Laboratory Learning Objectives Measurement: Relationships Between Student Evaluation Scores and Perceived Learning

Contribution: This article provides evidence that perceived learning has a relationship and influences the way students evaluate laboratory experiments, facilities, and demonstrators. Background: Debate continues on the capability and/or reliability of students to evaluate teaching and/or learning. Understanding such relationships can help educators decode evaluation data to develop more effective teaching experiences. Research Question: Does a relationship exist between student evaluation scores and perceived learning? Methodology: Perceived learning across the cognitive, psychomotor, and affective domains was measured using the Laboratory Learning Objectives Measurement (LLOM) tool at an Australian (344 students) and Serbian (181 students) university. A multilevel statistical analysis was conducted. Findings: Statistically significant relationships were found between student evaluation scores and perceived learning across the cognitive, psychomotor, and affective domains with some differences found between the two universities. This provides evidence that perceived learning plays a role in influencing student evaluation scores. Students perceived an improvement of learning across all three domains confirming the multifaceted benefits of the laboratory for engineering education

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment with Multi-Criteria Decision Making Using Full Consistency Method

The progress in the research of various areas of robotics, artificial intelligence, and other similar scientific disciplines enabled the application of multi-robot systems in different complex environments and situations. It is necessary to elaborate the strategies regarding the path planning and paths coordination well in order to efficiently execute a global mission in common environment, prior to everything. This paper considers the multi-robot system based on the cloud technology with a high level of autonomy, which is intended for execution of tasks in a complex and crowded environment. Cloud approach shifts computation load from agents to the cloud and provides powerful processing capabilities to the multi-robot system. The proposed concept uses a multi-robot path planning algorithm that can operate in an environment that is unknown in advance. With the aim of improving the efficiency of path planning, the implementation of Multi-criteria decision making (MCDM) while using Full consistency method (FUCOM) is proposed. FUCOM guarantees the consistent determination of the weights of factors affecting the robots motion to be symmetric or asymmetric, with respect to the mission specificity that requires the management of multiple risks arising from different sources, optimizing the global cost map in that way

The Puller-Follower Control of Compliant and Noncompliant Antagonistic Tendon Drives in Robotic Systems

This paper proposes a new control strategy for noncompliant and compliant antagonistic tendon drives. It is applied to a succession of increasingly complex single‐joint systems, starting with a linear and noncompliant system and ending with a revolute, nonlinearly tendon coupled and compliant system. The last configuration mimics the typical human joint structure, used as a model for certain joints of the anthropomimetic robot ECCEROBOT. The control strategy is based on a biologically inspired puller‐ follower concept, which distinguishes the roles of the agonist and antagonist motors. One actuator, the puller, is considered as being primarily responsible for the motion, while the follower prevents its tendon from becoming slack by maintaining its tendon force at some non‐zero level. Certain movements require switching actuator roles; adaptive co‐contraction is used to prevent tendons slackening, while maintaining energetic efficiency. The single‐joint control strategy is then evaluated in a multi‐ joint system. Dealing with the gravitational and dynamic effects arising from the coupling in a multi‐joint system, a robust control design has to be applied with on‐line gravity compensation. Finally, an experiment corresponding to object grasping is presented to show the controlle

Control Of Compliant Anthropomimetic Robot Joint

In this paper we propose a control strategy for a robot joint which fully mimics the typical human joint structure. The joint drive is based on two actuators (dc motors), agonist and antagonist, acting through compliant tendons and forming a nonlinear multi-input multi-output (MIMO) system. At any time, we consider one actuator, the puller, as being responsible for motion control, while the role of the other is to keep its tendon force at some appropriate low level. This human-like and energetically efficient approach requires the control of "switching", or exchanging roles between actuators. Moreover, an algorithm based on adaptive force reference is used to solve a problem of slacken tendons during the switching and to increase the energy efficiency. This approach was developed and evaluated on increasingly complex robot joint configurations, starting with linear and noncompliant system, and finishing with nonlinear and compliant system

Modelling and control of a compliantly engineered anthropomimetic robot in contact tasks

This paper attempts to develop a dynamic model and design a controller for a fully anthropomorphic, compliantly driven robot. To imitate muscles, the robot's joints are actuated by DC motors antagonistically coupled through tendons. To ensure safe interaction with humans in a humancentered environment, the robot exploits passive mechanical compliance, in the form of elastic springs in the tendons. To enable simulation, the paper first derives a mathematical model of robot dynamics, starting from the "Flier" approach. The control of the antagonistic drives is based on a biologically inspired puller-and-follower concept where at any instant the puller is responsible for the joint motion while the follower keeps the inactive tendon from slackening. In designing the controller, it was necessary to use the advanced theory of nonlinear control for dealing with individual joints, and then to apply the theory of robustness in order to extend control to the multi-jointed robot body

Force Sharing Problem During Gait Using Inverse Optimal Control

Human gait patterns have been intensively studied, both from medical and engineering perspectives, to understand and compensate pathologies. However, the muscle-force sharing problem is still debated as acquiring individual muscle force measurements is challenging, requiring the use of invasive devices.Recent studies, using various objective functions, suggest muscleforce sharing may result from an optimization process. This study proposes using inverse optimal control to identify an objective function. Two popular methods of inverse optimal control, bilevel and inverse Karush-Kuhn-Tucker, were investigated. The identifiedobjective functions were then used to predict muscle forces during gait, and their performances were compared to an exhaustive list of biological cost functions from the literature. The best predictionwas achieved by the bilevel inverse optimal control method, with a root-mean-squared error of 176 N (162 N) and a correlationcoefficient of 0.76 (0.68) for the stance (swing) phase of the gait cycle.These muscle force predictions were thereafter used to compute joint stiffness, exhibiting an average root-mean-square error of 42 Nm.rad?1 and a correlation coefficient of 0.90 when comparedto the reference. The bilevel method's prevalence in terms of robustnessover inverse Karush-Kuhn-Tucker was demonstrated on human data and explained on a toy example

Assessing the Quality of a Set of Basis Functions for Inverse Optimal Control via Projection onto Global Minimizers

International audienceInverse optimization (Inverse optimal control) is the task of imputing a cost function such that given test points (trajectories) are (nearly) optimal with respect to the discovered cost. Prior methods in inverse optimization assume that the true cost is a convex combination of a set of convex basis functions and that this basis is consistent with the test points. However, the consistency assumption is not always justified, as in many applications the principles by which the data is generated are not well understood. This work proposes using the distance between a test point and the set of global optima generated by the convex combinations of the convex basis functions as a measurement for the expressive quality of the basis with respect to the test point. A large minimal distance invalidates the set of basis functions. The concept of a set of global optima is introduced and its properties are explored in unconstrained and constrained settings. Upper and lower bounds for the minimum distance in the convex quadratic setting are implemented by bi-level gradient descent and an enriched linear matrix inequality respectively. Extensions to this framework include max-representable basis functions, nonconvex basis functions (local minima), and applying polynomial optimization techniques