Robot-Based Creation of Complete 3D Workpiece Models

A complete object database containing a model (representing geometric and texture information) of every possible workpiece is a common necessity e.g. for different object recognition or task planning approaches. The generation of these models is often a tedious process. In this paper we present a fully automated approach to tackle this problem by generating complete workpiece models using a robotic manipulator. A workpiece is recorded by a depth sensor from multiple views for one side, then turned, and captured from the other side. The resulting point clouds are merged into one complete model. Additionally, we represent the information provided by the object’s texture using keypoints. We present a proof of concept and evaluate the precision of the final models. In the end we conclude the usefulness of our approach showing a precision of around 1 mm for the resulting models.

A Novel Training and Collaboration Integrated Framework for Human–Agent Teleoperation

Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operational errors or task failure. Although agent-based methods offer a promising solution to the above problems, the human experience and intelligence are necessary for teleoperation scenarios. In this paper, a truncated quantile critics reinforcement learning-based integrated framework is proposed for human–agent teleoperation that encompasses training, assessment and agent-based arbitration. The proposed framework allows for an expert training agent, a bilateral training and cooperation process to realize the co-optimization of agent and human. It can provide efficient and quantifiable training feedback. Experiments have been conducted to train subjects with the developed algorithm. The performances of human–human and human–agent cooperation modes are also compared. The results have shown that subjects can complete the tasks of reaching and picking and placing with the assistance of an agent in a shorter operational time, with a higher success rate and less workload than human–human cooperation.

Effectiveness of different requirements checklists for novice designers

Working under constrained conditions can boost or kill creativity, depending on the nature of the constraints (organizational, personal or task-related). However, a design process without clearly identified constraints, which set the project objectives, could lead to inefficiencies and unfruitful iterations. Some of the most acknowledged procedures to support requirement definition are focused on the use of specific checklists. However, notwithstanding the importance of the task, little attention was dedicated to the verification of the effectiveness of these tools. In such a context, the paper presents an investigation aimed at assessing the performance of three checklists that exploit different strategies to elicit requirements. To that purpose, a sample of fifty engineering students was asked to use the checklists to define the requirements for a specific design case. The outcomes of the experiment were assessed according to well-acknowledged effectiveness metrics, i.e. quantity, operationality, validity, non-redundancy, and completeness. The result of the assessment highlights that checklists based on more general questions or abstract stimuli can better support novice designers in making explicit internally felt design constraints that can potentially lead to more innovative design.

EXPRESS: The Dominance of Item Learning in the Location-Specific Proportion Congruence Paradigm

Prior research has shown that various cues are exploited to reactively adjust attention and such adjustments depend on learning associations between cues and proportion congruence. This raises the intriguing question of what will be learned when more than one cue is available, a question that has implications for understanding which cue(s) will dominate in guiding reactive adjustments. Using a picture-word Stroop task, Bugg, Suh, Colvett, and Lehmann (2020) provided initial evidence that item learning dominated over location learning in a location-specific proportion congruence (LSPC) paradigm, a pattern that may explain the difficulty researchers have faced in replicating and reproducing the LSPC effect. One goal was to reproduce this pattern using a non-overlapping two-item sets design that more closely matched prior studies, and another goal was to examine generalizability of the pattern to two other tasks. Using a prime-probe, color-word Stroop task (Experiment 1) and a flanker task (Experiment 2), we again found clear dominance of item learning. In Experiment 3, we attempted to disrupt item learning and promote location learning by using a counting procedure that directed participants’ attention to location. Once again, we found the same pattern of item dominance. Additionally, in none of the experiments did we find evidence for conjunctive (location-item) learning. Collectively, the findings suggest item learning is neither design- or task-specific; rather, it is robust, reliable, and not easily disrupted. Discussion centers on factors dictating dominance of item- over location-based adjustments and implications for the broader literature on LSPC effects.

Explaining Evolutionary Agent-Based Models via Principled Simplification

Understanding how evolutionary agents behave in complex environments is a challenging problem. Agents can be faced with complex fitness landscapes derived from multi-stage tasks, interaction with others, and limited environmental feedback. Agents that evolve to overcome these can sometimes access greater fitness, as a result of factors such as cooperation and tool use. However, it is often difficult to explain why evolutionary agents behave in certain ways, and what specific elements of the environment or task may influence the ability of evolution to find goal-achieving behaviours; even seemingly simple environments or tasks may contain features that affect agent evolution in unexpected ways. We explore principled simplification of evolutionary agent-based models, as a possible route to aiding their explainability. Using the River Crossing Task (RCT) as a case study, we draw on analysis in the Minimal River Crossing (RC-) Task testbed, which was designed to simplify the original task while keeping its key features. Using this method, we present new analysis concerning when agents evolve to successfully complete the RCT. We demonstrate that the RC- environment can be used to understand the effect that a cost to movement has on agent evolution, and that these findings can be generalised back to the original RCT. Then, we present new insight into the use of principled simplification in understanding evolutionary agents. We find evidence that behaviour dependent on features that survive simplification, such as problem structure, are amenable to prediction; while predicting behaviour dependent on features that are typically reduced in simplification, such as scale, can be invalid.

