Constructing musculoskeletal models of extinct vertebrates requires subjective assumptions about soft tissue parameters rarely preserved in the fossil record. Despite these necessary assumptions about fundamental inpu...
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Constructing musculoskeletal models of extinct vertebrates requires subjective assumptions about soft tissue parameters rarely preserved in the fossil record. Despite these necessary assumptions about fundamental input values, paleobiologists rarely perform objective tests of best-estimate models before reaching conclusions based on predicted results. The extent to which lack of knowledge on soft tissue anatomy limits the accuracy of running speed estimates of extinct dinosaurs is therefore poorly understood. In this study, a sensitivity analysis is performed on an evolutionary robotics model of the non-avian theropod dinosaur Allosaurus, used previously to estimate maximum running speed in this extinct animal. A range of muscle parameters were varied over the range observed in extant vertebrates, whereas mass-related parameters were altered across the range of published estimates for Allosaurus. Muscle parameters have a linear relationship with maximum running speed, whereas surprisingly total body mass and torso center of mass have little effect. Muscle force values produced the greatest range in predicted running speeds (4.5-10.7 m/s) and stride lengths (4-5.8 m) in the sensitivity analysis, equating to 65.9% and 30.7% variation about the original 'best-estimate' prediction, a relatively high potential margin of error. These results highlight the importance of sensitivity analyses in biomechanical modeling of extinct taxa, particularly where values for soft tissues parameters are not tightly constrained. The current range in plausible values for soft tissue properties makes a robust quantitative assessment of behavioral ecology and species interactions in dinosaurian communities extremely difficult.
In traditional evolutionary robotics, robot controllers are evolved in a separate design phase preceding actual deployment;we call this off-line evolution. Alternatively, robot controllers can evolve while the robots ...
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In traditional evolutionary robotics, robot controllers are evolved in a separate design phase preceding actual deployment;we call this off-line evolution. Alternatively, robot controllers can evolve while the robots perform their proper tasks, during the actual operational phase;we call this on-line evolution. In this paper we describe three principal categories of on-line evolution for developing robot controllers (encapsulated, distributed, and hybrid), present an evolutionary algorithm belonging to the first category (the (mu + 1) ON-LINE algorithm), and perform an extensive study of its behaviour. In particular, we use the Bonesa parameter tuning method to explore its parameter space. This delivers near-optimal settings for our algorithm in a number of tasks and, even more importantly, it offers profound insights into the impact of our algorithm's parameters and features. Our experimental analysis of (mu + 1) ON-LINE shows that it seems preferable to try many alternative solutions and spend little effort on refining possibly faulty assessments;that there is no single combination of parameters that performs well on all problem instances and that the most influential parameter of this algorithm-and therefore the prime candidate for a control scheme-is the evaluation length tau.
Creating artificial life forms through evolutionary robotics faces a "chicken and egg" problem: Learning to control a complex body is dominated by problems specific to its sensors and effectors, while buildi...
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Creating artificial life forms through evolutionary robotics faces a "chicken and egg" problem: Learning to control a complex body is dominated by problems specific to its sensors and effectors, while building a body that is controllable assumes the pre-existence of a brain. The idea of coevolution of bodies and brains is becoming popular, but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evolution of creatures in simulation has usually resulted in virtual entities that are not buildable, while embodied evolution in actual robotics is constrained by the slow pace of real time. The work we present takes a step in addressing the problem of body evolution by applying evolutionary techniques to the design of structures assembled out of elementary components that stick together. Evolution takes place in a simulator that computes forces and stresses and predicts stability of three-dimensional brick structures. The final printout of our program is a schematic assembly, which is then built physically. We demonstrate the functionality of this approach to robot body building with many evolved artifacts.
Under the effects of surroundings such as gravitational force, ambient temperature, and chemical substances, each animal has acquired an optimized body structure through its evolution. For example, vertebrate land ani...
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Under the effects of surroundings such as gravitational force, ambient temperature, and chemical substances, each animal has acquired an optimized body structure through its evolution. For example, vertebrate land animals have a sophisticated musculoskeletal structure including not only monoarticular muscles but also multiarticular muscles to support their weight against gravitational force. Many researchers have developed musculoskeletal robots with a biarticular muscle mechanism that enables them to execute physical tasks similar to the mimicked animal. However, the developmental process of the musculoskeletal structure has not been examined in detail in past studies. In this study, we developed a musculoskeletal robot with redundant air cylinders to investigate the developmental process of the body structure of the animal. We proposed a switching mechanism between several muscle structures called the actuator network system (ANS). In the ANS, the selection of mutually interconnected, simultaneously activated air cylinders is changed by switching the interconnections. The experimental results indicate that by changing the connection of the cylinders and the inner pressure of the connected cylinders, i.e., the strength of the connection, the response of the robot to external forces can be modified, thus demonstrating the feasibility of our approach.
Biological vision incorporates intelligent cooperation between the sensory and the motor systems, which is facilitated by the development of motor skills that help to shape visual information that is relevant to a spe...
