A new architecture for controlling mobile robots is described. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up of asynchronous modules that communicate over low-bandwidth channels. Each module is an instance of a fairly simple computational machine. Higher-level layers can subsume the roles of lower levels by suppressing their outputs. However, lower levels continue to function as higher levels are added. The result is a robust and flexible robot control system. The system has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms. Eventually it is intended to control a robot that wanders the office areas of our laboratory, building maps of its surroundings using an onboard arm to perform simple tasks.

A robust layered control system for a mobile robot

/pdf/a-robust-layered-control-system-for-a-mobile-robot-23y2pwsdw0.pdf

An Introduction to MultiAgent Systems.

A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

/pdf/metaheuristics-from-design-to-implementation-1vqdfv8nk9.pdf

Metaheuristics: From Design to Implementation

A sonar-based mapping and navigation system developed for an autonomous mobile robot operating in unknown and unstructured environments is described. The system uses sonar range data to build a multileveled description of the robot's surroundings. Sonar readings are interpreted using probability profiles to determine empty and occupied areas. Range measurements from multiple points of view are integrated into a sensor-level sonar map, using a robust method that combines the sensor information in such a way as to cope with uncertainties and errors in the data. The resulting two-dimensional maps are used for path planning and navigation. From these sonar maps, multiple representations are developed for various kinds of problem-solving activities. Several dimensions of representation are defined: the abstraction axis, the geographical axis, and the resolution axis. The sonar mapping procedures have been implemented as part of an autonomous mobile robot navigation system called Dolphin. The major modules of this system are described and related to the various mapping representations used. Results from actual runs are presented, and further research is mentioned. The system is also situated within the wider context of developing an advanced software architecture for autonomous mobile robots.

Sonar-based real-world mapping and navigation

Large blackouts in North America: Historical trends and policy implications

Experiments over a variety of optimization problems indicate that scale-effective convergence is an emergent behavior of certain computer-based agents, provided these agents are organized into an asynchronous team (A-Team). An A-Team is a problem-solving architecture in which the agents are autonomous and cooperate by modifying one another‘s trial solutions. These solutions circulate continually. Convergence is said to occur if and when a persistent solution appears. Convergence is said to be scale-effective if the quality of the persistent solution increases with the number of agents, and the speed of its appearance increases with the number of computers. This paper uses a traveling salesman problem to illustrate scale-effective behavior and develops Markov models that explain its occurrence in A-Teams, particularly, how autonomous agents, without strategic planning or centralized coordination, can converge to solutions of arbitrarily high quality. The models also perdict two properties that remain to be experimentally confirmed:

• construction and destruction are dual processes. In other words, adept destruction can compensate for inept construction in an A-Team, and vice-versa. (Construction refers to the process of creating or changing solutions, destruction, to the process of erasing solutions.)

• solution quality is independent of agent-phylum. In other words, A-Teams provide an organizational framework in which humans and autonomous mechanical agents can cooperate effectively.

/pdf/asynchronous-teams-cooperation-schemes-for-autonomous-agents-1dr9ynpx8m.pdf

Asynchronous Teams: Cooperation Schemes for Autonomous Agents

Despite efforts to mitigate blackout risk, the data available from the North American Electric Reliability Council (NERC) for 1984-2006 indicate that the frequency of large blackouts in the United States is not decreasing. This paper describes the data and methods used to come to this conclusion and several other patterns that appear in the data. These patterns have important implications for those who make investment and policy decisions in the electricity industry. Several example calculations show how these patterns can significantly affect the decision-making process.

Trends in the history of large blackouts in the United States

We present a computer algorithm for the automated assignment of polypeptide backbone and13Cβ resonances of a protein of known primary sequence. Input to the algorithm consistsof cross peaks from several 3D NMR experiments: HNCA, HN(CA)CO, HN(CA)HA,HNCACB, COCAH, HCA(CO)N, HNCO, HN(CO)CA, HN(COCA)HA, and CBCA(CO)NH.Data from these experiments performed on glutamine-binding protein are analyzed statisticallyusing Bayes' theorem to yield objective probability scoring functions for matching chemicalshifts. Such scoring is used in the first stage of the algorithm to combine cross peaks fromthe first five experiments to form intraresidue segments of chemical shifts{Ni,HiN,Ciα,Ciβ,C′i}, while the latter five are combined into interresiduesegments {Ciα,Ciβ,C′i,Ni+1,HNi+1}. Given a tentative assignment of segments,the second stage of the procedure calculates probability scores based on the likelihood ofmatching the chemical shifts of each segment with (i) overlapping segments; and (ii) chemicalshift distributions of the underlying amino acid type (and secondary structure, if known). Thisjoint probability is maximized by rearranging segments using a simulated annealing program,optimized for efficiency. The automated assignment program was tested using CBCANH andCBCA(CO)NH cross peaks of the two previously assigned proteins, calmodulin and CheA.The agreement between the results of our method and the published assignments wasexcellent. Our algorithm was also applied to the observed cross peaks of glutamine-bindingprotein of Escherichia coli, yielding an assignment in excellent agreement with that obtainedby time-consuming, manual methods. The chemical shift assignment procedure described hereshould be most useful for NMR studies of large proteins, which are now feasible with the useof pulsed-field gradients and random partial deuteration of samples.

Automated probabilistic method for assigning backbone resonances of (13C,15N)-labeled proteins.

A Multi-Algorithm Problem (MAP) is a problem with many approximate algorithms available to solve it. Examples of MAPs are most of the combinatorial optimization problems and the multi-criterion problems with conflicting objectives.
Since a MAP has no single algorithm able to solve it optimally in reasonable time, and each available algorithm for a MAP has its own approach to solving a problem, there exists the possibility of combining these algorithms in order to take advantage of their strengths. This thesis demonstrates that by using an Asynchronous Team, a software organization characterized by cyclic, iterative data flow and autonomous agents which communicate asynchronously through shared memories, one can synergistically combine many algorithms in order to reach better results than each algorithm can do by itself.
As an example of a MAP, we chose the Euclidean version of the Traveling Salesman Problem (TSP), which is a difficult combinatorial problem and has many heuristic algorithms that only provide approximate solutions for it. Asynchronous Teams with a few algorithms were able to reach optimal solutions for all the TSP instances tackled whereas, individual algorithms could not. Moreover, parallel execution of Asynchronous Teams on a computer network presented linear speed up. We also observed that Asynchronous Teams for solving TSP are scale efficient; that is, the more algorithms an Asynchronous Team uses, the better it performs.
We tested and analyzed several design parameters for Asynchronous Teams such as selection and destruction policies (how agents select and destroy data from shared memories,) initialization policies (how to initiate shared memories,) memory sizes, and the influence of data flows on the performance of Asynchronous Teams. We also present some specially developed algorithms to be exclusively executed in Asynchronous Teams.
Finally, we present a Markov-based model that not only explains the behavior, but also helps in designing Asynchronous Teams by giving insights about expected value of final solutions, optimal relative execution frequencies of algorithms, and convergence of Asynchronous Teams.

Sarosh N. Talukdar

Papers

Large blackouts in North America: Historical trends and policy implications

Asynchronous Teams: Cooperation Schemes for Autonomous Agents

Trends in the history of large blackouts in the United States

Automated probabilistic method for assigning backbone resonances of (13C,15N)-labeled proteins.

Asynchronous organizations for multi-algorithm problems