Solving “Limited” Task Allocation Problem for UAVs Based on Optimization Algorithms

With the rapid development of science and technology, unmanned technology has been widely used in many fields. One of the most important applications is in the field of civil and military UAVs. In the field of military UAVs (unmanned aerial vehicles), UAVs usually have to complete a series of tasks. In this series of tasks, there are often some key tasks. Key tasks play an important role, which is highly related to the feasibility of the whole action or task; mission failure sometimes causes incalculable damage. When assigning tasks to UAVs, it is necessary to ensure the accurate implementation of key tasks, so as to ensure the orderly implementation of the overall task. This paper not only successfully solved the previous problems but also comprehensively considered the minimization of resource consumption and the maximization of task revenue in the process of UAV task allocation. On the basis of considering the key system, considering the constraints and multiobjective problems in the UAV task allocation process, the violence allocation algorithm, constraint optimization evolutionary algorithm, PSO algorithm, and greedy algorithm combined with a constraint evolutionary algorithm are improved and optimized; it has been proven that they can solve the above difficulties. At the same time, several comparison experiments have been carried out; the performance and conclusion of the above four algorithms in the “limited” UAV task allocation scheme are analyzed in the experimental part.

A Framework for Measuring Relevancy in Discovery Environments

Discovery environments are ubiquitous in academic libraries but studying their effectiveness and use in an academic environment has mostly centered around user satisfaction, experience, and task analysis. This study aims to create a quantitative, reproducible framework to test the relevancy of results and the overall success of Washington State University’s discovery environment (Primo by Ex Libris). Within this framework, the authors use bibliographic citations from student research papers submitted as part of a required university class as the proxy for relevancy. In the context of this study, the researchers created a testing model that includes: (1) a process to produce machine-generated keywords from a corpus of research papers to compare against a set of human-created keywords, (2) a machine process to query a discovery environment to produce search result lists to compare against citation lists, and (3) four metrics to measure the comparative success of different search strategies and the relevancy of the results. This framework is used to move beyond a sentiment or task-based analysis to measure if materials cited in student papers appear in the results list of a production discovery environment. While this initial test of the framework produced fewer matches between researcher-generated search results and student bibliography sources than expected, the authors note that faceted searches represent a greater success rate when compared to open-ended searches. Future work will include comparative (A/B) testing of commonly deployed discovery layer configurations and limiters to measure the impact of local decisions on discovery layer efficacy as well as noting where in the results list a citation match occurs.

Critical Language Pedagogy and Task-Based Language Teaching: Reciprocal Relationship and Mutual Benefit

Curriculum development and empirical research in the field of second language acquisition could benefit the field of critical language pedagogy (CLP) and its practitioners. This article reviews central concepts in the organization of curriculum in CLP and compares them with another major curricular initiative in second language teaching, namely task-based or task-supported approaches, with particular emphasis on task-based language teaching. Content itself (as in activities or materials) and the role of metacognitive instruction are considered. A real-world example of a task-based or task-supported short-term program is reviewed as exemplifying some areas of contact or benefit.

Symbiotic Approach in a Modular Mobile Robot

This paper presentsthe application of a symbiotic approachin a modular mobile robot. This characteristic behavior might help addressthe challengesin modular reconfigurable robotoperation. The general model symbiosisalgorithm will help decide if the modular part is harmful or beneficial to the performance or task of the robotic systemthru the carrying capacity. The symbiotic behavior is presented and implemented in this paper viamodel-baseddesign with the aid of MATLABSimulink using a 6 wheeled mobile robot with 3 modular body to identify the carrying capacity of the system.Carrying capacity istranslated and used as thedistance and velocity capacity of the design model robotic system.Carrying capacity is greatly influence by the number species or in our case modules it is shown in this paper that carrying capacity are not fixed in quantities but should be consider as functions of the population sizesand function. The mathematical formulation of the idea is to investigate its consequence. Aside from the population size role or interaction.

Localization of a Micro AUV with Dynamic Trilateration Using Low-power Packet Radio RSSI

Communication radio-based AUV localization was demonstrated in this study. The proposed solution was formulated and derived for both stationary and linearly drifting objects of interest and is possible of operating in GNSS-denied operations. Linear curve-fit to experimental data for radio-distance mapping with range calculation was tested in terrestrial and marine environments. The use of packet radio equipment on a secondary basis for localization may present a potential for reduced requirements for high precision or task-specific hardware in the future.