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The challenge of optimizing personalized learning pathways to maximize student engagement and minimize task completion time while adhering to prerequisite constraints remains a significant issue in educational technology. This paper applies the Salp Swarm Algorithm (SSA) as a new solution to this problem. Our approach compares SSA against traditional optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results demonstrate that SSA significantly outperforms these methods, achieving a lower average fitness value of 307.0 compared to 320.0 for GA and 315.0 for PSO. Furthermore, SSA exhibits greater consistency, with a lower standard deviation and superior computational efficiency, as evidenced by faster execution times. The success of SSA is attributed to its balanced approach to exploration and exploitation within the search space. These findings highlight the potential of SSA as an effective tool for optimizing personalized learning experiences 

This innovative practice WIP paper describes our ongoing development and deployment of an online robotics education platform that highlighted a gap in providing an interactive, feedback-rich learning environment essential for mastering pro-gramming concepts in robotics, which they were not getting with the traditional code→ simulate→turn-in workflow. Since teaching resources are limited, students would benefit from feedback in real-time to find and fix their mistakes in the programming assignments. To integrate such automated feedback, this paper will focus on creating a system for unit testing while integrating it into the course workflow. We facilitate this real-time feedback by including unit testing in the design of programming assignments so students can understand and fix their errors on their own and without the prior help of instructors/TAs serving as a bottleneck. In line with the framework's personalized student-centered approach, this method makes it easier for students to revise and debug their programming work, encouraging hands-on learning. The updated course workflow, which includes unit tests, will strengthen the learning environment and make it more interactive so that students can learn how to program robots in a self-guided fashion. 

In this work we deal with the problem of establishing a system architecture to facilitate the real-time autonomous volumetric mapping alongside the semantic characterization of sagebrush ecosystem landscapes, in order to support the pre-fire modeling and analysis required to plan for wildfire prevention and/or suppression. The world, and more specifically the broader region of N. Nevada has been facing one of its most challenging periods over the course of the last decade, as far as uncontrolled wildfires are concerned. This has led to the development of research initiatives aimed at the ecosystem-specific modeling of the pre-, during-, and post-fire process effects in order to better understand, predict, and address these phenomena. However, to collect the required wide-field information that contains both centimeter-level volumetric mapping fidelity, as well as semantic details related to plant (sub)-species, which for the common case of sagebrush can only be identified based on close-up inspection of their foliage fine structure, satellite photography remains insufficient. To this end, we propose a perception and mapping architecture of an aerial robotic system that is capable of: a) LiDAR-based centimeter-level reconstruction, b) robust multi-modal sensor fusion Simultaneous Localization and Mapping (SLAM) lever-aging LiDAR, IMU, Visual-Inertial Odometry, and Differential GPS in a global optimization mapping framework, as well as c) a gimbal-driven point-zoom camera for the efficient real-time collection of close-up imagery of foliage pertaining to specific target plants, in order to allow their real-time identification based on their leaf micro-structure, by leveraging Deep-Learned classification deployed on a Neural Processing Unit. We present the associated systems, the overall hardware and software architecture, as well as a series of field deployment studies validating the proposed aerial robotic capabilities. 

This paper addresses the problem of dynamic allocation of robot resources to tasks with hierarchical representations and multiple types of execution constraints, with the goal of enabling single-robot multitasking capabilities. Although the vast majority of robot platforms are equipped with more than one sensor (cameras, lasers, sonars) and several actuators (wheels/legs, two arms), which would in principle allow the robot to concurrently work on multiple tasks, existing methods are limited to allocating robots in their entirety to only one task at a time. This approach employs only a subset of a robot's sensors and actuators, leaving other robot resources unused. Our aim is to enable a robot to make full use of its capabilities by having an individual robot multitask, distributing its sensors and actuators to multiple concurrent activities. We propose a new architectural framework based on Hierarchical Task Trees that supports multitasking through a new representation of robot behaviors that explicitly encodes the robot resources (sensors and actuators) and the environmental conditions needed for execution. This architecture was validated on a two-arm, mobile, PR2 humanoid robot, performing tasks with multiple types of execution constraints. 

