A biologically inspired method for vision-based docking of wheeled mobile robots

We present a new control law for the problem of docking a wheeled robot to a target at a certain location with a desired heading. Recent research into insect navigation has inspired a solution which uses only one video camera. The control law is of the “behavioral” type in that all control actions are based on immediate visual information. Docking success under certain conditions is proved mathematically and simulation studies show the control law to be robust to camera intrinsic parameter errors. Experiments were performed for verification of the control law.     

FPGA implementation of a real-time biologically inspired image enhancement algorithm

This paper presents an FPGA implementation of a novel image enhancement algorithm, which compensates for the under-/over-exposed image regions, caused by the limited dynamic range of contemporary standard dynamic range image sensors. The algorithm, which is motivated by the attributes of the shunting center-surround cells of the human visual system, is implemented in Altera Stratix II GX: EP2SGX130GF1508C5 FPGA device. The proposed implementation, which is synthesized in an FPGA technology, employs reconfigurable pipeline, structured memory management, and data reuse in spatial operations, to render in real-time the huge amount of input data that the video signal comprises. It also avoids the use of computationally intensive operations, achieving the required specifications in terms of flexibility, timing, performance and visual quality. The proposed implementation allows real-time processing of color images with sizes up to 2.5 Mpixels, at frame rate of 25 fps. As a result, the architectural solution described in this work offers a low-cost implementation for automatic exposure correction in real-time video systems.

A new artificial life body: biologically inspired dynamic bipedal humanoid robots

Recently, a biologically inspired, bipedal, dynamic, humanoid robot was developed at the Artificial Life and Robotics Laboratory of Oita University. This bipedal humanoid robot is able to walk dynamically and to go up and down stairs. The central pattern generator developed produces various types of walking pattern. This robot has a pair of small CMOS color CCD cameras, a speaker, and a microphone in the head part, and will have a GPS, a portable telephone, and other sensors in the body part, so that the integration of locomotion and behavior to achieve specific demonstrations will be realized. This project develops dynamic mobility and the ability for autonomous recognition and navigation using the biological central nervous system, the brain system, and the real-time control system. Also, the design principles that demonstrate the dynamic interaction between neural and mechanical controls will be clarified. In Phase I, the platform of a small, bipedal, humanoid robot is used to develop autonomous locomotion and autonomous sensing and navigation. In Phase II of the project, an iteration on the platform design for human-size, bipedal, humanoid robots will be performed for operational testing. The development of bipedal humanoid robots that capture biological systems with unique principles and practices could dramatically increase their performance in tasks for national security needs.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

A fast biologically inspired algorithm for recurrent motion estimation

We have previously developed a neurodynamical model of motion segregation in cortical visual area V1 and MT of the dorsal stream. The model explains how motion ambiguities caused by the motion aperture problem can be solved for coherently moving objects of arbitrary size by means of cortical mechanisms. The major bottleneck in the development of a reliable biologically inspired technical system with real-time motion analysis capabilities based on this neural model is the amount of memory necessary for the representation of neural activation in velocity space. We propose a sparse coding framework for neural motion activity patterns and suggest a means by which initial activities are detected efficiently. We realize neural mechanisms such as shunting inhibition and feedback modulation in the sparse framework to implement an efficient algorithmic version of our neural model of cortical motion segregation. We demonstrate that the algorithm behaves similarly to the original neural model and is able to extract image motion from real world image sequences. Our investigation transfers a neuroscience model of cortical motion computation to achieve technologically demanding constraints such as real-time performance and hardware implementation. In addition, the proposed biologically inspired algorithm provides a tool for modeling investigations to achieve acceptable simulation time     

Solving the minimum bisection problem using a biologically inspired computational model

The traditional trend of DNA computing aims at solving computationally intractable problems. The minimum bisection problem (MBP) is a well-known NP-hard problem, which is intended to partition the vertices of a given graph into two equal halves so as to minimize the number of those edges with exactly one end in each half. Based on a biologically inspired computational model, this paper describes a novel algorithm for the minimum bisection problem, which requires a time cost and a DNA strand length that are linearly proportional to the instance size.     

Optimal design of microfluidic networks using biologically inspired principles

From the earliest of times, Man has sought to replicate ideas that have evolved naturally in plants and animals. Understanding and extracting these “natural” design strategies has opened up a whole new field of research known as biomimetics. Designs formulated using biologically inspired principles range from novel surface treatments that mimic physiological processes to geometrical optimization for improving the performance of a system. In this paper, we will show how biomimetic principles based on the laws that govern biological vascular trees can be used to design artificial microfluidic distribution systems. The study focuses specifically on microfluidic manifolds composed of constant-depth rectangular- or trapezoidal-sectioned channels, as these geometries can readily be fabricated using standard micro-fabrication techniques. We will show that it is possible to introduce a prescribed element of flow control into the system by carefully selecting the branching parameter that governs the change in channel dimension at each bifurcation.     

