Kinematic and Potential Energy Analysis of Self-Adaptive Robotic Legs

2018 ◽  
Author(s):  
Dmitri Fedorov ◽  
Lionel Birglen
Keyword(s):  
Potential Energy ◽  
Energy Analysis ◽  
Terminal Link ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
No Sensor ◽  
Improve Stability ◽  
Do So ◽  
Support Phase ◽  
Self Adaptive

This paper presents how the kinematic and potential energy analysis of self-adaptive robotic legs can help to improve their performances with respect to their ability to overcome obstacles and the required actuation torque to do so. Self-adaptive leg mechanisms, inspired by the underactuated linkages used in grasping, generally rely on a single degree of freedom (DOF) to generate a trajectory at its endpoint that is appropriate for walking applications. When colliding with an unexpected obstacle, a second DOF in the leg automatically engages and creates a motion allowing the leg to overcome said obstacle. Since this behavior is obtained mechanically, with no sensor or control, these robotic legs are referred to as self-adaptive. In this paper, the conditions for the passive adaptation to obstacles are first briefly recalled. Then, the range of obstacles for which this adaptation is possible is determined through the analysis, using potential energy, of the mechanism workspace and it is shown how the results are connected to its kinematics. In particular, the influence of the shape of the terminal link of the leg is discussed with two compared examples. Finally, practical designs and especially the relative advantages of various locking mechanisms, required to improve stability during the support phase of the leg trajectory, are discussed.

A Theory for the Identification of a Single Degree-of-Freedom Machine From its Periodic Operating Characteristics

1983 ◽  
Vol 105 (3) ◽  
pp. 445-451 ◽  
Author(s):  
J. L. Wiederrich
Keyword(s):  
Kinetic Energy ◽  
Potential Energy ◽  
Dynamic Analysis ◽  
Dynamic Properties ◽  
Degree Of Freedom ◽  
Operating Characteristics ◽  
Energy Potential ◽  
Single Degree Of Freedom ◽  
Single Degree

The dynamic properties of a machine are defined by its kinetic energy, potential energy, and dissipation functions. These functions form the basis for the dynamic analysis of a machine. This paper presents a theory whereby these functions may be determined from the observed forced periodic operating response of a single degree of freedom machine. This method may have applications in machinery development and diagnosis.

scholarly journals Reduction of Dietrich-Ruina attractors to unimodal maps

1997 ◽  
Vol 4 (2) ◽  
pp. 63-69 ◽  
Author(s):  
S. Shkoller ◽  
J.-B. Minster
Keyword(s):  
Dynamical System ◽  
Geometric Analysis ◽  
Chaotic Attractors ◽  
Chaotic Motions ◽  
Complex Behaviour ◽  
Unimodal Maps ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Rate Dependent ◽  
Do So

Abstract. We present a geometric analysis of a quasi-static single degree of freedom elastic slider with a state and rate dependent friction law. In particular, we examine and characterize the regime of chaotic motions displayed by the Dieterich-Ruina model. We do so by numerically reducing the chaotic attractors to a family of unimodal maps and discuss why this suggests complex behaviour in the dynamical system.

Active Stiffness Control of Cable Vibration

1993 ◽  
Vol 60 (4) ◽  
pp. 948-953 ◽  
Author(s):  
Yozo Fujino ◽  
Pennung Warnitchai ◽  
B. M. Pacheco
Keyword(s):  
Energy Analysis ◽  
Optimal Algorithm ◽  
Degree Of Freedom ◽  
Scale Model ◽  
Mass Ratio ◽  
Combined System ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Stiffness Control ◽  
Cable Vibrations

To suppress cable vibrations, active stiffness control, which changes the tension as a positive use of parametric excitation, is studied. An optimal algorithm is obtained from energy analysis and verified by experiment on a scale model. Numerical investigation is then made on the control of a combined system of cable and single-degree- of-freedom structure; the degree-of-freedom of the latter is along the cable axis. It is found by numerical simulation that instability occurs when the mass ratio of cable to structure is large, or when the frequency ratio of structure to cable is close to 2.0.

