Chaotic dynamics of a behavior-based miniature mobile robot: effects of environment and control structure

Complexity Measures: Open Questions and Novel Opportunities in the Automatic Design and Analysis of Robot Swarms

Complexity measures and information theory metrics in general have recently been attracting the interest of multi-agent and robotics communities, owing to their capability of capturing relevant features of robot behaviors, while abstracting from implementation details. We believe that theories and tools from complex systems science and information theory may be fruitfully applied in the near future to support the automatic design of robot swarms and the analysis of their dynamics. In this paper we discuss opportunities and open questions in this scenario.

Applications of Chaotic Dynamics in Robotics

This article presents a summary of applications of chaos and fractals in robotics. Firstly, basic concepts of deterministic chaos and fractals are discussed. Then, fundamental tools of chaos theory used for identifying and quantifying chaotic dynamics will be shared. Principal applications of chaos and fractal structures in robotics research, such as chaotic mobile robots, chaotic behaviour exhibited by mobile robots interacting with the environment, chaotic optimization algorithms, chaotic dynamics in bipedal locomotion and fractal mechanisms in modular robots will be presented. A brief survey is reported and an analysis of the reviewed publications is also presented.

Learning from adaptive neural network output feedback control of a unicycle-type mobile robot

This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs.

Modulation of rat behaviour by using a rat-like robot

In this paper, we study the response of a rat to a rat-like robot capable of generating different types of behaviour (stressful, friendly, neutral). Experiments are conducted in an open-field where a rat-like robot called WR-4 is put together with live rats. The activity level of each rat subject is evaluated by scoring its locomotor activity and frequencies of performing rearing (rising up on its hind limbs) and body grooming (body cuddling and head curling) actions, whereas the degree of preference of that is indicated by the robot-rat distance and the frequency of contacting WR-4. The moving speed and behaviour of WR-4 are controlled in real-time based on the feedback from rat motion. The activity level and degree of preference of rats for each experimental condition are analysed and compared to understand the influence of robot behaviour. The results of this study show that the activity level and degree of preference of the rat decrease when exposed to a stressful robot, and increase when the robot exhibit friendly behaviour, suggesting that a rat-like robot can modulate rat behaviour in a controllable, predictable way.

An Improved Chaotic Motion Path Planner for Autonomous Mobile Robots Based on a Logistic Map

This paper presents a chaotic motion path planner based on a Logistic Map (SCLCP) for an autonomous mobile robot to cover an unknown terrain randomly, namely entirely, unpredictably and evenly. The path planner has been improved by arcsine and arccosine transformation. A motion path planner based only on the Logistic Chaotic Map (LCP) can show chaotic behaviour, which possesses the chaotic characteristics of topological transitivity and unpredictability, but lacks better evenness. Therefore, the arcsine and arccosine transformations are used to enhance the randomness of LCP. The randomness of the followed path planner, LCP, the improved path planner SCLCP and the commonly used Random Path Planner (RP) are discussed and compared under different sets of initial conditions and different iteration rounds. Simulation results confirm that a better evenness index of SCLCP can be obtained with regard to previous works.

A systematic analysis of the Braitenberg vehicle 2b for point-like stimulus sources

Braitenberg vehicles have been used experimentally for decades in robotics with limited empirical understanding. This paper presents the first mathematical model of the vehicle 2b, displaying so-called aggression behaviour, and analyses the possible trajectories for point-like smooth stimulus sources. This sensory-motor steering control mechanism is used to implement biologically grounded target approach, target-seeking or obstacle-avoidance behaviour. However, the analysis of the resulting model reveals that complex and unexpected trajectories can result even for point-like stimuli. We also prove how the implementation of the controller and the vehicle morphology interact to affect the behaviour of the vehicle. This work provides a better understanding of Braitenberg vehicle 2b, explains experimental results and paves the way for a formally grounded application on robotics as well as for a new way of understanding target seeking in biology.

Neural Network Analysis of Ex-vivo Expansion of Hematopoietic Stem Cells

The shortage of hematopoietic stem cells (HSCs) greatly limits their widespread clinical applications. Few studies however, investigated the relationship between the cellular expansion and the influencing factors although wide variety results of the ex-vivo expansion of HSCs existed in literature. Here, a back-propagation (BP) neural network model was employed to evaluate the ex-vivo expansions of nuclear cells (NCs), CD34(+) cells, and colony-forming units (CFU-Cs), where the output was the cellular expansion folds and the inputs include inoculated density, cytokines, resources, serum, stroma, culture time, and bioreactor types. Around 124, 86, and 90 samples were used to train the neural network for the expansion evaluations of NCs, CD34(+ )cells, and CFU-Cs, respectively, while 17, 14, and 10 samples were applied to predict respectively. The results show that for the training of network, the interval accuracy of the expansion folds for the different cells is 85.5, 86.1, and 86.7%, respectively, while the truth-value accuracy is still up to 59.7, 50.0, and 62.2%, respectively within a relative error (RE) of +/-20%. For the prediction of network, the interval accuracy can be up to 82.4, 71.4, and 70%, respectively, while the truth-value accuracy is only 29.4, 14.3, and 50.0%, respectively (RE = +/-20%). Moreover, six verification experiments were carried out based on our interval predicted values and the results proved that the five group predicted conditions lead to the correct expansion of the HSCs with the accuracy more than 80%. Considering the complexity of HSC expansion and complicated wide range of the experimental data, such relatively high interval accuracy for training and prediction as well as verification are satisfied. Therefore this nonlinear modeling makes it possible to describe quantitatively the effects of the culture conditions on the HSC expansion and to predict the optimal culture conditions for higher ex-vivo expansion of HSCs.