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Distributed evolutionary learning control for mobile robot navigation based on virtual and physical agents

SIMULATION MODELLING PRACTICE AND THEORY 2020年 102卷 102058-102058页

作者： Ponce, Hiram Moya-Albor, Ernesto Martinez-Villasemor, Lourdes Brieva, Jorge Univ Panamer Fac Ingn Augusto Rodin 498 Ciudad De Mexico 03920 Mexico

This paper presents a distributed evolutionary learning control based on social wound treatment for mobile robot navigation using an integrated multi-robot system comprised of simulated and physical robots. To do so, this work proposes an extension of the population-based metaheuristic wound treatment optimization (WTO) method into a distributed scheme. In addition, this distributed WTO method is implemented on the multi-robot system allowing them to experience the environment in their own and communicate their findings, resulting in an emergence intelligence. We implemented our proposal using the combination of five simulated robots with one physical robot for tuning a navigation controller to move freely in a workspace. Results showed that the solution found by this multi-robot system aims using the output controller in the physical robot for successfully achieving the goal to move the robot around a U-maze, without applying any transfer learning approach. We consider this proposal useful in evolutionary robotics, and of great importance to decrease the gap related to transfer knowledge in robotics from simulation to reality.

关键词： Multi-robot system evolutionary robotics Metaheuristic optimization Decentralized systems Control navigation

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Locating Odour Sources with Geometric Syntactic Genetic Programming 1

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23rd European International Conference on the Applications of evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Macedo, Joao Marques, Lino Costa, Ernesto Univ Coimbra Dept Elect & Comp Engn ISR P-3030290 Coimbra Portugal Univ Coimbra Dept Informat Engn CISUC P-3030290 Coimbra Portugal

ISBN: (数字)9783030437220

ISBN: (纸本)9783030437213;9783030437220

Using robots to locate odour sources is an interesting problem with important applications. Many researchers have drawn inspiration from nature to produce robotic methods, whilst others have attempted to automatically create search strategies with Artificial Intelligence techniques. This paper extends Geometric Syntactic Genetic Programming and applies it to automatically produce robotic controllers in the form of behaviour trees. The modification proposed enables Geometric Syntactic Genetic Programming to evolve trees containing multiple symbols per node. The behaviour trees produced by this algorithm are compared to those evolved by a standard Genetic Programming algorithm and to two bio-inspired strategies from the literature, both in simulation and in the real world. The statistically validated results show that the Geometric Syntactic Genetic Programming algorithm is able to produce behaviour trees that outperform the bio-inspired strategies, while being significantly smaller than those evolved by the standard Genetic Programming algorithm. Moreover, that reduction in size does not imply statistically significant differences in the performance of the strategies.

关键词： evolutionary robotics Odour source localisation Genetic Programming Geometric operators

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Levels of Coupling in Dyadic Interaction: An Analysis of Neural and Behavioral Complexity

Levels of Coupling in Dyadic Interaction: An Analysis of Neu...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Resendiz-Benhumea, Georgina Montserrat Sangati, Ekaterina Froese, Tom Okinawa Inst Sci & Technol Grad Univ Embodied Cognit Sci Unit Onna Okinawa Japan Univ Nacl Autonoma Mexico Inst Appl Math & Syst Res Mexico City DF Mexico

ISBN: (纸本)9781728125473

From an enactive approach, some previous studies have demonstrated that social interaction plays a fundamental role in the dynamics of neural and behavioral complexity of embodied agents. In particular, it has been shown that agents with a limited internal structure (2-neuron brains) that evolve in interaction can overcome this limitation and exhibit chaotic neural activity, typically associated with more complex dynamical systems (al least 3-dimensional). In the present paper we make two contributions to this line of work. First, we propose a conceptual distinction in levels of coupling between agents that could have an effect on neural and behavioral complexity. Second, we test the generalizability of previous results by testing agents with richer internal structure and evolving them in a richer, yet non-social, environment. We demonstrate that such agents can achieve levels of complexity comparable to agents that evolve in interactive settings. We discuss the significance of this result for the study of interaction.

关键词： agent-based modeling social interaction coupling neural entropy evolutionary robotics continuous-time recurrent neural network minimal cognition dyad

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Evolving neural networks to follow trajectories of arbitrary complexity

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NEURAL NETWORKS 2019年 116卷 224-236页

作者： Inden, Benjamin Jost, Jurgen Nottingham Trent Univ Dept Comp & Technol Clifton Lane Nottingham NG11 8NS England Max Planck Inst Math Sci Leipzig Germany Santa Fe Inst Santa Fe NM 87501 USA

