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

Parallel elastic actuation for efficient large payload locomotion

For legged devices, their ability of carrying payload is a necessity for a wide range of tasks. In this paper, we present a new approach of carrying payload by using a parallel elastic mechanism, which is able to carry payloads at least 3 times of its bodyweight. Although the robot has no sensory feedback and consists of only two rigid bodies and one spring loaded joint, it is able to achieve efficient and stable forward hopping for a wide range of attached payload. The presented payload carrier ETH Cargo is based on the further development of our platform CHIARO for the payload range between 0 and 100kg. After parameter optimizing using simulations, a series of real world experiments prove stable and high efficiency hopping of the prototype over a wide range of payloads.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.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICRA.2015.713927

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

Efficient and Stable Locomotion for Impulse-Actuated Robots Using Strictly Convex Foot Shapes

Impulsive actuation enables robots to perform agile manoeuvres and surpass difficult terrain, yet its capacity to induce continuous and stable locomotion have not been explored. We claim that strictly convex foot shapes can improve impulse effectiveness (impulse used per travelled distance) and locomotion speed by facilitating periodicity and stability. To test this premise, we introduce a theoretical two-dimensional model based on rigidbody mechanics to prove stability. We then implement a more elaborate model in simulation to study transient behaviour and impulse effectiveness. Finally, we test our findings on a robot platform to prove their physical validity. Our results prove, that continuous and stable locomotion can be achieved in the strictly convex case of a disc with off-centred mass. In keeping with our theory, stable limit cycles of the off-centred disc outperform the theoretical performance of a cube in simulation and experiment, using up to 10 times less impulse per distance to travel at the same locomotion speed

On the discretisation of actuation in locomotion: Impulse- and shape-based modelling for hopping robots

In an age where computers challenge the smartest human beings in cognitive tasks, the conspicuous discrepancy between robot and animal locomotion appears paradoxical. While animals can move around autonomously in complex environments, today’s robots struggle to independently operate in such surroundings. There are many reasons for robots’ inferior performance, but arguably the most important one is our missing understanding of complexity. This thesis introduces the notion of discrete actuation for the study of locomotion in robots and animals. The actuation of a system with discrete actuation is restricted to be applied at a finite number of instants in time and is impulsive. We find that, despite their simplicity, such systems can predict various experimental observations and inspire novel technologies for robot design and control. We further find that, through the study of discrete actuation, causal relationships between actuation and resulting behaviour are revealed and become quantifiable, which relates the findings presented in this thesis to the broader concepts of complexity, self-organisation, and self-stability. We present four case studies in Chapters 3-6 which demonstrate how the concept of discrete actuation can be employed to understand the physics of locomotion and to facilitate novel robot technologies. We first introduce the impulsive eccentric wheel model which is a discretely actuated system for the study of hopping locomotion. We find that the model predicts robot hopping trajectories and animal related hopping characteristics by reducing the dynamics of hopping–usually described by hybrid differential equations–to analytic maps. The reduction of complexity of the model equations reveals the underlying physics of the locomotion process, and we identify the importance of robot shape and mass distribution for the locomotion performance. As a concrete application of the model, we compare the energetics of hopping and rolling locomotion in environments with obstacles and find when it is better to hop than to roll, based on the fundamental physical principles we discover in the model analysis. The theoretical insights of this modelling approach enable new actuation techniques and design for robots which we display in Robbit; a robot that uses strictly convex foot shapes and rotational impulses to induce hopping locomotion. We show that such systems outperform hopping with non-strictly convex shapes in terms of energy effective and robust locomotion. A system with discrete actuation motivates the exploitation of shape and the environment to improve locomotion dynamics, which reveals advantageous effect of inelastic impacts between the robot foot and the environment. We support this idea with experimental results from the robot CaneBot which can change its foot shape to induce timed impacts with the environment. Even though inelastic impacts are commonly considered detrimental for locomotion dynamics, we show that their appropriate control improves the locomotion speed considerably. The findings presented in this thesis show that discrete actuation for locomotion inspires novel ways to appreciate locomotion dynamics and facilitates unique control and design technologies for robots. Furthermore, discrete actuation emphasises the definition of causality in complex systems which we believe will bring robots closer to the locomotion behaviour of animals, enabling more agile and energy effective robots

Adaptive, fast walking in a biped robot under neuronal control and learning

Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (> 3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks

A self organization approach to goal-directed multimodal locomotion based on Attractor Selection Mechanism

The realization and utilization of multimodal locomotion to enable robots to accomplish useful tasks is a significantly challenging problem in robotics. Related to the challenge, it is crucial to notice that the locomotion dynamics of the robots is a result of interactions between a particular control structure and its body-environment dynamics. From this perspective, this paper presents a simple control structure known as Attractor Selection Mechanism that enables a robot to self organize its multiple locomotion modes for accomplishing a goal-directed locomotion task. Despite the simplicity, the approach enables the robot to automatically explore different body-environment dynamics and stabilize onto particular attractors which corresponds to locomotion modes relevant to accomplish the task. The robot used throughout the paper is a curved-beam hopping robot, which despite its simple actuation method, possesses rich and complex bodyenvironment dynamics.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICRA.2015.713990