Optimization-Driven Controller Design for a High-Performance Electro-Hydrostatic Asymmetric Actuator Operating in All Quadrants

This paper presents an optimization-driven controller design for smooth and accurate position control of a newly developed single-rod electro-hydrostatic actuator (EHA). The design approach uses logically guided iterative runs of the EHA to determine the optimal gain and poles' locations of a simple, yet effective low-bandwidth controller. The optimization algorithm used in the paper is the globalized bounded Nelder-Mead algorithm with deterministic restarts for improved globalization and lower numerical cost. The design also incorporates a pre-filter to ensure minimum jerk in the system's step input response in the beginning and while approaching steady-state. The step response of the filter is a 7th-degree polynomial curve that ensures the minimum change in acceleration. Experimental results reveal that the addition of the proposed pre-filter reduces jerk in the system by up to 90%. Results also indicate that the controller performs very well in all quadrants with external load uncertainty of up to 367 kg and thus proves the effectiveness of the design approach.

Integrated jerk as an indicator of affinity for artificial agent kinematics: laptop and virtual reality experiments involving index finger motion during two-digit grasping

Uncanny valley research has shown that human likeness is an important consideration when designing artificial agents. It has separately been shown that artificial agents exhibiting human-like kinematics can elicit positive perceptual responses. However the kinematic characteristics underlying that perception have not been elucidated. This paper proposes kinematic jerk amplitude as a candidate metric for kinematic human likeness, and aims to determine whether a perceptual optimum exists over a range of jerk values. We created minimum-jerk two-digit grasp kinematics in a prosthetic hand model, then added different amplitudes of temporally smooth noise to yield a variety of animations involving different total jerk levels, ranging from maximally smooth to highly jerky. Subjects indicated their perceptual affinity for these animations by simultaneously viewing two different animations side-by-side, first using a laptop, then separately within a virtual reality (VR) environment. Results suggest that (a) subjects generally preferred smoother kinematics, (b) subjects exhibited a small preference for rougher-than minimum jerk kinematics in the laptop experiment, and that (c) the preference for rougher-than minimum-jerk kinematics was amplified in the VR experiment. These results suggest that non-maximally smooth kinematics may be perceptually optimal in robots and other artificial agents.

Powered knee and ankle prosthesis with indirect volitional swing control enables level-ground walking and crossing over obstacles

Powered prostheses aim to mimic the missing biological limb with controllers that are finely tuned to replicate the nominal gait pattern of non-amputee individuals. Unfortunately, this control approach poses a problem with real-world ambulation, which includes tasks such as crossing over obstacles, where the prosthesis trajectory must be modified to provide adequate foot clearance and ensure timely foot placement. Here, we show an indirect volitional control approach that enables prosthesis users to walk at different speeds while smoothly and continuously crossing over obstacles of different sizes without explicit classification of the environment. At the high level, the proposed controller relies on a heuristic algorithm to continuously change the maximum knee flexion angle and the swing duration in harmony with the user’s residual limb. At the low level, minimum-jerk planning is used to continuously adapt the swing trajectory while maximizing smoothness. Experiments with three individuals with above-knee amputation show that the proposed control approach allows for volitional control of foot clearance, which is necessary to negotiate environmental barriers. Our study suggests that a powered prosthesis controller with intrinsic, volitional adaptability may provide prosthesis users with functionality that is not currently available, facilitating real-world ambulation.

Reward-based invigoration of sequential reaching

Seeking reward is a powerful tool for shaping human behaviour. While it has been demonstrated that reward invigorates performance of simple movements, its effect on more complex sequential actions is less clear. In addition, it is unknown why reward-based improvements for discrete actions are transient, i.e. performance gains are lost once reward is removed, but appear long lasting for sequential actions. We show across three experiments that reward invigorates sequential reaching performance. Driven by a reward-based increase in speed, movements also exhibited greater coarticulation, smoothness and a closer alignment to a minimum jerk trajectory. Critically, these performance gains were maintained across multiple days even after the removal of reward. We propose that coarticulation, the blending together of sub-movements into a single continuous action, provides a mechanism by which reward can invigorate sequential performance whilst also increasing efficiency. This change in efficiency appears essential for the retention of reward-based improvements in motor behaviour.

Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks

This paper aims at developing a convolutional neural network (CNN)-based tool that can automatically detect the left-turning vehicles (right-hand traffic rule) at signalized intersections and extract their trajectories from a recorded video. The proposed tool uses a region-based CNN trained over a limited number of video frames to detect moving vehicles. Kalman filters are then used to track the detected vehicles and extract their trajectories. The proposed tool achieved an acceptable accuracy level when verified against the manually extracted trajectories, with an average error of 16.5 cm. Furthermore, the trajectories extracted using the proposed vehicle tracking method were used to demonstrate the applicability of the minimum-jerk principle to reproduce variations in the vehicles’ paths. The effort presented in this paper can be regarded as a way forward toward maximizing the potential use of deep learning in traffic safety applications.