Complex Dynamic Environments Research Articles

Seeking a path through the crowd: Robot navigation in unknown dynamic environments with moving obstacles based on an integrated environment representation

We present a novel algorithm for collision free navigation of a non-holonomic robot in unknown complex dynamic environments with moving obstacles. Our approach is based on an integrated representation of the information about the environment which does not require to separate obstacles and approximate their shapes by discs or polygons and is very easy to obtain in practice. Moreover, the proposed algorithm does not require any information on the obstacles’ velocities. Under our navigation algorithm, the robot efficiently seeks a short path through the crowd of moving or steady obstacles. A mathematically rigorous analysis of the proposed approach is provided. The performance of the algorithm is demonstrated via experiments with a real robot and extensive computer simulations.

A network analysis of Sibiu County, Romania

Network science methods have proved to be able to provide useful insights from both a theoretical and a practical point of view in that they can better inform governance policies in complex dynamic environments. The tourism research community has provided an increasing number of works that analyse destinations from a network science perspective. However, most of the studies refer to relatively small samples of actors and linkages. With this note we provide a full network study, although at a preliminary stage, that reports a complete analysis of a Romanian destination (Sibiu). Our intention is to increase the set of similar studies with the aim of supporting the investigations in structural and dynamical characteristics of tourism destinations.

Problems and Challenges in Control Theory under Complex Dynamical Network Environments

Problems and Challenges in Control Theory under Complex Dynamical Network Environments

Generation of New Robot Skills from Learned Skills

Future humanoid robots, working beside humans in complex dynamic environments, would be required to perform a wide repertoire of task. To this end traditional methods for deriving a control policy won't succeed, learning approaches are fundamental. Learning from demonstrations (LfD) techniques have appear as a means for developing intuitive control methods and generalize a skill based on a set of demonstrations. However, learning appropriate skills for every conceivable scenario the robot may encounter would still be a daunting undertaking. In this work we propose a framework for the generation and adaptation of new robot skills from previously learned skill models. Using previously learned models of a robot skill, and knowledge of the current task, the models of a skill is adapted to generate a new task by a merger or combination operation over the given robot skill models.

Routing Optimization of Intelligent Vehicle in Automated Warehouse

Routing optimization is a key technology in the intelligent warehouse logistics. In order to get an optimal route for warehouse intelligent vehicle, routing optimization in complex global dynamic environment is studied. A new evolutionary ant colony algorithm based on RFID and knowledge-refinement is proposed. The new algorithm gets environmental information timely through the RFID technology and updates the environment map at the same time. It adopts elite ant kept, fallback, and pheromones limitation adjustment strategy. The current optimal route in population space is optimized based on experiential knowledge. The experimental results show that the new algorithm has higher convergence speed and can jump out the U-type or V-type obstacle traps easily. It can also find the global optimal route or approximate optimal one with higher probability in the complex dynamic environment. The new algorithm is proved feasible and effective by simulation results.

Inner-Learning Mechanism Based Control Scheme for Manipulator with Multitasking and Changing Load

With the rapid development of robot technology and its application, manipulators may face complex tasks and dynamic environments in the coming future, which leads to two challenges of control: multitasking and changing load. In this paper, a novel multicontroller strategy is presented to meet such challenges. The presented controller is composed of three parts: subcontrollers, inner-learning mechanism, and switching rules. Each subcontroller is designed with self-learning skills to fit the changing load under a special task. When a new task comes, switching rule reselects the most suitable subcontroller as the working controller to handle current task instead of the older one. Inner-learning mechanism makes the subcontrollers learn from the working controller when load changes so that the switching action causes smaller tracking error than the traditional switch controller. The results of the simulation experiments on two-degree manipulator show the proposed method effect.