scholarly journals Passive Gravity Balancing with a Self-Regulating Mechanism for Variable Payload

Machines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 145
Author(s):  
Diego Franchetti ◽  
Giovanni Boschetti ◽  
Basilio Lenzo
Keyword(s):  
Potential Energy ◽  
The Other ◽  
Robotic Systems ◽  
Single Degree Of Freedom ◽  
System Parameters ◽  
Single Degree ◽  
Energy Consumptions ◽  
Simulation Results ◽  
Practical Implications ◽  
Auxiliary Mechanism

Gravity balancing techniques allow for the reduction of energy consumptions in robotic systems. With the appropriate arrangements, often including springs, the overall potential energy of a manipulator can be made configuration-independent, achieving an indifferent equilibrium for any position. On the other hand, such arrangements lose their effectiveness when some of the system parameters change, including the mass. This paper proposes a method to accommodate different payloads for a mechanism with a single degree-of-freedom (DOF). By means of an auxiliary mechanism including a slider, pulleys and a counterweight, the attachment point of a spring is automatically regulated so as to maintain the system in indifferent equilibrium regardless of the position, even when the overall mass of the system varies. Practical implications for the design of the mechanism are also discussed. Simulation results confirm the effectiveness of the proposed approach.

A Single-Degree-of-Freedom Self-Regulated Gravity Balancer for Adjustable Payload1

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Yu-Lin Chu ◽  
Chin-Hsing Kuo
Keyword(s):  
Potential Energy ◽  
Degree Of Freedom ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Manual Adjustment ◽  
Sense And Respond

This paper presents a single-degree-of-freedom (single-DoF) gravity balancer that can deal with variable payload without requesting manual or other auxiliary adjustment. The proposed design is an integration of two mechanism modules, i.e., a standard spring-based statically balanced mechanism and a spring adjusting mechanism. A tensile spring is attached to the statically balanced mechanism for balancing the payload, and its installation points are controlled by two cables, which are driven by the spring adjusting mechanism. When different payloads are applied, the spring adjusting mechanism will act to regulate the spring installation points to suitable places such that the overall potential energy of the mechanism and the (variable) payload remains constant within the workspace of the balancer. This therefore suggests the main novelty of the proposed design where the balancer mechanism can automatically sense and respond the change of the payload without manual adjustment to the balancing mechanism. A prototype is built up and successfully tested for the proposed concept.

scholarly journals A cross-species neural integration of gravity for motor optimization

2021 ◽  
Vol 7 (15) ◽  
pp. eabf7800
Author(s):  
Jeremie Gaveau ◽  
Sidney Grospretre ◽  
Bastien Berret ◽  
Dora E. Angelaki ◽  
Charalambos Papaxanthis
Keyword(s):  
Optimization Strategy ◽  
Motor Patterns ◽  
Activation Patterns ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Neural Integration ◽  
Gravity Effects ◽  
Neural Signature ◽  
Experimental Findings ◽  
Muscular Activation

Recent kinematic results, combined with model simulations, have provided support for the hypothesis that the human brain shapes motor patterns that use gravity effects to minimize muscle effort. Because many different muscular activation patterns can give rise to the same trajectory, here, we specifically investigate gravity-related movement properties by analyzing muscular activation patterns during single-degree-of-freedom arm movements in various directions. Using a well-known decomposition method of tonic and phasic electromyographic activities, we demonstrate that phasic electromyograms (EMGs) present systematic negative phases. This negativity reveals the optimal motor plan’s neural signature, where the motor system harvests the mechanical effects of gravity to accelerate downward and decelerate upward movements, thereby saving muscle effort. We compare experimental findings in humans to monkeys, generalizing the Effort-optimization strategy across species.

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