Safe Robot Trajectory Control Using Probabilistic Movement Primitives and Control Barrier Functions.

In this paper, we present a novel means of control design for probabilistic movement primitives (ProMPs). Our proposed approach makes use of control barrier functions and control Lyapunov functions defined by a ProMP distribution. Thus, a robot may move along a trajectory within the distribution while guaranteeing that the system state never leaves more than a desired distance from the distribution mean. The control employs feedback linearization to handle nonlinearities in the system dynamics and real-time quadratic programming to ensure a solution exists that satisfies all safety constraints while minimizing control effort. Furthermore, we highlight how the proposed method may allow a designer to emphasize certain safety objectives that are more important than the others. A series of simulations and experiments demonstrate the efficacy of our approach and show it can run in real time.

Characterizing dopaminergic neuron vulnerability using genome-wide analysis.

Parkinson's disease (PD) is primarily characterized by the loss of dopaminergic (DA) neurons in the brain. However, little is known about why DA neurons are selectively vulnerable to PD. To identify genes that are associated with DA neuron loss, we screened through 201 wild-caught populations of Drosophila melanogaster as part of the Drosophila Genetic Reference Panel. Here, we identify the top-associated genes containing single-nucleotide polymorphisms that render DA neurons vulnerable. These genes were further analyzed by using mutant analysis and tissue-specific knockdown for functional validation. We found that this loss of DA neurons caused progressive locomotor dysfunction in mutants and gene knockdown analysis. The identification of genes associated with the progressive loss of DA neurons should help to uncover factors that render these neurons vulnerable in PD, and possibly develop strategies to make these neurons more resilient.

Mechanism and Model of a Soft Robot for Head Stabilization in Cancer Radiation Therapy.

We present a parallel robot mechanism and the constitutive laws that govern the deformation of its constituent soft actuators. Our ultimate goal is the real-time motion-correction of a patient's head deviation from a target pose where the soft actuators control the position of the patient's cranial region on a treatment machine. We describe the mechanism, derive the stress-strain constitutive laws for the individual actuators and the inverse kinematics that prescribes a given deformation, and then present simulation results that validate our mathematical formulation. Our results demonstrate deformations consistent with our radially symmetric displacement formulation under a finite elastic deformation framework.

Extremum Seeking Control for Model-Free Auto-Tuning of Powered Prosthetic Legs.

This paper proposes an extremum seeking controller (ESC) for simultaneously tuning the feedback control gains of a knee-ankle powered prosthetic leg using continuous-phase controllers. Previously, the proportional gains of the continuous-phase controller for each joint were tuned manually by trial-and-error, which required several iterations to achieve a balance between the prosthetic leg tracking error performance and the user's comfort. In this paper, a convex objective function is developed, which incorporates these two goals. We present a theoretical analysis demonstrating that the quasi-steady-state value of the objective function is independent of the controller damping gains. Furthermore, we prove the stability of error dynamics of continuous-phase controlled powered prosthetic leg along with ESC dynamics using averaging and singular perturbation tools. The developed cost function is then minimized by ESC in real-time to simultaneously tune the proportional gains of the knee and ankle joints. The optimum of the objective function shifts at different walking speeds, and our algorithm is suitably fast to track these changes, providing real-time adaptation for different walking conditions. Benchtop and walking experiments verify the effectiveness of the proposed ESC across various walking speeds.

Guiding Engineering Student Teams' Ethics Discussions with Peer Advising.

This study explores how peer advising affects student project teams' discussions of engineering ethics. Peer ethics advisors from non-engineering disciplines are expected to provide diverse perspectives and to help engineering student teams engage and sustain ethics discussions. To investigate how peer advising helps engineering student teams' ethics discussions, three student teams in different peer advising conditions were closely observed: without any advisor, with a single volunteer advisor, and with an advising team working on the ethics advising project. Micro-scale discourse analysis based on cognitive ethnography was conducted to find each team's cultural model of understanding of engineering ethics. Cultural-historical activity theory (CHAT) analysis was also conducted to see what influenced each team's cultural model. In cultural model, the engineering team with an ethics advising team showed broader understanding in social implications of engineering. The results of CHAT analysis indicated that differences in rules, community, and division of labor among three teams influenced the teams' cultural models. The CHAT analysis also indicated that the peer advisors working on the ethics advising project and the engineering team working on engineering design project created a collaborative environment. The findings indicated that collaborative environment supported peer ethics advising to facilitate team discussions of engineering ethics.

