This research-to-practice paper investigated the development of Functional Analysis Learning Trajectories (FALT) within the undergraduate courses for engineers including Calculus, Differential Equations, Communications Theory, Control Systems and Electromagnetism. Focusing on the local instructional practices, manifestations of functional analysis ideas are tracked along these courses. Corresponding concept maps are collaboratively within and across courses to portray the relevant connections for engineering mathematics education. These trajectories help engineering and mathematics instructors to collectively design, implement, refine task sequences to improve students' preparedness for upper-level math-heavy courses in engineering, connecting mathematics to their disciplines. Here we utilized collaborative concept mapping as a research heuristics to build curricular innovations with functional analysis learning trajectories across courses, hierarchically arranging, integrating, and conceptually connecting instructional tasks. Through sequences of dual stance learning tasks, students are given opportunities to take multiple stances in a learning task from the perspectives of engineers and mathematical scientists. A higher stance on mathematics was supported to be developed by comparing and connecting alternative disciplinary perspectives and practices with the dual modeling tasks and reflecting on the cross-cutting ideas along the learning trajectory. Here we present the collaborative design and analysis of concept maps along functional analysis learning trajectories for undergraduates. This research builds an interdisciplinary scholarship of teaching/learning mathematics across disciplines among engineering and math faculty. This work helps foster reflection and collaboration on their teaching practices to design and implement instructional tasks to build coherent mathematical perspectives across disciplines. It exemplifies how to design a research-based practices to build cross-curricular innovations. Concept maps within courses are presented and discussed here to build mathematical connections and functional analysis learning trajectories for engineering and applied mathematics education.

Due to the insufficient transient amount of energy supplied from ambient energy sources and constrained amount of energy storage in super-capacitors, energy harvesting (EH) nodes are limited with operations and vulnerable to frequent faults due to energy scarcity. Consequently, such faults will reduce reliability and energy utility due to data collisions, lost data, or idle listening. To address these challenges, this work implements a novelty task scheduling scheme to minimize energy waste and maximize throughput under these scenarios and constraints. To demonstrate the effectiveness, we use a green test bed using LoRa nodes for evaluation.

This paper presents a coaxial modular aerial system (CMAS) formed by homogeneous modules driven by their center of mass. CMAS is designed to perform independent and cooperative flight with or without payload. Properties of the modularity concept allow the system to adapt to different situations and/or tasks by adding/removing modules to/from a configuration. The CMAS module is based on a coaxial motor and a two degree-of-freedom mechanism that transfers its center of mass from one side to another to make the module navigate around. The magnetic-based connector mechanism allows the module to be attached to other modules and to different metallic surfaces. A decentralized and asynchronous 3D path planning algorithm is implemented to avoid the trajectories of other modules/obstacles and ensures safe reconfiguration of the modules. Simulations within various environments show the applicability of the reconfiguration algorithm.

Unmanned Aerial Systems (UAS), commonly known as drones, have revolutionized various industries with their diverse applications. As the demand for seamless and intuitive drone control grows, researchers are exploring innovative approaches to improve human-swarm interaction. This paper presents a novel method for operating a swarm of drones in real time using wearable technology and machine learning. Through the integration of motion capture data and classification algorithms, we strive to achieve an intuitive level of control that is accessible to users with varying skill levels. While the full realization of this approach remains a work in progress, our research lays the groundwork for future endeavors in this domain. In this paper, we discuss the limitations of existing control methods and present our methodology for data preprocessing, model training and testing, and result analysis. Our findings indicate the potential of this approach and open avenues for refining the interaction between humans and drone swarms.

The paper presents a practical approach for planning trajectories for multiple vehicles where both collision avoidance and minimum travelling time are simultaneously considered. It is first proposed to exploit the mixed-integer programming (MIP) approach to formulate the collision avoidance paradigm, where the linear dynamic models are utilized to derive the linear constraints. Moreover, travelling time of each vehicle is compromised among them and set to be minimized so that all the vehicles can practically reach the expected destinations at the shortest time. Unfortunately, the formulated optimization problem is NP-hard. In order to effectively address it, we propose to employ the alternating direction method of multipliers (ADMM), which can share the computational burdens to distributive optimization solvers. Thus, the proposed method can enable each vehicle to obtain an expected trajectory in a practical time. Convergence of the proposed algorithm is also discussed. To verify effectiveness of our approach, we implemented it in a numerical example, where the obtained results are highly promising.