Many experiments have been performed that use evolutionary algorithms for learning the topology and connection weights of a neural network that controls a robot or virtual agent. These experiments are not only performed to better understand basic biological principles, but also with the hope that with further progress of the methods, they will become competitive for automatically creating robot behaviors of interest. However, current methods are limited with respect to the (Kolmogorov) complexity of evolved behavior. Using the evolution of robot trajectories as an example, we show that by adding four features, namely (1) freezing of previously evolved structure, (2) temporal scaffolding, (3) a homogeneous transfer function for output nodes, and (4) mutations that create new pathways to outputs, to standard methods for the evolution of neural networks, we can achieve an approximately linear growth of the complexity of behavior over thousands of generations. Overall, evolved complexity is up to two orders of magnitude over that achieved by standard methods in the experiments reported here, with the major limiting factor for further growth being the available run time. Thus, the set of methods proposed here promises to be a useful addition to various current neuroevolution methods. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Neuroevolution Complexity Incremental evolution Open-ended evolution evolutionary robotics Trajectory learning

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Can Signal Delay be Functional? Including Delay in Evolved Robot Controllers

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ARTIFICIAL LIFE 2019年第4期25卷 315-333页

作者： Egbert, Matthew Keane, Andrew Postlethwaite, Claire Wong, Nelson Univ Auckland Sch Comp Sci Te Ao Marama Ctr Fundamental Inquiry Auckland New Zealand Univ Auckland Dept Math Auckland New Zealand

Engineers, control theorists, and neuroscientists often view the delay imposed by finite signal propagation velocities as a problem that needs to be compensated for or avoided. In this article, we consider the alternative possibility that in some cases, signal delay can be used functionally, that is, as an essential component of a cognitive system. To investigate this idea, we evolve a minimal robot controller to solve a basic stimulus-distinction task. The controller is constrained so that the solution must utilize a delayed recurrent signal. Different from previous evolutionary robotics studies, our controller is modeled using delay differential equations, which (unlike the ordinary differential equations of conventional continuous-time recurrent neural networks) can accurately capture delays in signal propagation. We analyze the evolved controller and its interaction with its environment using classical dynamical systems techniques. The analysis shows what kinds of invariant sets underlie the various successful and unsuccessful performances of the robot, and what kinds of bifurcations produce these invariant sets. In the second phase of our analysis, we turn our attention to the parameter theta, which describes the amount of signal delay included in the model. We show how the delay destabilizes certain attractors that would exist if there were no delay and creates other stable attractors, resulting in an agent that performs well at the target task.

关键词： evolutionary robotics delay delay differential equations lag dynamical systems finite signal conduction velocity

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Challenges in cooperative coevolution of physically heterogeneous robot teams

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NATURAL COMPUTING 2019年第1期18卷 29-46页

作者： Gomes, Jorge Mariano, Pedro Christensen, Anders Lyhne BioMachines Lab Lisbon Portugal Inst Telecomunicacoes Lisbon Portugal Univ Lisbon Fac Ciencias BioISI Lisbon Portugal IUL ISCTE Lisbon Portugal

Heterogeneous multirobot systems have shown significant potential in many applications. Cooperative coevolutionary algorithms (CCEAs) represent a promising approach to synthesise controllers for such systems, as they can evolve multiple co-adapted components. Although CCEAs allow for an arbitrary level of team heterogeneity, in previous works heterogeneity is typically only addressed at the behavioural level. In this paper, we study the use of CCEAs to evolve control for a heterogeneous multirobot system where the robots have disparate morphologies and capabilities. Our experiments rely on a simulated task where a simple ground robot must cooperate with a complex aerial robot to find and collect items. We first show that CCEAs can evolve successful controllers for physically heterogeneous teams, but find that differences in the complexity of the skills the robots need to learn can impair CCEAs' effectiveness. We then study how different populations can use different evolutionary algorithms and parameters tuned to the agents' complexity. Finally, we demonstrate how CCEAs' effectiveness can be improved using incremental evolution or novelty-driven coevolution. Our study shows that, despite its limitations, coevolution is a viable approach for synthesising control for morphologically heterogeneous systems.

关键词： evolutionary robotics Cooperative coevolution Multirobot systems Heterogeneous systems Premature convergence Artificial neural networks

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Sensor based evolutionary Mission Planning

Sensor based Evolutionary Mission Planning

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Bhardwaj, Akanksha Kala, Rahul Indian Inst Informat Technol Robot & Machine Intelligence Lab Allahabad Prayagraj India

ISBN: (纸本)9781728121536

Robot mission planning deals with accomplishing a mission that requires the robot to visit a set of sites and carrying out specific tasks at each site, consisting of Boolean and temporal constraints. The Boolean constraints are like "Visit any one of the three coffee machines" and "Visit any two of the three instructors",while the temporal constraints typically ask the robot to carry some sub-missions in a sequential order and some others in parallel. Each mission can have a different number of sites and nature of constraints involved. Each mission is expressed in a generalized form using OR, AND, and THEN clauses. Further,the notion of sensors is added, wherein a resource for a mission may be unavailable and therefore the robot will have to plan an alternate (maybe longer) strategy, however the availability will only be known when the robot physically visits the mission site. The typical mechanisms to solve for such mission using formal languages and verification engines incur an exponential complexity. Therefore, this paper proposes an evolutionary algorithm to solve the problem, enabling the use of probabilistic optimality properties. However, calculating the expected path length with the prior probabilities of the sensor is also exponential complexity which causes the resulting algorithm to have an exponential complexity in terms of the number of sensors. A greedy heuristic cost function is devised that imitates human decision making, wherein the robot tries to solve the mission with the shortest length as well as maximize the probability of completion of the task. Further, each site is assumed to have an associated waiting time which is the expected time for which the robot will have to wait in the queue till the resource is available for use. Another greedy heuristic is used to align the sites in the crossover operation to approach a better solution faster. Experimental results confirm that the proposed solution performs well as compared to baseline optimistic