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Biological vision incorporates intelligent cooperation between the sensory and the motor systems, which is facilitated by the development of motor skills that help to shape visual information that is relevant to a specific vision task. In this article, we seek to explore an approach to active vision inspired by biological systems, which uses limited constraints for motor strategies through progressive adaptation via an evolutionary method. This type of approach gives freedom to artificial systems in the discovery of eye-movement strategies that may be useful to solve a given vision task but are not known to us. In the experiment sections of this article, we use this type of evolutionary active vision system for more complex natural images in both two-dimensional (2D) and three-dimensional (3D) environments. To further improve the results, we experiment with the use of pre-processing the visual input with both the uniform local binary patterns (ULBP) and the histogram of oriented gradients (HOG) for classification tasks in the 2D and 3D environments. The 3D experiments include application of the active vision system to object categorisation and indoor versus outdoor environment classification. Our experiments are conducted on the iCub humanoid robot simulator platform.
In this paper, we propose a vision-based autonomous robotics navigation system, it uses a bio-inspired optical flow approach using the Hermite transform and a fuzzy logic controller, the input membership functions wer...
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ISBN:
(纸本)9783030608873;9783030608866
In this paper, we propose a vision-based autonomous robotics navigation system, it uses a bio-inspired optical flow approach using the Hermite transform and a fuzzy logic controller, the input membership functions were tuned applying a distributed evolutionary learning based on social wound treatment inspired in the Megaponera analis ant. The proposed method was implemented in a virtual robotics system using the V-REP software and in communication con MATLAB. The results show that the optimization of the input fuzzy membership functions improves the navigation behavior against an empirical tuning of them.
In this paper, we present our work on the training of robotised architectural components of intelligent buildings, focusing on how architectural components can learn to behave animalistically, according to the judgmen...
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ISBN:
(纸本)9783319229799;9783319229782
In this paper, we present our work on the training of robotised architectural components of intelligent buildings, focusing on how architectural components can learn to behave animalistically, according to the judgment of human users. Our work aims at recovering the lost contact with animals in the urban context, taking advantage of biophilic empathy. The parameters governing the robotised elements we propose are mainly qualitative (emotions and aesthetical perception), which cannot easily be described by mathematical parameters. Additionally, due to their complexity, it is often impossible - or at least impractical, to hardcode suitable controllers for such structures. Thus, we propose the use of Artificial Intelligence learning techniques, concretely evolutionary Algorithms, to allow the user to teach the robotised components how to behave in response to their resemblance to specific animal behaviors. This idea is tested on an intelligent fa, cade that learns optimal configurations according to the perception of aggressiveness and calmness.
Ensuring the integrity of a robot swarm in terms of maintaining a stable population of functioning robots over long periods of time is a mandatory prerequisite for building more complex systems that achieve user-defin...
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ISBN:
(纸本)9781450334723
Ensuring the integrity of a robot swarm in terms of maintaining a stable population of functioning robots over long periods of time is a mandatory prerequisite for building more complex systems that achieve user-defined tasks. mEDEA is an environment-driven evolutionary algorithm that provides promising results using an implicit fitness function combined with a random genome selection operator. Motivated by the need to sustain a large population with sufficient spare energy to carry out user-defined tasks in the future, we develop an explicit fitness metric providing a measure of fitness that is relative to surrounding robots and examine two methods by which it can influence spread of genomes. Experimental results in simulation find that use of the fitness-function provides significant improvements over the original algorithm;in particular, a method that influences the frequency and range of broadcasting when combined with random selection has the potential to conserve energy whilst maintaining performance, a critical factor for physical robots.
We study behavioural patterns learned by a robotic agent by means of two different control and adaptive approaches - a radial basis function neural network trained by evolutionary algorithm, and a traditional reinforc...
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ISBN:
(纸本)9783540859833
We study behavioural patterns learned by a robotic agent by means of two different control and adaptive approaches - a radial basis function neural network trained by evolutionary algorithm, and a traditional reinforcement Q-learning algorithm. In both cases, a set of rules controlling the agent is derived from the learned controllers, and these sets are compared. It is shown that both procedures lead to reasonable and compact, albeit rather different, rule sets.
The majority of robotic design approaches start with designing morphology, then designing the robot control. Even in evolutionary robotics, the morphology tends to be fixed while evolving the robot control, which cons...
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ISBN:
(纸本)9781509007998
The majority of robotic design approaches start with designing morphology, then designing the robot control. Even in evolutionary robotics, the morphology tends to be fixed while evolving the robot control, which considered insufficient since the robot control and morphology are interdependent. Moreover, both control and morphology are highly interdependent with the surrounding environment, which affects the used optimization strategies. Therefore, we propose in this paper a novel hybrid GP/GA method for designing autonomous modular robots that co-evolves the robot control and morphology and also considers the surrounding environment to allow the robot of achieving behavior specific tasks and adapting to the environmental changes. The introduced method is automatically designing feasible robots made up of various modules. Then, our new evolutionary designer is evaluated using a benchmark problem in modular robotics, which is a walking task where the robot has to move a certain distance.
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