scholarly journals Study of the Impact of Delay on Human Remote Navigators with Application to Receding Horizon Control

2012 ◽  
Vol 3 (2) ◽  
Author(s):  
Chad R. Burns ◽  
Ranxiao F. Wang ◽  
Dušan M. Stipanović
Keyword(s):  
Human Behavior ◽  
Nonlinear Model ◽  
Autonomous Vehicles ◽  
Human Performance ◽  
Receding Horizon Control ◽  
Delay Compensation ◽  
Receding Horizon ◽  
Remote Navigation ◽  
Human In The Loop ◽  
The Impact

AbstractThis paper examines the impact of delays on human performance and human strategies when remotely navigating autonomous vehicles, and develops a robust human inspired delay compensation. Vehicles chosen for the study are ground autonomous vehicles which are allowed to stop, providing an instrumental feature that enables it to capture some important human behavior. The effects of delay on human behavior when remotely navigating autonomous vehicles have been captured by a nonlinear model predictive (also known as receding horizon) controller. This study provides some insights into designing human in-the-loop systems for remote navigation of autonomous vehicles when the delays are not negligible. We offer a human inspired strategy for dealing with delay in a fully autonomous receding horizon controller which we show to be safe and convergent for bounded delays.

scholarly journals The Impact of Increasing Autonomy on Training Requirements in a UAV Supervisory Control Task

2019 ◽  
Vol 13 (4) ◽  
pp. 295-309 ◽  
Author(s):  
Mary Cummings ◽  
Lixiao Huang ◽  
Haibei Zhu ◽  
Daniel Finkelstein ◽  
Ran Wei
Keyword(s):  
Supervisory Control ◽  
Human Performance ◽  
Search Time ◽  
Recognition Algorithm ◽  
Control Task ◽  
Training Requirements ◽  
Human In The Loop ◽  
Search Tasks ◽  
Automation Control ◽  
The Impact

A common assumption across many industries is that inserting advanced autonomy can often replace humans for low-level tasks, with cost reduction benefits. However, humans are often only partially replaced and moved into a supervisory capacity with reduced training. It is not clear how this shift from human to automation control and subsequent training reduction influences human performance, errors, and a tendency toward automation bias. To this end, a study was conducted to determine whether adding autonomy and skipping skill-based training could influence performance in a supervisory control task. In the human-in-the-loop experiment, operators performed unmanned aerial vehicle (UAV) search tasks with varying degrees of autonomy and training. At the lowest level of autonomy, operators searched images and, at the highest level, an automated target recognition algorithm presented its best estimate of a possible target, occasionally incorrectly. Results were mixed, with search time not affected by skill-based training. However, novices with skill-based training and automated target search misclassified more targets, suggesting a propensity toward automation bias. More experienced operators had significantly fewer misclassifications when the autonomy erred. A descriptive machine learning model in the form of a hidden Markov model also provided new insights for improved training protocols and interventional technologies.

scholarly journals Receding Horizon Control Using Graph Search for Multi-Agent Trajectory Planning

2021 ◽  
Author(s):  
Patrick Scheffe ◽  
Matheus Vitor de Andrade Pedrosa ◽  
Kathrin Flaßkamp ◽  
Bassam Alrifaee
Keyword(s):  
Trajectory Planning ◽  
Autonomous Vehicles ◽  
Optimization Problems ◽  
Receding Horizon Control ◽  
Graph Search ◽  
Finite State Automaton ◽  
Receding Horizon ◽  
Nonconvex Optimization Problems ◽  
Control Approach ◽  
Finite State

<pre>It is hard to find the global optimum to general nonlinear, nonconvex optimization problems in reasonable time. This paper presents a method to transfer the receding horizon control approach, where nonlinear, nonconvex optimization problems are considered, into graph-search problems. Specifically, systems with symmetries are considered to transfer system dynamics into a finite state automaton. In contrast to traditional graph-search approaches where the search continues until the goal vertex is found, the transfer of a receding horizon control approach to graph-search problems presented in this paper allows to solve them in real-time. We proof that the solutions are recursively feasible by restricting the graph search to end in accepting states of the underlying finite state automaton. The approach is applied to trajectory planning for multiple networked and autonomous vehicles. We evaluate its effectiveness in simulation as well as in experiments in the Cyber-Physical Mobility Lab, an open source platform for networked and autonomous vehicles. We show real-time capable trajectory planning with collision avoidance in experiments on off-the-shelf hardware and code in MATLAB for two vehicles.</pre>

Cooperative Search of Autonomous Vehicles for Unknown Targets

2013 ◽  
Vol 281 ◽  
pp. 3-9 ◽  
Author(s):  
Sheng Qing Yang ◽  
Jian Qiao Yu ◽  
Si Yu Zhang
Keyword(s):  
Obstacle Avoidance ◽  
Hybrid Method ◽  
Autonomous Vehicles ◽  
Receding Horizon Control ◽  
Search Process ◽  
Receding Horizon ◽  
Cooperative Search ◽  
New Approach ◽  
Process Simulations ◽  
Curve Form

Motivated by recent research on cooperative search of autonomous vehicles, a new approach for searching unknown targets is introduced in this paper. The unknown targets are assumed to be static. ZAMBONI search in spiral curve form is considered to implement the cooperation of vehicles. Algorithms that based on geometry underlying search process are discussed to make vehicles act in the spiral curves form. The receding horizon control is introduced for obstacle avoidance which can result in a feasible trajectory during the search process. Simulations of the hybrid method based on ZAMBONI search and receding horizon control show promising results.

