Collision-based energetic comparison of rolling and hopping over obstacles.

Locomotion of machines and robots operating in rough terrain is strongly influenced by the mechanics of the ground-machine interactions. A rolling wheel in terrain with obstacles is subject to collisional energy losses, which is governed by mechanics comparable to hopping or walking locomotion. Here we investigate the energetic cost associated with overcoming an obstacle for rolling and hopping locomotion, using a simple mechanics model. The model considers collision-based interactions with the ground and the obstacle, without frictional losses, and we quantify, analyse, and compare the sources of energetic costs for three locomotion strategies. Our results show that the energetic advantages of the locomotion strategies are uniquely defined given the moment of inertia and the Froude number associated with the system. We find that hopping outperforms rolling at larger Froude numbers and vice versa. The analysis is further extended for a comparative study with animals. By applying size and inertial properties through an allometric scaling law of hopping and trotting animals to our models, we found that the conditions at which hopping becomes energetically advantageous to rolling roughly corresponds to animals' preferred gait transition speeds. The energetic collision losses as predicted by the model are largely verified experimentally

Model-Free Design Optimization of a Hopping Robot and Its Comparison With a Human Designer

When developing a robot, design iterations in the physical world are necessary, even though they are often costly and not systematic. Here, we present an automated iterative design process without using modeling or simulation, which we refer to as “model-free design optimization” based on Bayesian optimization. This letter particularly focuses on the cooptimization of morphology and controller, by using a mechanism to balance parameter specific costs (i.e., morphology samplings are more expensive than control ones) for effective and efficient design optimization processes. A hopping robot was employed for a feasibility analysis of the proposed optimization method, in which minimalistic two-dimensional and four-dimensional design optimization experiments were performed in real life. The results show that the proposed approach is capable of improving both of the robot design problems within a defined time limit. The method is also compared to optimization performances of a human designer under the same conditions. The automated method has advantage in finding the best solution more quickly in the four-dimensional search space, while the human optimization performs better in the two-dimensional case.This work was supported by the U.K. Engineering and Physical Sciences Research Council under Grant EP/N03211X/1

Physics driven behavioural clustering of free-falling paper shapes.

Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour.The Mathworks Ltd

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

Orbital Angular Momentum in Noncollinear Second Harmonic Generation by off-axis vortex beams

We experimentally study the behavior of orbital angular momentum (OAM) of light in a noncollinear second harmonic generation (SHG) process. The experiment is performed by using a type I BBO crystal under phase matching conditions with femtosecond pumping fields at 830 nm. Two specular off-axis vortex beams carrying fractional orbital angular momentum at the fundamental frequency (FF) are used. We analyze the behavior of the OAM of the SH signal when the optical vortex of each input field at the FF is displaced from the beam's axis. We obtain different spatial configurations of the SH field, always carrying the same zero angular momentum.Comment: 9 pages, 7 figure

Diffusion of individual birds in starling flocks

Flocking is a paradigmatic example of collective animal behaviour, where decentralized interaction rules give rise to a globally ordered state. In the emergence of order out of self-organization we find similarities between biological systems, as bird flocks, and some physical systems, as ferromagnets. In both cases, the tendency of individuals to align to their neighbours gives rise to a polarized state. There is, however, one crucial difference: the interaction network within an animal group is not necessarily fixed in time, as each individual moves and may change its neighbours. Therefore, the dynamical interaction mechanism in biological and physical system can be quite different, not only due to the gross disparity in the complexity of the individual entities, but also because of the potential role of inter-individual motion. To assess the relevance of this mechanism it is necessary to gain quantitative experimental information about how much individuals move with respect to each other within the group. Here, by using data from field observations on starlings, we study the diffusion properties of individual birds within a flock and investigate the effect of diffusion on the dynamics of the interaction network. We find that birds diffuse faster than Brownian particles (superdiffusion) and in a strongly anisotropic way. We also find that neighbours change in time exclusively as a consequence of diffusion, so that no specific mechanism to keep one's neighbours seems to be enforced. Finally, we study the diffusion properties of birds at the border of the flock. We find that these individuals remain on the border significantly longer than what would be expected on the basis of a purely diffusional model, suggesting that there is a sort barrier a bird must cross to make the transition from border to interior of the flock.Comment: 22 pages, 10 figure

GReTA - a novel Global and Recursive Tracking Algorithm in three dimensions

Tracking multiple moving targets allows quantitative measure of the dynamic behavior in systems as diverse as animal groups in biology, turbulence in fluid dynamics and crowd and traffic control. In three dimensions, tracking several targets becomes increasingly hard since optical occlusions are very likely, i.e. two featureless targets frequently overlap for several frames. Occlusions are particularly frequent in biological groups such as bird flocks, fish schools, and insect swarms, a fact that has severely limited collective animal behavior field studies in the past. This paper presents a 3D tracking method that is robust in the case of severe occlusions. To ensure robustness, we adopt a global optimization approach that works on all objects and frames at once. To achieve practicality and scalability, we employ a divide and conquer formulation, thanks to which the computational complexity of the problem is reduced by orders of magnitude. We tested our algorithm with synthetic data, with experimental data of bird flocks and insect swarms and with public benchmark datasets, and show that our system yields high quality trajectories for hundreds of moving targets with severe overlap. The results obtained on very heterogeneous data show the potential applicability of our method to the most diverse experimental situations.Comment: 13 pages, 6 figures, 3 tables. Version 3 was slightly shortened, and new comprative results on the public datasets (thermal infrared videos of flying bats) by Z. Wu and coworkers (2014) were included. in A. Attanasi et al., "GReTA - A Novel Global and Recursive Tracking Algorithm in Three Dimensions", IEEE Trans. Pattern Anal. Mach. Intell., vol.37 (2015