150,909 research outputs found
Adaptive self-management of teams of autonomous vehicles
Unmanned Autonomous Vehicles (UAVs) are increasingly deployed for missions that are deemed dangerous or impractical to perform by humans in many military and disaster scenarios. Collaborating UAVs in a team form a Self- Managed Cell (SMC) with at least one commander. UAVs in an SMC may need to operate independently or in sub- groups, out of contact with the commander and the rest of the team in order to perform specific tasks, but must still be able to eventually synchronise state information. The SMC must also cope with intermittent and permanent communication failures as well permanent UAV failures. This paper describes a failure management scheme that copes with both communication link and UAV failures, which may result in temporary disjoint sub-networks within the SMC. A communication management protocol is proposed to control UAVs performing disconnected individual operations, while maintaining the SMCs structure by trying to ensure that all members of the mission regardless of destination or task, can communicate by moving UAVs to act as relays or by allowing the UAVs to rendezvous at intermittent intervals. Copyright 2008 ACM.Accepted versio
How Push-To-Talk Makes Talk Less Pushy
This paper presents an exploratory study of college-age students using
two-way, push-to-talk cellular radios. We describe the observed and reported
use of cellular radio by the participants. We discuss how the half-duplex,
lightweight cellular radio communication was associated with reduced
interactional commitment, which meant the cellular radios could be used for a
wide range of conversation styles. One such style, intermittent conversation,
is characterized by response delays. Intermittent conversation is surprising in
an audio medium, since it is typically associated with textual media such as
instant messaging. We present design implications of our findings.Comment: 10 page
Cooperative learning in multi-agent systems from intermittent measurements
Motivated by the problem of tracking a direction in a decentralized way, we
consider the general problem of cooperative learning in multi-agent systems
with time-varying connectivity and intermittent measurements. We propose a
distributed learning protocol capable of learning an unknown vector from
noisy measurements made independently by autonomous nodes. Our protocol is
completely distributed and able to cope with the time-varying, unpredictable,
and noisy nature of inter-agent communication, and intermittent noisy
measurements of . Our main result bounds the learning speed of our
protocol in terms of the size and combinatorial features of the (time-varying)
networks connecting the nodes
Intermittent Connectivity for Exploration in Communication-Constrained Multi-Agent Systems
Motivated by exploration of communication-constrained underground environments using robot teams, we study the problem of planning for intermittent connectivity in multi-agent systems. We propose a novel concept of information-consistency to handle situations where the plan is not initially known by all agents, and suggest an integer linear program for synthesizing information-consistent plans that also achieve auxiliary goals. Furthermore, inspired by network flow problems we propose a novel way to pose connectivity constraints that scales much better than previous methods. In the second part of the paper we apply these results in an exploration setting, and propose a clustering method that separates a large exploration problem into smaller problems that can be solved independently. We demonstrate how the resulting exploration algorithm is able to coordinate a team of ten agents to explore a large environment
ACHORD: communication-aware multi-robot coordination with intermittent connectivity
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksCommunication is an important capability for multi-robot exploration because (1) inter-robot communication (comms) improves coverage efficiency and (2) robot-to-base comms improves situational awareness. Exploring comms-restricted (e.g., subterranean) environments requires a multi-robot system to tolerate and anticipate intermittent connectivity, and to carefully consider comms requirements, otherwise mission-critical data may be lost. In this paper, we describe and analyze ACHORD (Autonomous & Collaborative High-Bandwidth Operations with Radio Droppables), a multi-layer networking solution which tightly co-designs the network architecture and high-level decision-making for improved comms. ACHORD provides bandwidth prioritization and timely and reliable data transfer despite intermittent connectivity. Furthermore, it exposes low-layer networking metrics to the application layer to enable robots to autonomously monitor, map, and extend the network via droppable radios, as well as restore connectivity to improve collaborative exploration. We evaluate our solution with respect to the comms performance in several challenging underground environments including the DARPA SubT Finals competition environment. Our findings support the use of data stratification and flow control to improve bandwidth-usage.Peer ReviewedPostprint (author's final draft
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