Network Governance: Theory Development and Examples

Transaction Byte Analysis (TBA) is developed as a methodology to research firms governed by more than one board. TBA grounds social research in cybernetic laws to create a science of governance. Any co-ordination between social creatures requires the creation, transmission, reception and internal processing of signals measured in bytes. TBA fills a methodological gap for comparing control and communications networks within and/or between individuals and organizations. TBA also explicates why evolution has created contrary instincts in social animals to sustain and regulate themselves in unknowable, dynamic complex environments. TBA reveals why the neurological and/or physiological limits of humans to process bytes requires complex organizations to decompose decision-making labor into a network of control centers described as a “compound board.” A dominant shareholder represents a control center so compound boards govern most publicly traded firms around the world. Examples of large firms with network governance are used to reveal how compound boards allows contrary views to arise to improve operations, self-regulation, self-governance and sustainability. TBA reveals why regulation of complex firms by a single board and/or government regulators cannot be reliably achieved without network governance to allow stakeholders to become co-regulators with contrary viewpoints. Digital communications facilitate organizational research using TBA.

Nanomedicine metaphors: From war to care. Emergence of an oecological approach

• War metaphors have been widely used to describe the targeting ability and therapeutic potentials of multifunctional nanodevices against cancer. • This militaristic view overlooks the interactions of nanoparticles with the complex dynamical environment of the body. • Nanodevices should be defined by their interactions with the milieu, rather than as individual remotely-controlled entities. • Nanomedicine may take advantage of the milieu for designing new strategies of care. • The emerging oecological approach, focusing on nanoparticle relations with their environment, may prove more promising than the magic bullet concept. Images such as ‘therapeutic missile’ are commonly used to present targeted drug delivery devices. The ballistic metaphor, reminiscent of Paul Ehrlich's ‘magic bullet’, has raised great expectations. Accordingly, chemists, physicists and engineers have worked hard to design smart ‘missiles’ delivering their load of drug to the target. While paying attention to the equipment of the nanodevice for the transport and molecular recognition for the delivery, they have to face the challenges posed by the messy environment of the body. We question the relevance of the missile metaphor, and suggest that an alternative oecological metaphor would be more appropriate. Such an approach, focused on interactions between the nanovehicle and the complex, versatile, heterogeneous biological milieu, seems more heuristic and promising.

Randomized path planning with preferences in highly complex dynamic environments

SUMMARYIn this paper we consider the problem of planning paths for articulated bodies operating in workplaces containing obstacles and regions with preferences expressed as degrees of desirability. Degrees of desirability could specify danger zones and desire zones. A planned path should not collide with the obstacles and should maximize the degrees of desirability. Region desirability can also convey search-control strategies guiding the exploration of the search space. To handle desirability specifications, we introduce the notion of flexible probabilistic roadmap (flexible PRM) as an extension of the traditional PRM. Each edge in a flexible PRM is assigned a desirability degree. We show that flexible PRM planning can be achieved very efficiently with a simple sampling strategy of the configuration space defined as a trade-off between a traditional sampling oriented toward coverage of the configuration space and a heuristic optimization of the path desirability degree. For path planning problems in dynamic environments, where obstacles and region desirability can change in real time, we use dynamic and anytime search exploration strategies. The dynamic strategy allows the planner to replan efficiently by exploiting results from previous planning phases. The anytime strategy starts with a quickly computed path with a potentially low desirability degree which is then incrementally improved depending on the available planning time.

Automated negotiation in open and distributed environments

Automated negotiation is one of the most common approaches used to make decisions and manage disputes between computational entities leading them to optimal agreements. Many existing works tackle single-issue negotiations and the negotiation environment is assumed to be static so that the agents can make decisions based solely on the proposals of the counterparts and their own fixed parameters. Most real-world scenarios, however, involve complex domains and dynamic environments. In such cases, it is no longer sufficient to consider negotiation as an isolated activity in a static environment. Therefore, a more general framework for automated negotiation is needed in which the negotiation agents can be very flexible and adaptive. In this paper, we describe a generic framework for automated negotiation, which captures descriptively the social dynamics of the negotiation process. The proposed framework enables the agents to behave responsively to the changes in the environment. Their strategies can adapt as the conditions outside of the negotiation change to ensure that their decisions remain rational. And the agents are proactive and responsive by searching for options, which are outside of the negotiation and which may improve their outcomes. The key ideas and the overall system architecture together with a specific negotiation instance in a basic bilateral setting are described, along with two illustrative examples. The first example is in the context of e-commerce, and the second example is an application scenario of service level agreement negotiation in service computing. We also describe a prototypical implementation of the proposed negotiation framework.

