Evolutionary Robotics Approach Research Articles

An evolutionary robotics approach to a multi-legged robotic swarm in a rough terrain environment

An evolutionary robotics approach to a multi-legged robotic swarm in a rough terrain environment

When Less Is More in Embodied Evolution: Robotic Swarms Have Better Evolvability with Constrained Communication

Embodied evolution is an evolutionary robotics approach that implements an evolutionary algorithm over a population of robots and evolves while the robots perform their tasks. In embodied evolution, robots send and receive genomes from their neighbors and generate an offspring genome from the exchanged genomes. This study focused on the effects of the communication range for exchanging genomes on the evolvability of embodied evolution. Experiments were conducted using computer simulations, where robot controllers were evolved during a two-target navigation task. The results of the experiments showed that the robotic swarm could achieve better performance by reducing the communication range for exchanging genomes.

Generating collective behavior of a multi-legged robotic swarm using an evolutionary robotics approach

Generating collective behavior of a multi-legged robotic swarm using an evolutionary robotics approach

Wankelmut: A Simple Benchmark for the Evolvability of Behavioral Complexity

In evolutionary robotics, an encoding of the control software that maps sensor data (input) to motor control values (output) is shaped by stochastic optimization methods to complete a predefined task. This approach is assumed to be beneficial compared to standard methods of controller design in those cases where no a priori model is available that could help to optimize performance. For robots that have to operate in unpredictable environments as well, an evolutionary robotics approach is favorable. We present here a simple-to-implement, but hard-to-pass benchmark to allow for quantifying the “evolvability” of such evolving robot control software towards increasing behavioral complexity. We demonstrate that such a model-free approach is not a free lunch, as already simple tasks can be unsolvable barriers for fully open-ended uninformed evolutionary computation techniques. We propose the “Wankelmut” task as an objective for an evolutionary approach that starts from scratch without pre-shaped controller software or any other informed approach that would force the behavior to be evolved in a desired way. Our main claim is that “Wankelmut” represents the simplest set of problems that makes plain-vanilla evolutionary computation fail. We demonstrate this by a series of simple standard evolutionary approaches using different fitness functions and standard artificial neural networks, as well as continuous-time recurrent neural networks. All our tested approaches failed. From our observations, we conclude that other evolutionary approaches will also fail if they do not per se favor or enforce the modularity of the evolved structures and if they do not freeze or protect already evolved functionalities from being destroyed again in the later evolutionary process. However, such a protection would require a priori knowledge of the solution of the task and contradict the “no a priori model” approach that is often claimed in evolutionary computation. Thus, we propose a hard-to-pass benchmark in order to make a strong statement for self-complexifying and generative approaches in evolutionary computation in general and in evolutionary robotics specifically. We anticipate that defining such a benchmark by seeking the simplest task that causes the evolutionary process to fail can be a valuable benchmark for promoting future development in the fields of artificial intelligence, evolutionary robotics, and artificial life.

Behavioral specialization emerges from the embodiment of a robotic swarm

This paper focuses on the effect of the embodiment of robots on collective behavior in robotic swarms. The research field of swarm robotics emphasizes the importance of the embodiment of robots; however, only a few studies have discussed how it influences the collective behavior of a robotic swarm. In this paper, a path-formation task is performed by robotic swarms in computer simulations with and without considering collisions among robots to discuss the effect of the robot embodiment. Additionally, the experiments were performed with varying the size of robots. The robot controllers were obtained by an evolutionary robotics approach. The results show that the robot collisions would affect not only the performance of the robotic swarm but also the emergent behavior to accomplish the task. The robot collisions seem to provide feedback on robotic swarms to emerge the division of labor among robots to manage congestion.

Evolving collective cognition for object identification in foraging robotic swarms

This paper focuses on the collective cognition by robotic swarms. The robotic swarms expected to perform tasks that are beyond the capability of a single robot by collective behavior that emerge from local interactions, similar to biological swarms. However, the robotic swarms have to rely on the collective cognition more than biological swarms when considering the limitation in sensory capabilities and the cost of each robot. In this paper, we develop controllers for a robotic swarm to accomplish a foraging task that requires collective cognition. In this task, robots have to both collectively distinguish two objects, namely food and poison, and cooperatively transport food objects to the nest. We applied an evolutionary robotics approach with the covariance matrix adaptation evolution strategy to develop controllers for robotic swarms. The results of computer simulations show that collective cognition was successfully developed, which allows the robots to transport only food objects. In addition, we also perform experiments to examine the scalability and the flexibility of the developed controllers.