Development of a quadruped walking robot AiDIN-III using biologically inspired kinematic analysis

This paper presents a bio-inspired mechanism design for a quadruped walking robot. The approach is derived from the observation on the behaviors of quadruped locomotion, skeletal structure, and the study on the stability of walking based on morphological analysis. In the first, we define the design parameters such as the dimensions of the body and limbs, the center of mass position, and locomotion mechanisms based on surveys on the literatures from biologists. Then, by using the parameters, we propose an useful framework for determining the design parameters of a quadruped walking robot. For implementations, we manufacture a dog-type self-contained quadruped walking robot, named AiDIN-III (Artificial Digitigrade for Natural Environment version III) and the effectiveness of the proposed idea is validated via experimental works.     

Lump localisation through a deformation-based tactile feedback system using a biologically inspired finger sensor

The ability to localise harder areas in soft tissues is often desired during robot-assisted surgical operations. A deformation-based tactile feedback system was tested for the detection of objects within soft tissues, after being chosen over common pressure-based designs. This system uses a biologically inspired sensor that offers a new finger-like approach to tactile sensing. A tactile shape display developed from previous successful designs was used to output the sensed tactile information. Using the tactile feedback system on a mechanical teleoperated device, test subjects palpated a number of artificial tissue models to locate objects of varying stiffness. The addition of the tactile feedback system improved the detection of the objects from 64% to 98%, reduced the localisation error from 18 to 11 mm, and also decreased the time the users spent palpating the tissue from 55 to 37 s. This demonstrates that a deformation-based tactile feedback system can be used to successfully locate hard embedded objects within soft tissue, with a significant improvement over force and visual feedback alone. During testing, it was found that the users were able to more accurately locate the softest embedded objects compared to stiffer ones. Reasons for this observation are discussed.     

Global path planning of mobile robots using a memetic algorithm

In this paper, a memetic algorithm for global path planning (MAGPP) of mobile robots is proposed. MAGPP is a synergy of genetic algorithm (GA) based global path planning and a local path refinement. Particularly, candidate path solutions are represented as GA individuals and evolved with evolutionary operators. In each GA generation, the local path refinement is applied to the GA individuals to rectify and improve the paths encoded. MAGPP is characterised by a flexible path encoding scheme, which is introduced to encode the obstacles bypassed by a path. Both path length and smoothness are considered as fitness evaluation criteria. MAGPP is tested on simulated maps and compared with other counterpart algorithms. The experimental results demonstrate the efficiency of MAGPP and it is shown to obtain better solutions than the other compared algorithms.     

Image recommendation based on a novel biologically inspired hierarchical model

Image recommendation has become an increasingly relevant problem recently, since strong demand to quickly find interested images from vast amounts of image library. We describe a biologically inspired hierarchical model for image recommendation. The biologically inspired model (BIM) for invariant feature representation has attracted widespread attention, which approximately follows the organization of cortex visuel. BIM is a computation architecture with four layers. With the image data size increases, the four-layer framework is prone to be overfitting, which limits its application. To address this issue, we propose a biologically inspired hierarchical model (BIHM) for feature representation, which adds two more discriminative layers upon the conventional four-layer framework. In contrast to the conventional BIM that mimics the inferior temporal cortex, which corresponds to the low level feature, the proposed BIHM adds two more layers upon the conventional framework to simulate inferotemporal cortex, exploring higher level feature invariance and selectivity. Furthermore, we firstly utilize the BIHM in the image recommendation. To demonstrate the effectiveness of proposed model, we use it to image classification and retrieval tasks and perform experiments on CalTech5, Imagenet and CalTech256 datasets. The experiment results show that BIHM exhibits better performance than the conventional model in the tasks and is very comparable to existing architectures.     

Bioinspired algorithm for area surveillance using autonomous robots

Territorial surveillance plays a constantly increasing role in security. However, completely automatic surveillance using autonomous robots is hard to implement and maintain. Current methods described in literature propose systems that include direct communication of the robots or the use of a centralised system to coordinate the robots. These systems are prone to equipment failure and/or malicious attacks to the centralised system. In this paper, we propose a bioinspired algorithm that allows indirect communication between the robots that are considered minimally equipped. This is achieved by applying a parallel and distributed technique inspired by the emergent behaviour of social insects, namely ant colonies. In particular, the development of a collective memory for robots and areas covered is achieved subsequently through self-organisation of the autonomous robots to a continuous dynamic coverage of the test space. The algorithm is shown to have a robust behaviour and competitive performance. Several simulations run for various space sizes, different number of robots, different pheromone evaporation rates as well various percentages of space covered by obstacles. In all cases the efficacy of the proposed algorithm has been successfully proven when compared with other well known techniques.     