Problems and Challenges in Control Theory under Complex Dynamical Network Environments

This article addresses some new problems and challenges faced by the conventional control theory under complex dynamical network environments. After introducing the network science and engineering background, it discusses some research issues regarding pinning control of complex dynamical networks, controllability of directed networks, as well as “network of networks” and its modeling and control.

A Neurally Controlled Computer Game Avatar With Humanlike Behavior

This paper describes the NeuroBot system, which uses a global workspace architecture, implemented in spiking neurons, to control an avatar within the Unreal Tournament 2004 (UT2004) computer game. This system is designed to display humanlike behavior within UT2004, which provides a good environment for comparing human and embodied AI behavior without the cost and difficulty of full humanoid robots. Using a biologically inspired approach, the architecture is loosely based on theories about the high-level control circuits in the brain, and it is the first neural implementation of a global workspace that has been embodied in a complex dynamic real-time environment. NeuroBot's humanlike behavior was tested by competing in the 2011 BotPrize competition, in which human judges play UT2004 and rate the humanness of other avatars that are controlled by a human or a bot. NeuroBot came a close second, achieving a humanness rating of 36%, while the most human human reached 67%. We also developed a humanness metric that combines a number of statistical measures of an avatar's behavior into a single number. In our experiments with this metric, NeuroBot was rated as 33% human, and the most human human achieved 73%.

Towards Believable Resource Gathering Behaviours in Real-time Strategy Games with a Memetic Ant Colony System

In this paper, the resource gathering problem in real-time strategy (RTS) games, is modeled as a path-finding problem where game agents responsible for gathering resources, also known as harvesters, are only equipped with the knowledge of its immediate sur- roundings and must gather knowledge about the dynamics of the navigation graph that it resides on by sharing information and cooperating with other agents in the game environment. This paper proposed the conceptual modeling of a memetic ant colony system (MACS) for believable resource gathering in RTS games. In the proposed MACS, the harvester's path-finding and resource gathering knowledge captured are extracted and represented as memes, which are internally encoded as state transition rules (mem- otype), and externally expressed as ant pheromone on the graph edge (sociotype). Through the inter-play between the memetic evolution and ant colony, harvesters as memetic automatons spawned from an ant colony are able to acquire increasing level of capability in exploring complex dynamic game environment and gathering resources in an adaptive manner, producing consistent and impressive resource gathering behaviors.

A novel system for studying mechanical strain waveform-dependent responses in vascular smooth muscle cells

While many studies have examined the effects mechanical forces on vSMCs, there is a limited understanding of how the different arterial strain waveforms that occur in disease and different vascular beds alter vSMC mechanotransduction and phenotype. Here, we present a novel system for applying complex, time-varying strain waveforms to cultured cells and use this system to understand how these waveforms can alter vSMC phenotype and signaling. We have developed a highly adaptable cell culture system that allows the application of mechanical strain to cells in culture and can reproduce the complex dynamic mechanical environment experienced by arterial cells in the body. Using this system, we examined whether the type of applied strain waveform altered phenotypic modulation of vSMCs by mechanical forces. Cells exposed to the brachial waveform had increased phosphorylation of AKT, EGR-1, c-Fos expression and cytoskeletal remodeling in comparison to cells treated with the aortic waveform. In addition, vSMCs exposed to physiological waveforms had adopted a more differentiated phenotype in comparison to those treated with static or sinusoidal cyclic strain, with increased expression of vSMC markers desmin, calponin and SM-22 as well as increased expression of regulatory miRNAs including miR-143, -145 and -221. Taken together, our studies demonstrate the development of a novel system for applying complex, time-varying mechanical forces to cells in culture. In addition, we have shown that physiological strain waveforms have powerful effects on vSMC phenotype.

