future design and control directions.

Evolution, Design, and Future Trajectories on Bipedal Wheel-legged Robot: A Comprehensive Review

Abstract Maintaining stability while walking on arbitrary surfaces or dealing with external perturbations is of great interest in humanoid robotics research. Increasing the system's autonomous robustness to variety of postural threats during locomotion is the key despite the need to evaluate noisy sensor signals. The equations of motion are the foundation of all published approaches. In contrast, we propose a more adequate evaluation of the equations of motion with respect to an arbitrary moving reference point in a non-inertial reference frame. We use simulation results to compare our algorithm with established approaches and test it with physical robot data. Conceptual advantages are, e.g., getting independent of global positions and velocity vectors estimated by sensor fusions or calculating the imaginary zero-moment point walking on different inclined ground surfaces. Further, we improve the calculation results by using specific characteristics of physical robots and reducing noise amplifying methods in our algorithm. 

Computational advantages in robotics by evaluating Newton-Euler equations with respect to a moving reference point in a non-inertial frame

This study describes a walking motion control algorithm capable of effective ground adaptation without using a specific balance control algorithm when a biped robot walks on irregular ground. Through preview control, a center-of-mass trajectory capable of stable walking was derived, and motion control was performed using center of mass-inverse kinematics (CoM-IK) and computed torque control (CTC) in the task space. By switching the dynamic model according to the support/swing state of the leg, the motion control gain of the support leg and the swing leg can be maintained to a minimum. Therefore, when the foot lands on an irregular ground, joint rigidity is always minimized so that it can be easily adapted. Finally, the performance of the proposed algorithm was verified by comparing and analyzing three motion control methods on level and irregular grounds in the Gazebo dynamic simulation environment. 

Effective Ground-Adaptive Walking Strategy for Biped Robots Using Center-of-Mass-Inverse Kinematics and Model Switching

The preview control walking method, which is commonly used in bipedal walking, requires jerk and ZMP errors as cost functions to generate body trajectory. Since the two inputs are dependent, optimization to form body trajectory is performed simultaneously with weight factors. Therefore, it is often seen that the resulting body trajectory rapidly changes on velocity according to the weight factors. This eventually requires a torque actuator in order to perform such action. In order to overcome this problem, we apply a method used on a quadruped to a bipedal robot. Since, it only targets to minimize the acceleration of the body trajectory, the body does not require rapid speed change. Also, this method can eliminate the computation time needed for preview control referred to preview time. When applying a quadruped robots walking method that has a relatively large support polygon than that of a bipedal robot, stability deterioration may occur. Therefore, we approached to secure ZMP constraints with relatively small support polygon area as within bipedal robots. In this paper we propose a body trajectory generation method that guarantees real-time stability while minimizing acceleration.

Body Trajectory Generation Using Quadratic Programming in Bipedal Robots

In this paper, we propose an effective localization method with only a stereo camera that has obstacles using particle filter. When localization with flow planning rather than robot scanned map, the error of localization increases when there is an obstacle. To solve this problem, First, we propose two types of obstacle recognition method: "Image Split Obstacle" and "Obstacle In Image" through image processing using the Opencv contour function. Afterwards, we solve the problems caused by the particle filter weight calculation process through a new sensing model using interval angle. In addition, we propose two probability models that can solve the problem of inconsistency between the number of landmarks of robots and particles. After that, we suggest an effective robot localization method by presenting a probability model that considers obstacles. As a result, the probability model considering obstacles showed an error rate reduction of about 45% compared to the existing model that does not considering obstacles.

Improving Localization Performance of Robot Using Obstacle Recognition and Probability Model through Image Processing

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Body Trajectory Generation Using Quadratic Programming in Bipedal Robots

Improving Localization Performance of Robot Using Obstacle Recognition and Probability Model through Image Processing