Morphogenic inspired self-organization control of unmanned aerial swarm robots

A central issue in distributed formation of swarm is enabling agents with only a local view of their environment to take actions that advance global system objectives (emergence of collective behavior). This paper describes a bio-inspired control algorithm using pheromone for coordinating a swarm of identical flying agents to spatially self-organize into arbitrary shapes using local communication maintaining a certain level of density. Development of morphological features such as the fine formation of particles, hollow particles, cracks in particles, and the evolution of the structure are used as illustrative examples. Distributed formation of swarming with no coordinated agreement or positioning information is an interesting research area because the global behaviors must emerge from many diverse local interactions. Different from most existing distributed control, the proposed approach considers the topological structure of the organization, supports dynamic reconfiguration and self-organization. This paper presents the experimental results on simulating in the forming of arbitrary shape, and also describes a analytic aspect of our model comparing with behavioral performances.

     

A biologically inspired active compliant joint using local positive velocity feedback (LPVF).

Starting from studies which revealed that positive feedback is found in the control system for walking in arthropods, we have constructed a new positive feedback driven joint that can be used for solving compliant motion tasks. We propose two different joint constructions each of which shows passive compliance. Based on these joints we introduce three different local positive velocity feedback (LPVF) controllers and discuss their properties in the context of motion generation in closed kinematic chains. The third circuit named undelayed dLPVF is used for the control of a compliant planar manipulator which turns a crank. Our concept is of highly decentralized nature and follows the idea of embodiment. In our case this means that a process which is controlled by LPVF controllers reveals its nature when the controllers interact with this process.     

Multiobjective optimization using an immunodominance and clonal selection inspired algorithm

Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm （IDCMA）. IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals （antibodies） are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant （active） antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-Ⅱ, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.     

A taxonomy of biologically inspired research in computer networking

The natural world is enormous, dynamic, incredibly diverse, and highly complex. Despite the inherent challenges of surviving in such a world, biological organisms evolve, self-organize, self-repair, navigate, and flourish. Generally, they do so with only local knowledge and without any centralized control. Our computer networks are increasingly facing similar challenges as they grow larger in size, but are yet to be able to achieve the same level of robustness and adaptability. Many research efforts have recognized these parallels, and wondered if there are some lessons to be learned from biological systems. As a result, biologically inspired research in computer networking is a quickly growing field. This article begins by exploring why biology and computer network research are such a natural match. We then present a broad overview of biologically inspired research, grouped by topic, and classified in two ways: by the biological field that inspired each topic, and by the area of networking in which the topic lies. In each case, we elucidate how biological concepts have been most successfully applied. In aggregate, we conclude that research efforts are most successful when they separate biological design from biological implementation – that is to say, when they extract the pertinent principles from the former without imposing the limitations of the latter.     

On biologically inspired predictions of the global financial crisis

Early-warning models provide means for ex ante identification of elevated risks that may lead to a financial crisis. This paper taps into the early-warning literature by introducing biologically inspired models for predicting systemic financial crises. We create three models: a conventional statistical model, a back-propagation neural network (NN) and a neuro-genetic (NG) model that uses a genetic algorithm for choosing the optimal NN configuration. The models are calibrated and evaluated in terms of usefulness for policymakers that incorporates preferences between type I and type II errors. Generally, model evaluations show that biologically inspired models outperform the statistical model. NG models are, however, shown not only to provide largest usefulness for policymakers as an early-warning model, but also in form of decreased expertise and labor needed for, and uncertainty caused by, manual calibration of an NN. For better generalization of data-driven models, we also advocate adopting to the early-warning literature a training scheme that includes validation data.     

Evaluation of a selective distributed discovery strategy in a fully decentralized biologically inspired environment

The increased demand and complexity of services operating within open distributed environments has emphasized the need for systems that are adaptive, self-organizing and more robust. In order to address these issues some agent oriented approaches have adopted ideas from natural systems as possible solutions. The introduction of biological properties, especially birth and death of agents as expected events, generates an extremely dynamic environment where it is difficult to maintain the overall connectivity of the overlay network and facilitate efficient discovery processes. In this paper we evaluate the performance of a selective discovery mechanism in a distributed bio-inspired multi-agent community through a simulation study. The primary focus of the study is on the impacts which death and (sexual/asexual) reproduction events have on the effectiveness of the discovery process in different overlay networks.     

Processing of chemical sensor arrays with a biologically inspired model of olfactory coding

This paper presents a computational model for chemical sensor arrays inspired by the first two stages in the olfactory pathway: distributed coding with olfactory receptor neurons and chemotopic convergence onto glomerular units. We propose a monotonic concentration-response model that maps conventional sensor-array inputs into a distributed activation pattern across a large population of neuroreceptors. Projection onto glomerular units in the olfactory bulb is then simulated with a self-organizing model of chemotopic convergence. The pattern recognition performance of the model is characterized using a database of odor patterns from an array of temperature modulated chemical sensors. The chemotopic code achieved by the proposed model is shown to improve the signal-to-noise ratio available at the sensor inputs while being consistent with results from neurobiology.