Measuring the Impact of Advanced Technologies and Reorganization on Human Performance in a Combat Information Center

2000 ◽  
Vol 44 (37) ◽  
pp. 642-645 ◽  
Author(s):  
Jared T. Freeman ◽  
Gwendolyn E. Campbell ◽  
Greg Hildebrand
Keyword(s):  
Human Behavior ◽  
Team Performance ◽  
Preliminary Data ◽  
Human Performance ◽  
Novel Technology ◽  
Advanced Technologies ◽  
Information Center ◽  
And Control ◽  
The Impact ◽  
The U.S

Systematically evaluating the impact of novel technology and organizational structure on team performance is a complex, multidimensional task. We define several of these dimensions that are of particular interest in the development of new command and control teams and technologies for the U.S. Navy. In addition, we describe an approach to stimulating and measuring human behavior on these dimensions, and an experiment in which this approach is applied. Preliminary data are presented.

scholarly journals Receding Horizon Control Using Graph Search for Multi-Agent Trajectory Planning

2021 ◽  
Author(s):  
Patrick Scheffe ◽  
Matheus Vitor de Andrade Pedrosa ◽  
Kathrin Flaßkamp ◽  
Bassam Alrifaee
Keyword(s):  
Trajectory Planning ◽  
Autonomous Vehicles ◽  
Optimization Problems ◽  
Receding Horizon Control ◽  
Graph Search ◽  
Finite State Automaton ◽  
Receding Horizon ◽  
Nonconvex Optimization Problems ◽  
Control Approach ◽  
Finite State

<pre>It is hard to find the global optimum to general nonlinear, nonconvex optimization problems in reasonable time. This paper presents a method to transfer the receding horizon control approach, where nonlinear, nonconvex optimization problems are considered, into graph-search problems. Specifically, systems with symmetries are considered to transfer system dynamics into a finite state automaton. In contrast to traditional graph-search approaches where the search continues until the goal vertex is found, the transfer of a receding horizon control approach to graph-search problems presented in this paper allows to solve them in real-time. We proof that the solutions are recursively feasible by restricting the graph search to end in accepting states of the underlying finite state automaton. The approach is applied to trajectory planning for multiple networked and autonomous vehicles. We evaluate its effectiveness in simulation as well as in experiments in the Cyber-Physical Mobility Lab, an open source platform for networked and autonomous vehicles. We show real-time capable trajectory planning with collision avoidance in experiments on off-the-shelf hardware and code in MATLAB for two vehicles.</pre>

scholarly journals Human Behavior Models for Agents in Simulators and Games: Part I: Enabling Science with PMFserv

2006 ◽  
Vol 15 (2) ◽  
pp. 139-162 ◽  
Author(s):  
Barry G. Silverman ◽  
Michael Johns ◽  
Jason Cornwell ◽  
Kevin O'Brien
Keyword(s):  
Cultural Values ◽  
Human Behavior ◽  
Intelligent Agents ◽  
Human Performance ◽  
Companion Paper ◽  
The State ◽  
Lessons Learned ◽  
Behavior Modeling ◽  
Value Systems ◽  
The Impact

This paper focuses on challenges to improving the realism of socially intelligent agents and attempts to reflect the state of the art in human behavior modeling with particular attention to the impact of personality/cultural values and affect as well as biology/stress upon individual coping and group decision making. The first section offers an assessment of the state of the practice and of the need to integrate valid human performance moderator functions (PMFs) from traditionally separated subfields of the behavioral literature. The second section pursues this goal by postulating a unifying architecture and principles for integrating existing PMF theories and models. It also illustrates a PMF testbed called PMFserv created for implementating and studying how PMFs may contribute to such an architecture. To date it interconnects versions of PMFs on physiology and stress; personality, cultural and emotive processes (Cognitive Appraisal-OCC, value systems); perception (Gibsonian affordance); social processes (relations, identity, trust, nested intentionality); and cognition (affect- and stress-augmented decision theory, bounded rationality). The third section summarizes several usage case studies (asymmetric warfare, civil unrest, and political leaders) and concludes with lessons learned. Implementing and interoperating this broad collection of PMFs helps to open the agenda for research on syntheses that can help the field reach a greater level of maturity. The companion paper, Part II, presents a case study in using PMFserv for rapid scenario composability and realistic agent behavior.

Sign in / Sign up

Export Citation Format

Share Document