1,609 research outputs found

    Evolved embodied phase coordination enables robust quadruped robot locomotion

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    Overcoming robotics challenges in the real world requires resilient control systems capable of handling a multitude of environments and unforeseen events. Evolutionary optimization using simulations is a promising way to automatically design such control systems, however, if the disparity between simulation and the real world becomes too large, the optimization process may result in dysfunctional real-world behaviors. In this paper, we address this challenge by considering embodied phase coordination in the evolutionary optimization of a quadruped robot controller based on central pattern generators. With this method, leg phases, and indirectly also inter-leg coordination, are influenced by sensor feedback.By comparing two very similar control systems we gain insight into how the sensory feedback approach affects the evolved parameters of the control system, and how the performances differs in simulation, in transferal to the real world, and to different real-world environments. We show that evolution enables the design of a control system with embodied phase coordination which is more complex than previously seen approaches, and that this system is capable of controlling a real-world multi-jointed quadruped robot.The approach reduces the performance discrepancy between simulation and the real world, and displays robustness towards new environments.Comment: 9 page

    Path Planning of Mobile Agents using AI Technique

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    In this paper, we study coordinated motion in a swarm robotic system, called a swarm-bot. A swarm-bot is a self-assembling and self-organizing. Artifact composed of a swarm of s-bots, mobile robots with the ability to connect to and is connect from each other. The swarm-bot concept is particularly suited for tasks that require all-terrain navigation abilities, such as space exploration or rescue in collapsed buildings. As a first step toward the development of more complex control strategies, we investigate the case in which a swarm-bot has to explore an arena while avoiding falling into holes. In such a scenario, individual s-bots have sensory–motor limitations that prevent them navigating efficiently. These limitations can be overcome if the s-bots are made to cooperate. In particular, we exploit the s-bots’ ability to physically connect to each other. In order to synthesize the s-bots’ controller, we rely on artificial evolution, which we show to be a powerful tool for the production of simple and effective solutions to the hole avoidance task

    Evolvability signatures of generative encodings: beyond standard performance benchmarks

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    Evolutionary robotics is a promising approach to autonomously synthesize machines with abilities that resemble those of animals, but the field suffers from a lack of strong foundations. In particular, evolutionary systems are currently assessed solely by the fitness score their evolved artifacts can achieve for a specific task, whereas such fitness-based comparisons provide limited insights about how the same system would evaluate on different tasks, and its adaptive capabilities to respond to changes in fitness (e.g., from damages to the machine, or in new situations). To counter these limitations, we introduce the concept of "evolvability signatures", which picture the post-mutation statistical distribution of both behavior diversity (how different are the robot behaviors after a mutation?) and fitness values (how different is the fitness after a mutation?). We tested the relevance of this concept by evolving controllers for hexapod robot locomotion using five different genotype-to-phenotype mappings (direct encoding, generative encoding of open-loop and closed-loop central pattern generators, generative encoding of neural networks, and single-unit pattern generators (SUPG)). We observed a predictive relationship between the evolvability signature of each encoding and the number of generations required by hexapods to adapt from incurred damages. Our study also reveals that, across the five investigated encodings, the SUPG scheme achieved the best evolvability signature, and was always foremost in recovering an effective gait following robot damages. Overall, our evolvability signatures neatly complement existing task-performance benchmarks, and pave the way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary figures. Accepted at Information Sciences journal (in press). Supplemental videos are available online at, see http://goo.gl/uyY1R

    Co-optimising Robot Morphology and Controller in a Simulated Open-Ended Environment

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    Designing robots by hand can be costly and time consuming, especially if the robots have to be created with novel materials, or be robust to internal or external changes. In order to create robots automatically, without the need for human intervention, it is necessary to optimise both the behaviour and the body design of the robot. However, when co-optimising the morphology and controller of a locomoting agent the morphology tends to converge prematurely, reaching a local optimum. Approaches such as explicit protection of morphological innovation have been used to reduce this problem, but it might also be possible to increase exploration of morphologies using a more indirect approach. We explore how changing the environment, where the agent locomotes, affects the convergence of morphologies. The agents' morphologies and controllers are co-optimised, while the environments the agents locomote in are evolved open-endedly with the Paired Open-Ended Trailblazer (POET). We compare the diversity, fitness and robustness of agents evolving in environments generated by POET to agents evolved in handcrafted curricula of environments. Our agents each contain of a population of individuals being evolved with a genetic algorithm. This population is called the agent-population. We show that agent-populations evolving in open-endedly evolving environments exhibit larger morphological diversity than agent-populations evolving in hand crafted curricula of environments. POET proved capable of creating a curriculum of environments which encouraged both diversity and quality in the populations. This suggests that POET may be capable of reducing premature convergence in co-optimisation of morphology and controllers.Comment: 17 pages, 8 figure

    Automation and robotics for the Space Exploration Initiative: Results from Project Outreach

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    A total of 52 submissions were received in the Automation and Robotics (A&R) area during Project Outreach. About half of the submissions (24) contained concepts that were judged to have high utility for the Space Exploration Initiative (SEI) and were analyzed further by the robotics panel. These 24 submissions are analyzed here. Three types of robots were proposed in the high scoring submissions: structured task robots (STRs), teleoperated robots (TORs), and surface exploration robots. Several advanced TOR control interface technologies were proposed in the submissions. Many A&R concepts or potential standards were presented or alluded to by the submitters, but few specific technologies or systems were suggested

    A Multitasking Surface Exploration Rover System

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