Cooperative adaptive cruise control (CACC) systems have the potential to increase traffic throughput by allowing smaller headway between vehicles and moving vehicles safely in a platoon at a harmonized speed. CACC systems have been attracting significant attention from both academia and industry since connectivity between vehicles will become mandatory for new vehicles in the USA in the near future. In this paper, we review three basic and important aspects of CACC systems: communications, driver characteristics, and controls to identify the most challenging issues for their real-world deployment. Different routing protocols that support the data communication requirements between vehicles in the CACC platoon are reviewed. Promising and suitable protocols are identified. Driver characteristics related issues, such as how to keep drivers engaged in driving tasks during CACC operations, are discussed. To achieve mass acceptance, the control design needs to depict real-world traffic variability such as communication effects, driver behavior, and traffic composition. Thus, this paper also discusses the issues that existing CACC control modules face when considering close to ideal driving conditions.

A Review of Communication, Driver Characteristics, and Controls Aspects of Cooperative Adaptive Cruise Control (CACC)

Symbiotic human-robot collaborative assembly

https://www.mdpi.com/2218-6581/8/4/100/pdf

Human–Robot Collaboration in Manufacturing Applications: A Review

Cobot programming for collaborative industrial tasks: An overview

Human–robot collaboration is a main technology of Industry 4.0 and is currently changing the shop floor of manufacturing companies. Collaborative robots are innovative industrial technologies introduced to help operators to perform manual activities in so called cyber-physical production systems and combine human inimitable abilities with smart machines strengths. Occupational health and safety criteria are of crucial importance in the implementation of collaborative robotics. Therefore, it is necessary to assess the state of the art for the design of safe and ergonomic collaborative robotic workcells. Emerging research fields beyond the state of the art are also of special interest. To achieve this goal this paper uses a systematic literature review methodology to review recent technical scientific bibliography and to identify current and future research fields. Main research themes addressed in the recent scientific literature regarding safety and ergonomics (or human factors) for industrial collaborative robotics were identified and categorized. The emerging research challenges and research fields were identified and analyzed based on the development of publications over time (annual growth).

Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review

Recent emergence of safe, lightweight, and flexible robots has opened a new realm for human–robot collaboration in manufacturing. Utilizing such robots with the new human–robot interaction (HRI) functionality to interact closely and effectively with a human co-worker, we propose a novel framework for integrating HRI factors (both physical and social interactions) into the robot motion controller for human–robot collaborative assembly tasks in a manufacturing hybrid cell. To meet human physical demands in such assembly tasks, an optimal control problem is formulated for physical HRI (pHRI)-based robot motion control to keep pace with human motion progress. We further augment social HRI (sHRI) into the framework by considering a computational model of the human worker’s trust in his/her robot partner as well as robot facial expressions. The human worker’s trust in robot is computed and used as a metric for path selection as well as a constraint in the optimal control problem. Robot facial expression is displayed for providing additional visual feedbacks to the human worker. We evaluate the proposed framework by designing a robotic experimental testbed and conducting a comprehensive study with a human-in-the-loop. Results of this paper show that compared to the manual adjustments of robot velocity, an autonomous controller based on pHRI, pHRI and sHRI with trust, or pHRI and sHRI with trust, and emotion result in 34%, 39%, and 44% decrease in human workload and 21%, 32%, and 60% increase in robot’s usability, respectively. Compared to the manual framework, human trust in robot increases by 38% and 42%, respectively, in the latter two autonomous frameworks. Moreover, the overall efficiency in terms of assembly time remains the same. Note to Practitioners —Conventionally, industrial robots are used to perform repetitive tasks in human-free cages with minimal HRI for safety concerns. Thanks to the new safety and flexibility functions embedded in human-friendly manufacturing robots, humans and robots can now collaborate closely with each other accomplishing the tasks that were previously done by human workers solely. However, existing criteria for designing robot controllers need to be modified by considering the human workers’ demands since the performance of a human worker would vary due to factors such as individual strength, working pattern, and interaction with the robot. To address this problem, we propose a novel framework that integrates HRI factors in controlling the motion of a robot for the collaborative assembly tasks. Within this framework, the speed of robot can be controlled such that its motion progress synchronizes with that of the human during the task to improve pHRI. Moreover, for better sHRI, human trust in robot is calculated and used to select robot path and modify its speed control. Furthermore, we dynamically change the robot facial expression to provide visual feedbacks for performance and safety concerns. The results of the experimental study show that integrating both pHRI and sHRI in the robot controller leads to a significant drop of human perceived workload and considerable increase of robot usability and human trust in robot while the overall efficiency in terms of assembly time remains intact. For practical utilization in assembly plants, sensory devices for tracking the human motion are required. The robot is also required to have built-in safety functions that reduce the impact of possible collisions between the human and the robot. We believe that this paper addresses how the implementation of human-friendly robots in the manufacturing environments can improve HRI and reduce workload.

