Design of a low-cost,highly mobile urban search and rescue robot

In this paper, we discuss the design of a novel robotic platform for urban search and rescue. The system developed possesses unique mobility capabilities based on a new adjustable compliance mechanism and overall locomotive morphology. The main facets of this work involve the morphological concepts, initial design and construction of a prototype vehicle, and a physical simulation to be used for developing controllers for semi-autonomous (supervisory) operation.     

An evolutionary algorithm for simultaneous localization and mapping (SLAM) of mobile robots

This paper presents a novel algorithm for simultaneous localization and mapping (SLAM) of mobile robots. The algorithm, termed Evolutionary SLAM, is based on an island model genetic algorithm (IGA). The IGA searches for the most probable map(s) such that the underlying robot's pose(s) provide(s) a robot with the best localization information. The correspondence problem in SLAM is solved by exploiting the property of natural selection, to support only better-performing individuals to survive. The algorithm does not follow any explicit heuristics for loop closure, rather it maintains multiple hypotheses to solve the loop-closing problem. The algorithm processes sensor data incrementally and, therefore, has the capability to work online. Experimental results in different indoor environments validate the robustness of the proposed algorithm.     

Error analysis of forward and reverse heat conduction and convection calculations considering uncertainties in welding

Abstract

Numerical models of fusion welding traditionally compute temperature field for a given set of welding conditions in a forward manner. The reliability of computed temperature profile depends on the accuracy of a number of model input parameters, values of which are uncertain in nature. Here, the authors show that a genetic algorithm (GA) assisted integrated numerical model, following either convection or conduction based calculations, can identify the suitable values of the uncertain model input parameters and in turn provide reliable computed results. Powered with GA, the integrated model is used further in a reverse manner to predict multiple sets of welding conditions for a target weld geometry. The convection based calculations have been able to provide more reliable multiple welding variables in reverse calculations.     

MODIFIED GENETIC ALGORITHM APPLIED TO SOLVE PRODUCT FAMILY OPTIMIZATION PROBLEM

The product family design problem solved by evolutionary algorithms is discussed.A successful product family design method should achieve an optimal tradeoff among a set of compet- ing objectives,which involves maximizing commonality across the family of products and optimizing the performances of each product in the family.A 2-level chromosome structured genetic algorithm (2LCGA)is proposed to solve this class of problems and its performance is analyzed in comparing its results with those obtained with other methods.By interpreting the chromosome as a 2-level linear structure,the variable commonality genetic algorithm(GA)is constructed to vary the amount of plat- form commonality and automatically searches across varying levels of commonality for the platform while trying to resolve the tradeoff between commonality and individual product performance within the product family during optimization process.By incorporating a commonality assessing index to the problem formulation,the 2LCGA optimize the product platform and its corresponding family of products in a single stage,which can yield improvements in the overall performance of the product family compared with two-stage approaches(the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage). The scope of the algorithm is also expanded by introducing a classification mechanism to allow mul- tiple platforms to be considered during product family optimization,offering opportunities for supe- rior overall design by more efficacious tradeoffs between commonality and performance.The effec- tiveness of 2LCGA is demonstrated through the design of a family of universal electric motors and comparison against previous results.     

Genetic algorithm for optimisation of A-TIG welding process for modified 9Cr–1Mo steel

Abstract

It is essential to set up the activated tungsten inert gas (A-TIG) welding process parameters to produce the desired weld bead geometry and heat affected zone (HAZ) width in modified 9Cr–1Mo steel weld joints. Therefore, it becomes necessary to develop a tool for optimisation of A-TIG welding process. Genetic algorithm (GA) based model has been developed to determine the optimum process parameters. In this methodology, first independent ANN models correlating depth of penetration, weld bead width and HAZ width with current, voltage and torch speed respectively were developed. Then, GA code was developed in which the objective function was evaluated using the ANN models. There was good agreement between the target and actual values of bead geometry and HAZ width obtained using the GA optimised process parameters. Thus, a methodology using GA has been developed for optimising the A-TIG process parameters for modified 9Cr–1Mo steel.     

Gait Pattern Adaptation for an Active Lower-Limb Orthosis Based on Neural Networks

This work deals with neural network (NN)-based gait pattern adaptation algorithms for an active lower-limb orthosis. Stable trajectories with different walking speeds are generated during an optimization process considering the zero-moment point (ZMP) criterion and the inverse dynamic of the orthosis–patient model. Additionally, a set of NNs is used to decrease the time-consuming analytical computation of the model and ZMP. The first NN approximates the inverse dynamics including the ZMP computation, while the second NN works in the optimization procedure, giving an adapted desired trajectory according to orthosis–patient interaction. This trajectory adaptation is added directly to the trajectory generator, also reproduced by a set of NNs. With this strategy, it is possible to adapt the trajectory during the walking cycle in an on-line procedure, instead of changing the trajectory parameter after each step. The dynamic model of the actual exoskeleton, with interaction forces included, is used to generate simulation results. Also, an experimental test is performed with an active ankle–foot orthosis, where the dynamic variables of this joint are replaced in the simulator by actual values provided by the device. It is shown that the final adapted trajectory follows the patient intention of increasing the walking speed, so changing the gait pattern.     

Non-holonomic Path Planning of a Free-Floating Space Robotic System Using Genetic Algorithms

In this paper, the non-holonomic characteristic of a free-floating space robotic system is used to plan the path of the manipulator joints, by whose motion the base attitude and the manipulator joints attain the desired states. Here, we parameterize the joint trajectory using sinusoidal functions, whose arguments are high-order polynomials. Then, we define the cost function for optimization according to the constraint conditions and the accuracy of the space robot. Finally, genetic algorithms (GAs) are used to search for the solutions of the parameters. Compared with others, our approach has advantages as follows. (i) The motion of the manipulator and the disturbance on the base are practically constrained. (ii) The dynamic singularities cannot affect the algorithm since only the direct kinematic equations are utilized. (iii) The planned path is smooth and more applicable for the control of the manipulator. (iv) The convergence of the algorithm is not affected by the attitude singularity since the orientation error is represented by quaternion, which is globally singularity-free. The simulation results of the spacecraft with a 6-d.o.f. manipulator verify the performance and the validity of the proposed method.     

Discomforting thoughts on "Infantile Trauma, Genetic Factors, and Adult Temperament."

A comparison of one report of research (Lindzey et al., 1960) with another (Levine, 1958) and why antithetical results eventuated. From Psyc Abstracts 36:04:4EC19L. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Optimisation of work roll shifting and bending to roll thinner gauge HR coils using genetic algorithm

Abstract

Rolling of thin gauge hot rolled (HR) coils demands stringent flatness tolerance. Thin HR coils (≤3 mm) are rolled towards the end of any rolling campaign. The profile and flatness of the strip depend on the profile of the loaded roll gap in the mill stands. There are five key factors that influence the loaded roll gap: initial roll surface profile, roll thermal expansion, wear of roll, deflection of roll stack and shifting of work rolls. This paper deals with all these factors individually for the formulation of an objective function in order to minimise the flatness error. The shifting and bending of rolls are the controllable parameters that require optimising. This has been accomplished using a genetic algorithm (GA) optimisation technique.