Nonlinear dynamics and control of an inertially actuated jumper robot

Razzaghi, Pouria; Khatib, Ehab Al; Hürmüzlü, Yildirim

doi:10.1007/s11071-019-04963-1

Cited by 24 publications

(17 citation statements)

References 27 publications

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“…Second-order Lagrangian equation is applied to solve the dynamic differential equations of the spring-mass system. 32 In order to describe a complete hopping cycle mathematically, the involved physical parameters of the robot are defined in Table 4:…”

Section: Hopping Locomotion Dynamic Analysismentioning

confidence: 99%

Models of a hybrid wheeled hopping self-balanced robot

Zeng

Bai

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Combining wheeled structure with hopping mechanism, this paper purposes a self-balanced hopping robot with hybrid motion pattern. The main actuator which is the cylindrical cam, optimized by particle swarm optimization (PSO), is equipped with the motor to control the hopping motion. Robotic system dynamics model is established and solved by Lagrangian method. After linearization, control characteristics of the system is obtained by classical control theory based on dynamics equations. By applying Adams and Matlab to simulate the system, hopping locomotion and self-balanced capability are validated respectively, and result shows that jump height can reach 750 mm theoretically. Then PID control scheme is developed and specific models of hardware and software are settled down accordingly. Finally, prototype is implemented and series of hopping experiments are conducted, showing that with different projectile angle, prototype can jump 550 mm in height and 460 mm in length, transcending majority of other existing hopping robots.

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Section: Hopping Locomotion Dynamic Analysismentioning

confidence: 99%

Models of a hybrid wheeled hopping self-balanced robot

Zeng

Bai

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…Inertially actuated jumping robots can vary their jumping frequency to change jumping height and/or progression speed. A jumping robot can track complicated paths by changing directions while it maintains ground contact [1]. When in the air, jumping robots can travel at high speeds.…”

Section: Introductionmentioning

confidence: 99%

Feedback Linearization of Inertially Actuated Jumping Robots

2021

Self Cite

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Inertially Actuated Jumping Robots (IAJR) provide a promising new means of locomotion. The difficulty of IAJR is found in the hybrid nature of the ground contact/flying dynamics. Recent research studies in our Systems Lab have provided a family tree of inertially actuated locomotion systems. The proposed Tapping Robot is the most prompt member of this tree. In this paper, a feedback linearization controller is introduced to provide controllability given the 3-dimensional motion complexity. The research objective is to create a general controller that can regulate the locomotion of Inertially Actuated Jumping Robots. The expected results can specify a desired speed and/or jump height, and the controller ensures the desired values are achieved. The controller can achieve the greatest response for the Basketball Robot at a maximum jump height of 0.25 m, which is greater than the former performance with approximately 0.18 m. The design paradigm used on the Basketball Robot was extended to the Tapping Robot. The Tapping Robot achieved a stable average forward velocity of 0.0773 m/s in simulation and 0.157 m/s in experimental results, which is faster than the forward velocity of former robot, Pony III, with 0.045 m/s.

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“…The Lyapunov theory was used to prove stability of the proposed method, and a four link SCARA robot was used to demonstrate efficacy of the proposed method via simulation. Razzaghi et al [8] introduced a unique hopping robot based on the inertial actuation concept, which could navigate in three-dimensional environments. They also applied sliding mode control based on the Lyapunov approach, and a state-dependent Riccati equation-based optimal controller was also designed.…”

Section: Introductionmentioning

confidence: 99%

Identification and Machine Learning Prediction of Nonlinear Behavior in a Robotic Arm System

Wang

Zhu

2021

Symmetry

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In this study, the subject of investigation was the dynamic double pendulum crank mechanism used in a robotic arm. The arm is driven by a DC motor though the crank system and connected to a fixed side with a mount that includes a single spring and damping. Robotic arms are now widely used in industry, and the requirements for accuracy are stringent. There are many factors that can cause the induction of nonlinear or asymmetric behavior and even excite chaotic motion. In this study, bifurcation diagrams were used to analyze the dynamic response, including stable symmetric orbits and periodic and chaotic motions of the system under different damping and stiffness parameters. Behavior under different parameters was analyzed and verified by phase portraits, the maximum Lyapunov exponent, and Poincaré mapping. Firstly, to distinguish instability in the system, phase portraits and Poincaré maps were used for the identification of individual images, and the maximum Lyapunov exponents were used for prediction. GoogLeNet and ResNet-50 were used for image identification, and the results were compared using a convolutional neural network (CNN). This widens the convolutional layer and expands pooling to reduce network training time and thickening of the image; this deepens the network and strengthens performance. Secondly, the maximum Lyapunov exponent was used as the key index for the indication of chaos. Gaussian process regression (GPR) and the back propagation neural network (BPNN) were used with different amounts of data to quickly predict the maximum Lyapunov exponent under different parameters. The main finding of this study was that chaotic behavior occurs in the robotic arm system and can be more efficiently identified by ResNet-50 than by GoogLeNet; this was especially true for Poincaré map diagnosis. The results of GPR and BPNN model training on the three types of data show that GPR had a smaller error value, and the GPR-21 × 21 model was similar to the BPNN-51 × 51 model in terms of error and determination coefficient, showing that GPR prediction was better than that of BPNN. The results of this study allow the formation of a highly accurate prediction and identification model system for nonlinear and chaotic motion in robotic arms.

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Nonlinear dynamics and control of an inertially actuated jumper robot

Cited by 24 publications

References 27 publications

Models of a hybrid wheeled hopping self-balanced robot

Models of a hybrid wheeled hopping self-balanced robot

Feedback Linearization of Inertially Actuated Jumping Robots

Identification and Machine Learning Prediction of Nonlinear Behavior in a Robotic Arm System

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