Effects of Congestion on Swarm Performance and Autonomous Specialization in Robotic Swarms

This paper focuses on the effect of congestion on swarm performance by considering the number of robots and their size. Swarm robotics is the study of a large group of autonomous robots from which collective behavior emerges without reliance on any centralized control. Due to the fact that robotic swarms are composed of a large number of robots, it is important to consider the congestion among them. However, only a few studies have focused on the relationship between the congestion and the performance of robotic swarms; moreover, these studies only discuss the effect of the number of robots. In this study, experiments were conducted by computer simulation and carried out by varying both the number of robots and the size of the robots in a path formation task. The robot controller was designed with an evolutionary robotics approach. The results show that not only the number of robots but also their size are essential features in the relationship between congestion and swarm performance. In addition, autonomous specialization within the robotic swarm emerged in situations with moderate congestion.

Evolving autonomous specialization in congested path formation task of robotic swarms

Redundancy in the number of robots is a fundamental feature of robotic swarms to confer robustness, flexibility, and scalability. However, robots tend to interfere with each other in a case, where multiple robots gather in a spatially limited environment. The aim of this paper is to understand how a robotic swarm develops an effective strategy to manage congestion. The controllers of the robots are obtained by an evolutionary robotics approach. The strategy of managing congestion is observed in the process of generating a collective path of robots visiting two landmarks alternately. The robotic swarm exhibits autonomous specialization that the robots traveling inside the path activate the LEDs, while the robots in the outer side deactivate them. We found that the congestion is regulated in an emergent way of autonomous specialization by the result of an artificial evolution.

Autonomous task allocation by artificial evolution for robotic swarms in complex tasks

Swarm robotics is a field in which multiple robots coordinate their collective behavior autonomously to accomplish a given task without any form of centralized control. In swarm robotics, task allocation refers to the behavior resulting in robots being dynamically distributed over different sub-tasks, which is often required for solving complex tasks. It has been well recognized that evolutionary robotics is a promising approach to the development of collective behaviors for robotic swarms. However, the artificial evolution often suffers from two issues—the bootstrapping problem and deception—especially when the underlying task is profoundly complex. In this study, we propose a two-step scheme consisting of task partitioning and autonomous task allocation to overcome these difficulties. We conduct computer simulation experiments where robotic swarms have to accomplish a complex collective foraging problem, and the results show that the proposed approach leads to perform more effectively than a conventional evolutionary robotics approach.

Evolution of Repertoire-Based Control for Robots With Complex Locomotor Systems

The evolution of task-oriented control for robots with complex locomotor systems is currently out of reach for traditional evolutionary computation techniques, as the coordination of a high number of locomotion parameters in response to the robot’s sensory inputs is extremely challenging. Evolutionary techniques have therefore mainly been applied to the optimization of specific locomotion patterns, such as forward motion. In this paper, we explore the evolutionary repertoire-based control (EvoRBC) approach, which divides the synthesis of control into two steps: 1) the evolution of a repertoire of locomotion primitives using a quality diversity algorithm and 2) the evolution of a high-level arbitrator that leverages the locomotion primitives in the repertoire to solve a given task. We comprehensively study the main components of the EvoRBC approach using a four-wheel steering robot. We then conduct a set of experiments in simulation using a hexapod robot. Our results show that EvoRBC is robust to parameter variations, and for all the robots tested, it is able to evolve controllers for a maze navigation task and significantly outperforms the traditional evolutionary robotics approach.