Understanding the extracellular matrix

Tissue engineering deals with the growth and regeneration techniques of connective tissues or organs from a combination of cells and a scaffold to produce a functional organ. The engineering techniques therefore begin with an implantation of artificial materials, providing a proper environment for cells or tissues to be grown and functionalized. The scaffolds are often formulated with biodegradable polymer, extracellular matrix, and growth factors, serving as a skeleton to be filled up with cells, and eventually grow into three-dimensional tissues. With the importance of the intercellular connection in the field of tissue engineering, considerable efforts have been made to design an artificial extracellular matrix in vitro: to achieve a three-dimensional network in its architecture and effective ingredients for its chemical composition. Another important aspect, however, that I would like to emphasize, is understanding how sophisticated the interaction is between the extracellular matrix network and cytoskeletal networks in cells. The cytoskeletal network in most eukaryotic cells is a combination of polymeric networks made of actin filaments, microtubules, and intermediate filaments. These networks play an essential role in determining not only the shape and mechanics of a cell, but more importantly, cell motility. In particular, an orchestrated movement of cells in particular directions to specific locations is essentially required during any tissue development in nature. These cellular migrations are often explained by a cytoskeletal model; the spontaneous cycling of polymerization and depolymerization of cytoskeletal filaments leads the cellular motility at the cell's front, where a tight interface with the extracellular network occurs. The concept of symmetry breaking is helpful for understanding the cytoskeletal mechanisms of a cell; for example, individual actin filaments and microtubules are structurally and kinetically polarized to generate pushing forces and pulling forces, which are indicative of the asymmetric nature of the filament organization in cytoskeletal networks. The mechanical properties of the external network are known to influence the polarized organization of cytoskeletal fibers in the target cells. Therefore, if an optimized extracellular matrix-based scaffold, from a simple supporting scaffold to a more complex dynamic bioactive environment, is to be provided, then one has to understand that the internal cytoskeletal fibers may respond differently to the given extracellular matrices, not only biologically but also physically. Hence, the effectiveness of both the cellular growth (chemical response) and cellular migration (physical response) should be reassessed, and scrutinized. Just the fact that researchers have been able to understand tissue growth and regeneration at this level of detail is itself impressive. What is more remarkable is that scientists are now developing technology to shape and even replicate these processes through the design of artificial extracellular matrices and the associated techniques and materials to implement their use. As these technologies move from the laboratory to clinical practice, they are bound to shape the field of periodontal and implant science in the future and have a positive impact on the quality of life of our patients. We should welcome these developments and encourage the ongoing growth of cell and tissue engineering.

A Comparison of Visualization and Command-Based Decision Aiding in a Simulated Aircraft Departure Sequencing Task

Three versions of a prototype ground controller interface were tested for their effect on aircraft departure sequencing in a simulation of Dallas/Forth-Worth International Airport. Two versions featured automated decision aids and a third served as a baseline with no decision aid. The two decision aids performed fundamentally different functions, with a Temporal Constraint Visualization (TCV) providing a visualization of spatiotemporal constraints on the departure sequence and a Timeline display providing release sequences derived by an optimization algorithm. Results indicate that participants in the TCV condition had more efficient departure sequences than the Baseline and Timeline conditions. No significant differences were found between conditions for timeliness of departures or handling of arrival aircraft. These results indicate that, through the use of appropriate decision aids, task performance in complex dynamic environments can be improved with humans retaining full decision making control. Additional research is warranted to investigate situation awareness and failure mode performance using these decision aid types.