We consider the problem of discovering and representing the topology of a modular self-reconfigurable robot (MSR) in which modules do not have a priori information about other modules that belong to the same configuration. We propose a solution that combines two main features of modules - their geometric shape and their inter-connection with other modules, represented as a connectivity graph. We describe a distributed algorithm that can be used by each module to construct the connectivity graph by discovering other modules in four steps - sharing IDs, creating a local configuration structure (LCS) or list of local neighbors, sharing a matrix with LCSs to adjacent modules, and building an adjacency matrix by combining LCSs. Data is exchanged between modules using infra-red (IR) communication. Our proposed technique can operate within limited computational resources available on a module and is robust to module failures. We have tested and demonstrated the successful operation of our proposed technique on the ModRED (Modular Robot for Exploration and Discovery) platform and shown that modules are able to discover both linear and branched configurations using our algorithm.

We describe a distributed and autonomous technique for dynamic gait adaptation for a chain-type, modular self-reconfigurable robot (MSR) using a fuzzy logic based, closed-loop controller. To maneuver itself, each module of the MSR is provided with a set of basic or fundamental gaits within a gait control table(GCT). A relevant problem in locomotion of a chain-type MSR is how to coordinate the gait of the individual modules with each other so that the desired locomotion of the MSR can be achieved. To address this problem, our proposed controller maps the inputs from the sensors of each module to an appropriate gait for the module determined from the goal and position of the module in the configuration, using a fuzzy technique. An inertial measurement unit (IMU) is used to close the loop between the goal and the module. We have verified the operation of our controller on a simulated 3-D model of an MSR called ModRED within the Webots robot simulator and also implemented it on the physical ModRED MSR. Our results illustrate that our controller can successfully adapt ModRED's locomotion by dynamically combining basic gaits from the individual modules in the configuration, regardless of the number of modules in the configuration and in the presence of noisy sensor inputs.

The complexity in the execution of cooperative tasks is high due to the fact that a robot team requires movement coordination at the beginning of the mission and continuous coordination during the execution of the task. A variety of techniques have been proposed to give a solution to this problem assuming standard mobile robots. This work focuses on presenting the execution of a cooperative task by a modular robot team. The complexity of the task execution increases due to the fact that each robot is composed of modules which have to be coordinated in a proper way to successfully work. A combined tight and loose cooperation strategy is presented and a bar-pushing example is used as a cooperative task to show the performance of this type of system.

The modeling of a modular robot system is complex due to its capability of changing and forming countless robot configurations with a set of modules. The greater the number of modules added to the structure, more complicated is the robot and its modeling. A methodology to represent modular robot configurations as a fixed shape robot is presented. The equivalent model is obtained through the characterization of the module, graph theory and geometric modeling of robots. After the transformation, the resulting kinematic and dynamic parameters are similar to the traditional model of a single configuration robot. Once the equivalent model is obtained, it can be used within different robot applications.

This paper describes a modular robot system design SMART, based on three types of modules for urban search tasks. The system attempts to give a quick solution to natural and man-made disaster emergencies. It allows for rapid and cost-effective design and fabrication. The approach is based on the use of an inventory of three types of modules i.e., power and control module, joint module, and specialized module. They are interchangeable in different ways to form different robot configurations for a variety of tasks. Forward and inverse kinematics from assembled robot configurations are analyzed. Description of control motion modes for human-modular robot system interaction is presented.

This paper shows the advantages of having a modular system with 3-DoF spherical actuator in the base module to perform tasks that require displacement and object manipulation. Having 3-DoF actuator improves the complexity of coordination patterns and control algorithms of the modular system more relevantly as compared to having only 1 or 2DoF actuator in the module. Nevertheless, modules with actuators of only 1 or 2DoF require more modules to be assembled together in order to achieve complex tasks. Experiments performed with RobMAT modular system proves that a 3DoF actuator in the module is better, because tasks such as displacement, obstacle climbing and object manipulation, can be efficiently carried out with systems of 2 modules, 4 modules and 6 (maximum) modules connected together.