Heuristics for the Multi-Robot Worst-Case Pursuit-Evasion Problem

There is a growing demand for the use of robots to assist humans in their tasks, especially those involving risks, such as search and rescue. For this reason, coordination among several robots has been a common option, and one of the ways to study and model these applications involves the problem of pursuit evasion. This paper extends the results presented earlier on the use of an evolutionary robotics approach to solve the worst case pursuit-evasion problem, in which evaders are considered arbitrarily fast and omniscient, while pursuers have limited sensing and communication capabilities, with no prior knowledge regarding environments, which are treated as discrete and can be multiply connected. First, a formulation based on random walk is offered. Then, the concept is extended to include a decentralized multi-robot control system based on a finite-state machine with state-action mapping defined by means of a genetic algorithm. Results show that the proposed system is able to decontaminate several types of maps, but does not generalize to all initial conditions, due to the incompleteness in the automaton mapping. Therefore, a complementary approach is presented in which random walk is used alternatively with the evolved automaton, indicating random actions in cases of states not sufficiently visited during evolution. In addition, a comparative analysis of the evolutionary approach and the random walk formulation is also carried out.

Studying the Development of Spatial Cognition with Evolving Neuro-Agents

Studying the Development of Spatial Cognition with Evolving Neuro-Agents This paper describes an evolutionary robotics approach to study spatial cognition. Through the analysis of evolving neuroagents, run on behavioral, computational and evolutionary/development level, we show how relevant is considering the dynamic perspective in the observed phenomenon genesis and how useful can be using the evolutionary robotics methodology to address this issue. The experiments described in this short paper use simulated agents led by an artificial neural network and evolved through artificial selection to accomplish some spatial tasks commonly used in animal psychology. Results indicate that behaviors, especially biases, can be understood only taking into account the evolutionary/ development pathway of each agent.

An evolutionary robotics approach for the distributed control of satellite formations

We propose and study a decentralized formation flying control architecture based on the evolutionary robotic technique. We develop our control architecture for the MIT SPHERES robotic platform on board the International Space Station and we show how it is able to achieve micrometre and microradians precision at the path planning level. Our controllers are homogeneous across satellites and do not make use of labels (i.e. all satellites can be exchanged at any time). The evolutionary process is able to produce homogeneous controllers able to plan, with high precision, for the acquisition and maintenance of any triangular formation.

2A2-J06 漸進進化を用いた協調採餌タスクにおけるロボティックスワームの群れ行動生成(スワームロボティクス)

Swarm robotics is the research field of multi-robot systems which consist of many homogeneous autonomous robots without any type of global controllers. In this paper, an evolutionary robotics (ER) approach, i.e., the method that robot controllers represented using artificial neural networks are designed by evolutionary algorithms, is applied. For complex tasks in ER approach, however, all individuals in the first generations are often scored with the same null value, and as a consequence the selection process cannot operate. In order to overcome this "bootstrap" problem, we apply an incremental approach to evolution within the context of evolutionary robotics. As a benchmark of robotic swarms, cooperative food-foraging problems are conducted to examine their performance.

Generating Cooperative Collective Behavior in Swarm Robotic Systems

Swarm robotics research involves multirobot systems that consist of many homogeneous autonomous robots but no global controller. In this paper, an evolutionary robotics approach using an artificial neural network is applied to a swarm robotic system. Conventionally, the neural network evolved using only synaptic weights under the condition of a fixed topology. Our research group has been developing a novel approach to a topology and weight evolving artificial neural network named Mutation-Based Evolving Artificial Neural Network (MBEANN). A series of computer simulations shows that MBEANN yields better results in terms of flexibility than conventional solutions to the cooperative package-pushing problem.

Evolutionary robotics approach to odor source localization

An Evolutionary Robotics (ER) approach to the task of odor source localization is investigated. In particular, Continuous Time Recurrent Neural Networks (CTRNNs) are evolved for odor source localization in simulated turbulent odor plumes. In the experiments, the simulated robot is equipped with a single chemical sensor and a wind direction sensor. Three main contributions are made. First, it is shown that the ER approach can be successfully applied to odor source localization in both low-turbulent and high-turbulent conditions. Second, it is demonstrated that a small neural network is able to successfully perform all three sub-tasks of odor source localization: (i) finding the odor plume, (ii) moving toward the odor source, and (iii) identifying the odor source. Third, the analysis of the evolved behaviors reveals two novel odor source localization strategies. These strategies are successfully re-implemented as finite state machines, validating the insights from the analysis of the neural controllers.