Anatomy of a Decision Trap in Complex New Product Development Projects

We conducted a longitudinal process study of one firm's failed attempt to develop a new product. Our extensive data analysis suggests that teams in complex dynamic environments characterized by delays are subject to multiple filters that blur their perception of actual project performance. Consequently, teams do not realize their projects are in trouble and repeatedly fall into a decision trap in which they stretch current project stages at the expense of future stages. This slowly and gradually reduces the likelihood of project success. However, because of the information filters, teams fail to notice what is happening until it is too late

Behaviour informatics: data integration for greater understanding of human and animal behaviour

Here we propose the establishment of a new addition to the current efforts in neuro-informatics aimed at an integrated study of the manly levels of human and animal behaviour. This new platform, which we call Behaviour Informatics, will facilitate the accumulation and sharing of behavioural data, and related analysis tools. Various Neuroinformatics platforms have in recent years been established to facilitate data sharing and integration for digital atlases of brain structure and anatomy, for fMRI and electrophysiology data, for modeling of spiking neural networks and many more. These Neuroinformatics efforts promise to provide a more coherent picture of the complete brain architecture. Similar efforts in behavioural studies would facilitate a more complete understanding of the relation between behavioural traits at the micro and macro levels and their dependence on environmental conditions. In addition to the pooling and standardization of data from behavioural experiments, another pillar of behaviour informatics could be a concerted effort to use virtual environments, like massively multi player games, to gather information on human behaviour in complex dynamic (social) environments with relatively minimal effort. In this poster I will present the goals and general framework that will form the basis of the roadmap for the creation of a Behaviour Informatics platform that will be discussed in the workshop: Behaviour Informatics: data bases, data mining and virtual world behaviour.

Dynamic simulation studies and experiments on rubber structures used in rail vehicles

Rail vehicles operate in highly complex dynamic environments. Rubber-to-metal bonded springs are used as both primary and secondary suspensions as well as engine installations in order to minimize vibration levels. The effect of rubber hysteresis is usually studied using viscoelastic models; however, this paper proposes an approach in which Rayleigh damping coefficients are introduced to model the rubber hysteresis. Simulations of the dynamic response of a rectangular rubber spring are performed and it is found that the natural frequencies of the rubber suspensions and engine installations on a railway vehicle show little change under both the tare and loaded conditions in service. The steady state dynamic response to a harmonic input in the frequency range between 1 and 1000 Hz is calculated and subsequently validated against experimental results. In addition the effect of a sudden impact, created by track irregularities, on the rubber suspension and engine installation is analysed. The proposed methodology may be used to guide the design of the rubber structures used in railway vehicles.

Effects of Selective Adaptation on Coding Sugar and Salt Tastes in Mixtures

Little is known about coding of taste mixtures in complex dynamic stimulus environments. A protocol developed for odor stimuli was used to test whether rapid selective adaptation extracted sugar and salt component tastes from mixtures as it did component odors. Seventeen human subjects identified taste components of "salt + sugar" mixtures. In 4 sessions, 16 adapt-test stimulus pairs were presented as atomized, 150-μL "taste puffs" to the tongue tip to simulate odor sniffs. Stimuli were NaCl, sucrose, "NaCl + sucrose," and water. The sugar was 98% identified but the suppressed salt 65% identified in unadapted mixtures of 2 concentrations of NaCl, 0.1 or 0.05 M, and sucrose at 3 times those concentrations, 0.3 or 0.15 M. Rapid selective adaptation decreased identification of sugar and salt preadapted ambient components to 35%, well below the 74% self-adapted level, despite variation in stimulus concentration and adapting time (<5 or >10 s). The 96% identification of sugar and salt extra mixture components was as certain as identification of single compounds. The results revealed that salt-sugar mixture suppression, dependent on relative mixture-component concentration, was mutual. Furthermore, like odors, stronger and recent tastes are emphasized in dynamic experimental conditions replicating natural situations.