Adaptive Morphology for Multi-Modal Locomotion

There is a growing interest in using robots in dangerous environments, such as for exploration, search-and-rescue or monitoring applications, in order to reduce the risks for workers or rescuers and to improve their efficiency. Typically, flying robots offer the possibility to quickly explore large areas while ground robots can thoroughly search specific regions of interest. While existing robotic solutions are very promising, they are often limited to specific use cases or environments. This makes them impractical for most missions involving complex or unpredictable scenarios, such as search-and-rescue applications. This limitation comes from the fact that existing robots usually exploit only a single locomotion strategy, which limits their flexibility and adaptability to different environments. In this thesis, a multi-modal locomotion strategy is investigated as a way to increase the versatility of mobile robots. We explore integrated design approaches, where the same actuators and structure are used for different modes of locomotion, which allows a minimization of the weight and complexity of the robot. This strategy is challenging because a single locomotor system must accommodate the potentially conflicting dynamics of multiple modes of locomotion. Herein, we suggest taking inspiration from nature, in particular the common vampire bat \emph{Desmodus rotundus}. The goal being to make multiple modes of locomotion dynamically compatible (i.e. have compatible speeds and torques requirements), by optimizing the morphology of the locomotor system and even by adapting the morphology of the robot to a specific mode of locomotion. It is demonstrated in this thesis that the integrated design approach can be effectively implemented on a multi-modal aerial and terrestrial robot, and that two modes of locomotion can be made dynamically compatible by optimizing the morphology. Furthermore, an adaptive morphology is used to increase the efficiency of the different modes of locomotion. A locomotor system used both for walking on the ground and controlling flight, has been successfully implemented on a multi-modal robot, which further has deployable wings to increase its performances on the ground and in the air. By successfully exploiting the concepts of integrated design and adaptive morphology, this robot is capable of hovering, forward flight and ground locomotion. This robot demonstrates a very high versatility compared to state of the art of mobile robots, while having a low complexity.

Designing Minimal and Scalable Insect-Inspired Multi-Locomotion Millirobots

Kazuaki Mori, Jamie Paik, Zhenishbek Zhakypov

In ant colonies, collectivity enables division of labour and resources with great scalability. Beyond their intricate social behaviours, individuals of the genus Odontomachus, also known as trap-jaw ants, have developed remarkable multi-locomotion mechanisms to ‘escape-jump’ upwards when threatened, using the sudden snapping of their mandibles, and to negotiate obstacles by leaping forwards using their legs. Emulating such diverse insect biomechanics and studying collective behaviours in a variety of environments may lead to the development of multi-locomotion robotic collectives deployable in situations such as emergency relief, exploration and monitoring; however, reproducing these abilities in small-scale robotic systems with simple design and scalability remains a key challenge. Existing robotic collectives are confined to two-dimensional surfaces owing to limited locomotion, and individual multi-locomotion robots are difficult to scale up to large groups owing to the increased complexity, size and cost of hardware designs, which hinder mass production. Here we demonstrate an autonomous multi-locomotion insect-scale robot (millirobot) inspired by trap-jaw ants that addresses the design and scalability challenges of small-scale terrestrial robots. The robot’s compact locomotion mechanism is constructed with minimal components and assembly steps, has tunable power requirements, and realizes five distinct gaits: vertical jumping for height, horizontal jumping for distance, somersault jumping to clear obstacles, walking on textured terrain and crawling on flat surfaces. The untethered, battery-powered millirobot can selectively switch gaits to traverse diverse terrain types, and groups of millirobots can operate collectively to manipulate objects and overcome obstacles. We constructed the ten-gram palm-sized prototype—the smallest and lightest self-contained multi-locomotion robot reported so far—by folding a quasi-two-dimensional metamaterial sandwich formed of easily integrated mechanical, material and electronic layers, which will enable assembly-free mass-manufacturing of robots with high task efficiency, flexibility and disposability.