Automatic Tuning of Virtual Constraint-Based Control Algorithms for Powered Knee-Ankle Prostheses.

State-of-art powered prosthetic legs are often controlled using a collection of joint impedance controllers designed for different phases of a walking cycle. Consequently, finite state machines are used to control transitions between different phases. This approach requires a large number of impedance parameters and switching rules to be tuned. Since one set of control parameters cannot be used across different amputees, clinicians spend enormous time tuning these gains for each patient. This paper proposes a virtual constraint-based control scheme with a smaller set of control parameters, which are automatically tuned in real-time using an extremum seeking controller (ESC). ESC, being a model-free control method, assumes no prior knowledge of either the prosthesis or human. Using a singular perturbation analysis, we prove that the virtual constraint tracking errors are small and the PD gains remain bounded. Simulations demonstrate that our ESC-based method is capable of adapting the virtual-constraint based control parameters for amputees with different masses.

The Roles of Implicit Understanding of Engineering Ethics in Student Teams' Discussion.

Following previous work that shows engineering students possess different levels of understanding of ethics-implicit and explicit-this study focuses on how students' implicit understanding of engineering ethics influences their team discussion process, in cases where there is significant divergence between their explicit and implicit understanding. We observed student teams during group discussions of the ethical issues involved in their engineering design projects. Through the micro-scale discourse analysis based on cognitive ethnography, we found two possible ways in which implicit understanding influenced the discussion. In one case, implicit understanding played the role of intuitive ethics-an intuitive judgment followed by reasoning. In the other case, implicit understanding played the role of ethical insight, emotionally guiding the direction of the discussion. In either case, however, implicit understanding did not have a strong influence, and the conclusion of the discussion reflected students' explicit understanding. Because students' implicit understanding represented broader social implication of engineering design in both cases, we suggest to take account of students' relevant implicit understanding in engineering education, to help students become more socially responsible engineers.

Tracking Control of Mobile Robots Localized via Chained Fusion of Discrete and Continuous Epipolar Geometry, IMU and Odometry.

This paper presents a novel navigation and control system for autonomous mobile robots that includes path planning, localization, and control. A unique vision-based pose and velocity estimation scheme utilizing both the continuous and discrete forms of the Euclidean homography matrix is fused with inertial and optical encoder measurements to estimate the pose, orientation, and velocity of the robot and ensure accurate localization and control signals. A depth estimation system is integrated in order to overcome the loss of scale inherent in vision-based estimation. A path following control system is introduced that is capable of guiding the robot along a designated curve. Stability analysis is provided for the control system and experimental results are presented that prove the combined localization and control system performs with high accuracy.

An Extended Kalman Filter to Estimate Human Gait Parameters and Walking Distance.

In this work, we present a novel method to estimate joint angles and distance traveled by a human while walking. We model the human leg as a two-link revolute robot. Inertial measurement sensors placed on the thigh and shin provide the required measurement inputs. The model and inputs are then used to estimate the desired state parameters associated with forward motion using an extended Kalman filter (EKF). Experimental results with subjects walking in a straight line show that distance walked can be measured with accuracy comparable to a state of the art motion tracking systems. The EKF had an average RMSE of 7 cm over the trials with an average accuracy of greater than 97% for linear displacement.

Homography-based control scheme for mobile robots with nonholonomic and field-of-view constraints.

In this paper, we present a visual servo controller that effects optimal paths for a nonholonomic differential drive robot with field-of-view constraints imposed by the vision system. The control scheme relies on the computation of homographies between current and goal images, but unlike previous homography-based methods, it does not use the homography to compute estimates of pose parameters. Instead, the control laws are directly expressed in terms of individual entries in the homography matrix. In particular, we develop individual control laws for the three path classes that define the language of optimal paths: rotations, straight-line segments, and logarithmic spirals. These control laws, as well as the switching conditions that define how to sequence path segments, are defined in terms of the entries of homography matrices. The selection of the corresponding control law requires the homography decomposition before starting the navigation. We provide a controllability and stability analysis for our system and give experimental results.