Collaborative Assembly in Hybrid Manufacturing Cells: An Integrated Framework for Human–Robot Interaction

Human-Robot Collaboration (HRC) on the factory floor has opened a new realm of manufacturing in real-world settings. In such applications, a human and robot work together with each other as coworkers while HRC plays a critical role in safety, productivity, and flexibility. In particular, human-robot trust determines his/her acceptance and hence allocation of autonomy to a robot, which alter the overall task efficiency and human workload. Inspired by well-known human factors research, we develop a time-series trust model for human-robot collaboration tasks, which is a function of prior trust, robot performance, and human performance. The robot performance is evaluated by its flexibility to keep pace with the human coworker and is molded as the difference between human and robot speed. The human performance in doing physical tasks is directly related to his/her muscle fatigue level. We use the muscle fatigue and recovery dynamics to capture the fatigue level of the human body when performing repetitive kinesthetic tasks, which are typical types of human motions in manufacturing. The robot speed can be controlled in three different modes: manually by the associate, autonomously through robust intelligence algorithms, or collaboratively by the combination of manual and autonomous inputs. We first simulate a typical 9-h work day for human robot collaborative tasks and implement the proposed trust model and the three control schemes. Furthermore, we experimentally validate our model and control schemes by conducting a series of human-in-the-loop experiments using the Rethink Robotics Baxter robot.

Modeling and Control of Trust in Human-Robot Collaborative Manufacturing

We develop a one human-one robot hybrid cell for collaborative assembly in manufacturing. The selected task is to assemble a few LEGO parts into a final assembled product following specified instructions and sequence in collaboration between the human and the robot. We develop a two-level feedforward optimization strategy that determines the optimal subtask allocation between the human and the robot for the selected assembly before the assembly starts. We derive dynamics models for human’s trust in the robot and the robot’s trust in the human for the assembly and estimate the trusts. The aim is to maintain satisfactory trust levels between the human and the robot through the application of the optimal subtask allocation. Again, subtask re-allocation is proposed to regain trusts if the trusts reduce to below the specified levels. Furthermore, it is hypothesized that fluctuations in human’s trust in the robot may cause fluctuations in human’s speeds and the human may appreciate if the robot adjusts its speeds with changes in human speeds. Hence, trust-based Model Predictive Control (MPC) is proposed to minimize the variations between human and robot speeds and to maximize the trusts. Experiment results prove the effectiveness of the hybrid cell, the feedforward optimal subtask allocation and of the trust-based MPC. The results also show that the overall assembly performance can be enhanced and the performance status can be monitored through a single dynamic parameter, i.e. the trust.Copyright © 2015 by ASME

Trust-Based Optimal Subtask Allocation and Model Predictive Control for Human-Robot Collaborative Assembly in Manufacturing

Recently, lightweight and flexible robots have been introduced for human-robot collaborative manufacturing. However, most of the research in this field focuses on the physical aspects of the interaction only. Nevertheless, psychological and social impact of human-robot collaboration (HRC) on manufacturing needs to be addressed and embedded in the design as well such that the robot actions become acceptable and comfortable for the human. Motivated by this need, we propose an integrated physical and social HRC framework for assembly tasks in a hybrid manufacturing cell. To address human physical demands, the robot motion control will be developed to keep pace with human motion during the task. Furthermore, psychological human factors will also be taken into account into the robot motion control for better HRC. More specifically, we consider a computational model of a human worker's trust in his/her robot partner and use the trust evaluation as a constraint in an optimal control problem. Finally, we run a pilot study to test our proposed framework.

An integrated framework for human-robot collaborative assembly in hybrid manufacturing cells

A trust-based switching impedance control strategy for human-robot cooperative manipulation is proposed. The robot behavior switches between the proactive and reactive modes based on the estimated trust of human in robot. The robot estimates the human desired trajectory. A history-based probabilistic trust model for human-robot cooperative manipulation tasks is proposed. Trust is modeled as a dynamic Bayesian network. In the proactive mode, the robot estimates the human desired trajectory and plans accordingly while in the reactive mode, the robot only reacts to the human input. A simulation of the trust-based switching impedance control strategy is presented.

Behzad Sadrfaridpour

Papers

Collaborative Assembly in Hybrid Manufacturing Cells: An Integrated Framework for Human–Robot Interaction

Modeling and Control of Trust in Human-Robot Collaborative Manufacturing

Trust-Based Optimal Subtask Allocation and Model Predictive Control for Human-Robot Collaborative Assembly in Manufacturing

An integrated framework for human-robot collaborative assembly in hybrid manufacturing cells

Trust-Based Impedance Control Strategy for Human-Robot Cooperative Manipulation