Research demonstrates a growing mental health crisis in graduate education, which can contribute to productivity, departure, and well-being issues. To address this crisis and advocate for systemic change, this project explored faculty perceptions about graduate student mental health and how these perceptions intersect with direct action when student mental health challenges arise. We were guided by phenomenological inquiry to explore how faculty attitudes (n = 3) about mental health shape programmatic and individual decisions around supporting mental health. We thematically analyzed interviews discussing stress and mental health focused on faculty experiences. Faculty interviews demonstrated varying attitudes toward graduate student stress and mental health. Faculty desires to engage in discussions about stress or mental health were on a wide spectrum, often with work productivity guiding these discussions. Further, faculty highlighted levels of discomfort with engaging in discussions about mental health, especially with the students they work closest with. Findings indicate a need to foster faculty skill and comfort with engaging with students about their mental health while also providing clear institutional policies that support these actions to address the mental health crisis. 

The research paper examines how engineering doctoral students describe their awareness and experiences with stress and mental health during their graduate studies. Despite the known bidirectional relationship between stress and mental health, there is limited research on how engineering doctoral students rationalize the disparity between the health consequences of chronic stress and the veneration of academic endurance in the face of these challenges. Given the dangers of chronic stress to physical and mental health, it is important to understand how students perceive the purpose and impact of stress and mental health within overlapping cultures of normalized stress. We conducted semi-structured interviews to understand participants' awareness, conceptualizations, and interpretations of stress and mental health. The research team analyzed interview transcripts using content analysis with inductive coding. Overall, we found that our participants recognized behavioral changes as an early sign of chronic stress while physical changes were a sign of sustained chronic stress; these cues signaled that participants needed additional support, including social support and campus mental health services. These findings support the need for greater mental health awareness and education within engineering doctoral programs to help students identify and manage chronic stress. 

Cloud computing is a concept introduced in the information technology era, with the main components being the grid, distributed, and valuable computing. The cloud is being developed continuously and, naturally, comes up with many challenges, one of which is scheduling. A schedule or timeline is a mechanism used to optimize the time for performing a duty or set of duties. A scheduling process is accountable for choosing the best resources for performing a duty. The main goal of a scheduling algorithm is to improve the efficiency and quality of the service while at the same time ensuring the acceptability and effectiveness of the targets. The task scheduling problem is one of the most important NP-hard issues in the cloud domain and, so far, many techniques have been proposed as solutions, including using genetic algorithms (GAs), particle swarm optimization, (PSO), and ant colony optimization (ACO). To address this problem, in this paper one of the collective intelligence algorithms, called the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. The performance of the proposed algorithm has been compared with that of GAs, PSO, continuous ACO, and the basic SSA. The results show that our algorithm has generally higher performance than the other algorithms. For example, compared to the basic SSA, the proposed method has an average reduction of approximately 21% in makespan. 

In an efficient and flexible human-robot collaborative work environment, a robot team member must be able to recognize both explicit requests and implied actions from human users. Identifying “what to do” in such cases requires an agent to have the ability to construct associations between objects, their actions, and the effect of actions on the environment. In this regard, semantic memory is being introduced to understand the explicit cues and their relationships with available objects and required skills to make “tea” and “sandwich”. We have extended our previous hierarchical robot control architecture to add the capability to execute the most appropriate task based on both feedback from the user and the environmental context. To validate this system, two types of skills were implemented in the hierarchical task tree: 1) Tea making skills and 2) Sandwich making skills. During the conversation between the robot and the human, the robot was able to determine the hidden context using ontology and began to act accordingly. For instance, if the person says “I am thirsty” or “It is cold outside” the robot will start to perform the tea-making skill. In contrast, if the person says, “I am hungry” or “I need something to eat”, the robot will make the sandwich. A humanoid robot Baxter was used for this experiment. We tested three scenarios with objects at different positions on the table for each skill. We observed that in all cases, the robot used only objects that were relevant to the skill.