Bio-inspired swing leg control for spring-mass robots running on ground with unexpected height disturbance

We proposed three swing leg control policies for spring-mass running robots, inspired by experimental data from our recent collaborative work on ground running birds. Previous investigations suggest that animals may prioritize injury avoidance and/or efficiency as their objective function during running rather than maintaining limit-cycle stability. Therefore, in this study we targeted structural capacity (maximum leg force to avoid damage) and efficiency as the main goals for our control policies, since these objective functions are crucial to reduce motor size and structure weight. Each proposed policy controls the leg angle as a function of time during flight phase such that its objective function during the subsequent stance phase is regulated. The three objective functions that are regulated in the control policies are (i) the leg peak force, (ii) the axial impulse, and (iii) the leg actuator work. It should be noted that each control policy regulates one single objective function. Surprisingly, all three swing leg control policies result in nearly identical subsequent stance phase dynamics. This implies that the implementation of any of the proposed control policies would satisfy both goals (damage avoidance and efficiency) at once. Furthermore, all three control policies require a surprisingly simple leg angle adjustment: leg retraction with constant angular acceleration

Control motion approach of a lower limb orthosis to reduce energy consumption

By analysing the dynamic principles of the human gait, an economic gait‐control analysis is performed, and passive elements are included to increase the energy efficiency in the motion control of active orthoses. Traditional orthoses use position patterns from the clinical gait analyses (CGAs) of healthy people, which are then de‐normalized and adjusted to each user. These orthoses maintain a very rigid gait, and their energy cosT is very high, reducing the autonomy of the user. First, to take advantage of the inherent dynamics of the legs, a state machine pattern with different gains in eachstate is applied to reduce the actuator energy consumption. Next, different passive elements, such as springs and brakes in the joints, are analysed to further reduce energy consumption. After an off‐line parameter optimization and a heuristic improvement with genetic algorithms, a reduction in energy consumption of 16.8% is obtained by applying a state machine control pattern, and a reduction of 18.9% is obtained by using passive elements. Finally, by combining both strategies, a more natural gait is obtained, and energy consumption is reduced by 24.6%compared with a pure CGA pattern

Improving Inverse Dynamics Accuracy in a Planar Walking Model Based on Stable Reference Point

Physiologically and biomechanically, the human body represents a complicated system with an abundance of degrees of freedom (DOF). When developing mathematical representations of the body, a researcher has to decide on how many of those DOF to include in the model. Though accuracy can be enhanced at the cost of complexity by including more DOF, their necessity must be rigorously examined. In this study a planar seven-segment human body walking model with single DOF joints was developed. A reference point was added to the model to track the body’s global position while moving. Due to the kinematic instability of the pelvis, the top of the head was selected as the reference point, which also assimilates the vestibular sensor position. Inverse dynamics methods were used to formulate and solve the equations of motion based on Newton-Euler formulae. The torques and ground reaction forces generated by the planar model during a regular gait cycle were compared with similar results from a more complex three-dimensional OpenSim model with muscles, which resulted in correlation errors in the range of 0.9–0.98. The close comparison between the two torque outputs supports the use of planar models in gait studies

Energy-Efficient Monopod Running with a Large Payload Based on Open-Loop Parallel Elastic Actuation

Despite the intensive investigations in the past, energetic efficiency is still one of the most important unsolved challenges in legged robot locomotion. This paper presents an unconventional approach to the problem of energetically efficient legged locomotion by applying actuation for spring mass running. This approach makes use of mechanical springs incorporated in parallel with relatively low-torque actuation, which is capable of both accommodating large payload and locomotion with low power input by exploiting self-excited vibration. For a systematic analysis, this paper employs both simulation models and physical platforms. The experiments show that the proposed approach is scalable across different payload between 0 and 150kg, and able to achieve a total cost of transport (TCOT) of 0.10, which is significantly lower than the previous locomotion robots and most of the biological systems in the similar scale, when actuated with the near-to natural frequency with the maximum payload.This study was supported by the Swiss National Science Foundation Grant No. PP00P2123387/1 and the Swiss National Science Foundation through the National Centre of Competence in Research Robotics

Energy-Efficient Monopod Running with a Large Payload Based on Open-Loop Parallel Elastic Actuation

Despite the intensive investigations in the past, energetic efficiency is still one of the most important unsolved challenges in legged robot locomotion. This paper presents an unconventional approach to the problem of energetically efficient legged locomotion by applying actuation for spring mass running. This approach makes use of mechanical springs incorporated in parallel with relatively low-torque actuation, which is capable of both accommodating large payload and locomotion with low power input by exploiting self-excited vibration. For a systematic analysis, this paper employs both simulation models and physical platforms. The experiments show that the proposed approach is scalable across different payload between 0 and 150kg, and able to achieve a total cost of transport (TCOT) of 0.10, which is significantly lower than the previous locomotion robots and most of the biological systems in the similar scale, when actuated with the near-to natural frequency with the maximum payload.This study was supported by the Swiss National Science Foundation Grant No. PP00P2123387/1 and the Swiss National Science Foundation through the National Centre of Competence in Research Robotics

Energy-Efficient Bipedal Gait Pattern Generation via CoM Acceleration Optimization

Energy consumption for bipedal walking plays a central role for a humanoid robot with limited battery capacity. Studies have revealed that exploiting the allowable Zero Moment Point region (AZR) and Center of Mass (CoM) height variation (CoMHV) are strategies capable of improving energy performance. In general, energetic cost is evaluated by integrating the electric power of multi joints. However, this Joint-Power-based Index requires computing joint torques and velocities in advance, which usually requires time-consuming iterative procedures, especially for multi-joints robots. In this work, we propose a CoM-Acceleration-based Optimal Index (CAOI) to synthesize an energetically efficient CoM trajectory. The proposed method is based on the Linear Inverted Pendulum Model, whose energetic cost can be easily measured by the input energy required for driving the point mass to track a reference trajectory. We characterize the CoM motion for a single walking cycle and define its energetic cost as Unit Energy Consumption. Based on the CAOI, an analytic solution for CoM trajectory generation is provided. Hardware experiments demonstrated the computational efficiency of the proposed approach and the energetic benefits of exploiting AZR and CoMHV strategies