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A fast optimal task allocation (OTA) algorithm maximizes the reliability of redundant distributed computing systems by finding an optimal and sub-optimal task-allocation. The method assigns the processing node for each module of a task or program. The algorithm is robust for a wide range of parameters such as number of modules, number of processing nodes, ratio of average execution cost to average communication cost and connectivity of modules. The performance improves on increasing the difference between the number of modules and connectivity.

This paper describes a framework for robotic task sequence planning which decomposes tasks into operations sequences for a generic robotic workcell. The approach provides robust excucution of tasks through properties of: traceability-implicit mapping of operations to task representation, and viability-retaining multiple paths for execution. Given the descriptions of the objects in the system and all feasible geometric configurations and relationships among these objects, an AND/OR net which describes the relationships of all feasible geometric states and associated feasibility criteria for net transitions is generated. This AND/OR net is mapped into a Petri net which incorporates all feasible sequences of high level operations. The resulting Petri net is then decomposed in a stepwise manner into lower level Petri nets of which each transition can be directly implemented by control commands or command sequences based on devices and objects in the system, or, by lower level planning transitions corresponding to path planning, grasp planning, fine motion planning, etc. The property analysis for different levels of decomposition is also presented, and the inheritance of properties between levels is defined. All possible task sequences could be found using a search algorithm based on feasible system states. The shortest sequence may be chosen from the lowest level decomposition and is guaranteed to be the optimal output of the hierarchical planning system to efficiently implement the desired tasks.

This study investigates the effects of a GSS on the decision outcome of five-person, face-to-face groups performing two distinct but related task types. A controlled laboratory experiment with a 3 X 2 factorial design was employed. The independent variables were support level and task type. A total of 68 groups were randomly assigned to different support levels (no support, structure support, or GSS support). Half of these groups performed an intellective task while the other half performed a preference task. The empirical findings show that task type affects consensus change and influence equality. Moreover, support level and task type jointly affect decision scheme satisfaction. This paper attempts to reconcile the body of empirical findings from related GSS studies. It discusses the implications of task type for GSS design and use.

Currently, most robot programming is done either by manual programming or using a teach pendant as part of the "teach-by-showing" method. Both of these methods have been found to have several drawbacks. We are developing a novel method with which to program a robot: the assembly-plan-from-observation (APO) method. The APO method aims to build a system that has the capability of observing a human performing an assembly task, understanding the task based on the observation, and subsequently generating a robot program to achieve the same task. This paper focuses on the task recognition module (TRM), the main component of a complete APO system. The TRM recognizes object configurations before and after an assembly task, detects a configuration transition, and infers the assembly task that causes such a configuration transition. We assume that each assembly task aims to achieve a face contact relation between an object that has just been manipulated and stationary environmental objects. We prepare abstract task models that associate transitions of face contact relations with assembly tasks that achieve such transitions. Next, we implement TRM in order to verify two issues: 1) that such a contact transition can be recovered from the output of the object recognizer, and 2) that given these relation transitions, it is possible to use the abstract task models to effect the generation of robot motion commands; the execution of these commands will culminate in a repetition on the original assembly task.

A new systematic approach to evaluating the task graph of tasks executed under preplanned allocation constraints is presented.