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

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.

Author(s):  
Chad R. Burns ◽  
Ranxiao F. Wang ◽  
Dušan M. Stipanović

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.


2021 ◽  
Author(s):  
Patrick Scheffe ◽  
Matheus Vitor de Andrade Pedrosa ◽  
Kathrin Flaßkamp ◽  
Bassam Alrifaee

<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>


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