关键词： mission planning evolutionary robotics evolutionary computation robot motion planning sensor-based planning policysearch

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Moonwalkers: Evolving Robots for Locomotion in a Moon-like Environment

Moonwalkers: Evolving Robots for Locomotion in a Moon-like E...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Koen Van Der Pool A.E. Eiben Vrije Universiteit Amsterdam Amsterdam The Netherlands

ISBN: (纸本)9781728190495

Robots are arguably essential for space research in the future, but designing and producing robots for unknown environments represents a grand challenge. The field of evolutionary robotics offers a solution by applying the principles of natural evolution to robot design. In this paper, we consider a Moon-like environment and investigate the joint evolution of morphologies (bodies) and controllers (brains) when fitness is determined by the ability to locomote. In particular, we are interested in the evolved morphologies and compare the emerging 'life forms' in a Moonlike environment to those evolved under Earth-like conditions. To model the Moon we change two environmental properties of our baseline environment that represents the Earth: gravity is set to a low value and the flat terrain is replaced by the NASA model of the Moon landing site of the Apollo 14. The results show that changing only one of these does not lead to different evolved robot morphologies, but changing both does. Our evolved Moonwalkers are usually bigger, have fewer limbs and a less space filling shape than the robots evolved on Earth.

关键词： Earth evolutionary robotics Shape Moon NASA Morphology Filling

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evolutionary Planning for Multi-User Multi-Task Missions

Evolutionary Planning for Multi-User Multi-Task Missions

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Kala, Rahul Indian Inst Informat Technol Robot & Machine Intelligence Lab Allahabad Prayagraj India

ISBN: (纸本)9781728121536

The problem of mission planning is to enable robots solve complex missions with Boolean and temporal operators in the mission specification. Typically, missions are specified using a Linear Temporal Logic Formulation and solved by using a model verification approach which has an exponential complexity. Given a language which is polynomial verifiable, evolutionary paradigm of mission planning can enable probabilistic optimality and probabilistic completeness, thus enabling the use of solvers for a very high number of variables, which is impossible to do using the model verification techniques. This paper motivates the heuristic of a mission consisting of a number of tasks, such that each task is a complex instruction given by a user, while many such users share a robot. The heuristic is used to generate a near-optimal solution of tasks that can then be fused optimally by a Dynamic Programming approach to make the solution of the mission. However, the problem is not decomposable and optimal solutions of tasks do not result in an optimal solution to mission. Hence a 2-step algorithm is used. The first step computes a near-optimal solution of the tasks. The second step does a full Genetic Algorithm search to generate task solutions that eventually fused by a Dynamic Programming approach produce an optimal mission solution. Comparative analysis is done with numerous baselines and the proposed approach is experimentally shown to perform better than all baselines. The experiments are also done on the Pioneer LX robot using the Robot Operating System framework.

关键词： mission planning robot motion planning evolutionary robotics evolutionary computation memetic computation

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Optimisation of water demand forecasting by artificial intelligence with short data sets

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BIOSYSTEMS ENGINEERING 2019年 177卷 59-66页

作者： Gonzalez Perea, Rafael Camacho Poyato, Emilio Montesinos, Pilar Rodriguez Diaz, Juan Antonio Univ Cordoba Dept Agron Campus RabanalesEdif Vinci E-14071 Cordoba Spain Univ Cordoba Dept Rural Engn Campus RabanalesEdif Vinci E-14071 Cordoba Spain

Irrigated agriculture is one of the key factors responsible for decreasing freshwater availability in recent years. Thus, the development of new tools which will help Irrigation District managers in their daily decision making process about the use of water and energy is essential. On the other hand, the new era of Big Data and information and communications technologies (ICT) has made it possible to have a larger amount of information available, leading to the development of new prediction tools. However, the quality and quantity of this information in many fields such as irrigated agriculture is limited. Consequently, the way in which the development of new predictive models is addressed must be reformulated. Thus, in this work, a new methodology to provide short-term forecasting of daily irrigation water demand when data availability is limited has been developed by coupling dynamic Artificial Neural Networks (ANN) architecture, the Bayesian framework and Genetic Algorithms (GA). The methodology was applied in the Bembezar MD Irrigation District (Southern Spain). The developed model improved the prediction accuracy by between 3% and 11% with respect to previous work. The best ANN model had a Standard Error Prediction (SEP) and a determination coefficient (R-2) of 8.7% and 96%, respectively. The accuracy of the model developed makes it a powerful tool for the daily management of irrigation districts. (C) 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： evolutionary robotics Optimal forecasting models Short time series Irrigation

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