Evolutionary controller design for area search using multiple UAVs with minimum altitude maneuver

Simultaneous operation of multiple UAVs enables to enhance the mission accomplishment efficiency. In order to achieve this, easily scalable control algorithms are required, and swarm intelligence having such characteristics as flexibility, robustness, decentralized control, and self-organization based on behavioral model comes into the spotlight as a practical alternative. Recently, evolutionary robotics is applied to the control of UAVs to overcome the weakness of difficulties in the logical design of behavioral rules. In this paper, the neural net controller evolved by evolutionary robotics approach is applied to the control of multiple UAVs which have the mission of searching limited area as much as possible. By applying incremental evolution technique we could evolve a neural net controller which can minimize energy consumption without sacrificing the performance of area coverage and collision avoidance.

Modelling a hormone-inspired controller for individual- and multi-modular robotic systems

For all living organisms, the ability to regulate internal homeostasis is a crucial feature. This ability to control variables around a set point is found frequently in the physiological networks of single cells and of higher organisms. Also, nutrient allocation and task selection in social insect colonies can be interpreted as homeostatic processes of a super-organism. And finally, behaviour can also represent such a control scheme. We show how a simple model of hormone regulation, inspired by simple biological organisms, can be used as a novel method to control the behaviour of autonomous robots. We demonstrate the formulation of such an artificial homeostatic hormone system (AHHS) by a set of linked difference equations and explain how the homeostatic control of behaviour is achieved by homeostatic control of the internal ‘hormonal’ state of the robot. The first task that we used to check the quality of our AHHS controllers was a very simple one, which is often a core functionality in controller programmes that are used in autonomous robots: obstacle avoidance. We demonstrate two implementations of such an AHHS controller that performs this task in differing levels of quality. Both controllers use the concept of homeostatic control of internal variables (hormones) and they extend this concept to also include the outside world of the robots into the controlling feedback loops: As they try to regulate internal hormone levels, they are forced to keep a homeostatic control of sensor values in a way that the desired goal ‘obstacle avoidance’ is achieved. Thus, the created behaviour is also a manifestation of the acts of homeostatic control. The controllers were evaluated using a stock-and-flow model that allowed sensitivity analysis and stability tests. Afterwards, we have also tested both controllers in a multi-agent simulation tool, which allowed us to predict the robots' behaviours in various habitats and group sizes. Finally, we demonstrate how this novel AHHS controller is suitable to control a multi-cellular robotic organism in an evolutionary robotics approach, which is used for self-programming in a gait-learning task. These examples shown in this article represent the first step in our research towards autonomous aggregation and coordination of robots to higher-level modular robotic organisms that consist of several joined autonomous robotic units. Finally, we plan to achieve such aggregation patterns and to control complex-shaped robotic organisms using AHHS controllers, as they are described here.

Coordinating Adaptive Behavior for Swarm Robotics Based on Topology and Weight Evolving Artificial Neural Networks

Swarm robotics is the research field of multi-robot systems which consist of many homogeneous autonomous robots without any type of global controllers. Generally, since a task given to this system can not be achieved by a single autonomous robot, emergent cooperative behavior is expected in a robot swarm by a certain mechanism through the interactions among the robots or with an environment. In this paper, an evolutionary robotics approach, i.e., the method that the robot controllers are designed by evolutionary algorithms with artificial neural networks, is applied. Among many approaches to evolving artificial neural networks, two approaches, NEAT and MBEANN, are adopted for computer simulations. Although a conventional neural network evolves only synaptic weights with the condition of a fixed topology, NEAT and MBEANN evolve not only their synaptic weights but also their topologies. As a benchmark of swarm robotics, cooperative package pushing problems by ten autonomous robots are conducted to examine their performance. The emerged behavioral characteristics are discussed.

2A2-F18 進化ロボティクスによる協調行動獲得に関する基礎研究

In this study, we treat the cooperative agents systems in which there are multiple autonomous mobile robots, and the 3-Dimensional balancing problem is given. This problem is an example of corresponding tasks to find the appropriate movements for multiple mobile robots. Each robot agent on a flat board keeps being balanced state by using evolutionary robotics approach. The behaviors of mobile robots are calculated by corresponding neuro-controller which consists of artificial neural networks. Evolutionary computation method is applied to evolve weight values of neuro-controller. Numerical simulations are carved out by a simulator constructed based on 3-D physical engine ODE (Open Dynamic Engine). As a result of simulations, usefulness of our approach is clarified.