2019

Synthesis, modeling, and experimental validation of distributed robotic search

James Pugh

The work of this thesis centers around the research subject of distributed robotic search. Within the field of distributed robotic systems, a task of particular interest is attempting to locate one or more targets in a possibly unknown environment. While numerous studies have proposed and analyzed methods to accomplish this, there is currently a lack of a strong foundation of tools and techniques that can be used to facilitate the development and evaluation of different approaches to distributed robotic search. In this work, we aim to provide such a foundation through tools, methods, models, and analysis of experimentation. An often overlooked aspect of the distributed robotic research process is the development and analysis of tools and modules to be used with robotic systems. These may include plug-ins for realistic robotic simulators, software/hardware systems to track multiple mobile robots in real-time, extension boards for robotic platforms, and the robots themselves. Along with other tools developed and used for this work, we focus particularly on the development, characterization, and validation of a fast, accurate on-board system for relative positioning and communication between robots, a capability which is critical for effective distributed robotic search. Designing individual robot controllers to generate a specific group behavior is a difficult and often counter-intuitive process. A possible alternative to hand-crafting distributed search controllers is automatic synthesis using machine-learning techniques. We explore the effectiveness of using a noise-resistant version of the Particle Swarm Optimization algorithm to optimize the weights of an embedded Artificial Neural Network, allowing the robot to learn obstacle avoidance behavior (a common benchmark for robotic learning techniques); we find that this technique appears to offer superior performance as compared to the canonical approach of using Genetic Algorithms for this type of learning. A method for faster learning using distributed evaluation in a robot team is tested and is found to offer comparable performance using only a fraction of the original learning time. This technique can be used for fast, effective learning and adaptation in a distributed robotic system performing search. The process of designing and analyzing algorithms for distributed robotic systems can be greatly facilitated if models are available to describe the dynamics of the algorithm at an abstract level. Inspired by previous examples in the distributed robotic field, we work to design a model of robotic search that captures the system at different levels of abstraction, ranging from accurately recreating the details of individual robots to describing the entire system as an indivisible whole. To capture the entire search process, we model both the exploration phase, where robots cover an environment in an effort to detect traces of targets, and the localization phase, where robots use target emission sensing to navigate towards the target. The utility of our models is demonstrated by using them to develop an effective technique for the declaration phase of search, where robots decide that a target has been accurately localized and announce its position. In distributed robotics research, it is important that techniques developed with abstracted simulations and models are ultimately validated using real robotic platforms in order to verify their correctness. In that spirit, we run systematic sound search experiments using teams of up to ten real robots. These experiments utilize the tools developed throughout the research process, demonstrate the utility of our learning technique for fast search adaptation, and serve to validate our models of distributed robotic localization. In addition, they allow us to analyze and better understand the subtle dynamics of the search process, providing information which should be useful for any future work on distributed robotic search.

EPFL2008

Variable-Stiffness Tensegrity Modular Robots

Davide Zappetti

The need for a new generation of robots able to safely locomote and manipulate beside or cooperatively with humans or in un-constructed environments has recently emerged as a priority of the robotics community. ln the past two decades, this challenge has been tackled by the growing field of soft robotics mostly focusing on developing completely deformable soft robotic bodies that can freely deform along any direction and comply with any unexpected or excessive external force. However, intrinsic softness can be a limitation in situations that require exerting substantial forces, to withstand bodyweights or to modulate forces applied to the environment. Inspiration for a novel mechanically hybrid approach in robotics can come from a novel model for biological structures based on the concept of tensegrity. This model rejects the idea of the vertebrate musculoskeletal system as beams, columns, and levers, and acknowledges the bones as stiff under compression elements held and stabilized in their positions by the pull of tensile components such as muscles, tendons, ligaments, and fascia. Therefore, rather than a frame supporting an amorphous soft tissue mass, the whole vertebrate body is divided into several modules stabilized by these tension components and its stiffness can vary depending on the pre-stress (i.e. tone) and stiffness of its muscles. This structural organization allows biological organisms to be more mechanically adaptive, robust, and lightweight at the same time. They can exhibit high physical compliance, apply efficient forces to the environment when needed, and, at the same time, withstand their bodyweight at different scales. In the same way, modular tensegrity structures used in robotic applications can provide a lightweight and robust framework where integrating stiff under compression struts for bodyweight support; variable stiffness cables for applying directionally larger forces when required, and soft cables to absorb energy and allow compliance. ln this thesis, tensegrity modular structures are investigated as a new approach to develop soft robots with variable stiffness capabilities. This investigation led to a set of design strategies to manufacture and actuate tensegrity modules with programmable stiffness and deformation; to mechanically and electrically connect tensegrity modules to develop untethered applications, and to implement variable-stiffness capabilities in the modules for active and passive stiffness change. Finally, preliminary results and insights are presented on using heuristic algorithms as a design method to find task-optimal morphologies, stiffness, and control of modular tensegrity robots. In this thesis modular tensegrity robots have been developed for performing locomotion and grasping tasks, however, the design methods presented can provide a guideline for designing robots for diverse applications where being lightweight, robust, and having variable-stiffness capabilities are of primary importance. We believe that the work presented and discussed in this thesis answered a gap in the current literature and most importantly, will pave the way to future research on tensegrity robots for a great variety of tasks and robotic applications.

EPFL2021