No abstract

No abstract

Team ViGIR and Team Hector participated in the DARPA Robotics Challenge (DRC) Finals, held June 2015 in Pomona, California, along with 21 other teams from around the world. Both teams competed using the same high‐level software, in conjunction with independently developed low‐level software specific to their humanoid robots. On the basis of previous work on operator‐centric manipulation control at the level of affordances, we developed an approach that allows one or more human operators to share control authority with a high‐level behavior controller. This collaborative autonomy decreases the completion time of manipulation tasks, increases the reliability of the human‐robot team, and allows the operators to adjust the robotic system's autonomy on‐the‐fly. This article discusses the technical challenges we faced and overcame during our efforts to allow the human operators to interact with the robotic system at a higher level of abstraction and share control authority with it. We introduce and evaluate the proposed approach in the context of our two teams' participation in the DRC Finals. We also present additional, systematic experiments conducted in the lab afterward. Finally, we present a discussion about the lessons learned while transitioning between operator‐centered manipulation control and behavior‐centered manipulation control during competition.

of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

No abstract

This paper presents an approach for the multiagent navigation and conflict resolution problem, that considers the issue of performance. We present a decentralized predictive navigation scheme that combines the Decentralized Navigation Functions methodology with the Model Predictive Control (MPC) framework while preserving the former's collision avoidance and convergence guarantees. Aircrafts flying at constant altitude are modeled as unicycles. Performance criteria are encoded in a cost functional. Due to decentralization, each agent does not take into account the decisions of others in the control law calculation, resulting in performance discrepancies. Therefore we employ event-triggered executions in our scheme. The improved performance is demonstrated through simulations.

No abstract

No abstract