26,538 research outputs found
Sampling-Based Multi-Robot Exploration
International audienceThis paper presents a new approach for collaborative multi-robot planning issues. The main problem that arises from multi-robot exploration is waiting situations. We consider that such problem involves two or more autonomous robots in an unknown environment. The mission objective is to explore the entire map, while trying to minimize its executing time. Moreover if each robot uses the same topological graph, then it uses the same exploration path that makes waiting situations arising. To solve this problem, we propose a new approach in this paper based on sampling iteratively maps to allow interactive multi-robot exploration. Our approach has been implemented in simulation and the experiments demonstrate that the overall completion time of an exploration task can be significantly reduced by our sampling-based method
Multi-AGV's Temporal Memory-based RRT Exploration in Unknown Environment
With the increasing need for multi-robot for exploring the unknown region in
a challenging environment, efficient collaborative exploration strategies are
needed for achieving such feat. A frontier-based Rapidly-Exploring Random Tree
(RRT) exploration can be deployed to explore an unknown environment. However,
its' greedy behavior causes multiple robots to explore the region with the
highest revenue, which leads to massive overlapping in exploration process. To
address this issue, we present a temporal memory-based RRT (TM-RRT) exploration
strategy for multi-robot to perform robust exploration in an unknown
environment. It computes adaptive duration for each frontier assigned and
calculates the frontier's revenue based on the relative position of each robot.
In addition, each robot is equipped with a memory consisting of frontier
assigned and share among fleets to prevent repeating assignment of same
frontier. Through both simulation and actual deployment, we have shown the
robustness of TM-RRT exploration strategy by completing the exploration in a
25.0m x 54.0m (1350.0m2) area, while the conventional RRT exploration strategy
falls short.Comment: 8 pages, 10 Figure
Collaborative Human-Robot Exploration via Implicit Coordination
This paper develops a methodology for collaborative human-robot exploration
that leverages implicit coordination. Most autonomous single- and multi-robot
exploration systems require a remote operator to provide explicit guidance to
the robotic team. Few works consider how to embed the human partner alongside
robots to provide guidance in the field. A remaining challenge for
collaborative human-robot exploration is efficient communication of goals from
the human to the robot. In this paper we develop a methodology that implicitly
communicates a region of interest from a helmet-mounted depth camera on the
human's head to the robot and an information gain-based exploration objective
that biases motion planning within the viewpoint provided by the human. The
result is an aerial system that safely accesses regions of interest that may
not be immediately viewable or reachable by the human. The approach is
evaluated in simulation and with hardware experiments in a motion capture
arena. Videos of the simulation and hardware experiments are available at:
https://youtu.be/7jgkBpVFIoE.Comment: 7 pages, 10 figures, to appear in the 2022 IEEE International
Symposium on Safety, Security, and Rescue Robotics (SSRR
Consensus-based Resource Scheduling for Collaborative Multi-Robot Tasks
We propose integrating the edge-computing paradigm into the multi-robot
collaborative scheduling to maximize resource utilization for complex
collaborative tasks, which many robots must perform together. Examples include
collaborative map-merging to produce a live global map during exploration
instead of traditional approaches that schedule tasks on centralized
cloud-based systems to facilitate computing. Our decentralized approach to a
consensus-based scheduling strategy benefits a multi-robot-edge collaboration
system by adapting to dynamic computation needs and communication-changing
statistics as the system tries to optimize resources while maintaining overall
performance objectives. Before collaborative task offloading, continuous
device, and network profiling are performed at the computing resources, and the
distributed scheduling scheme then selects the resource with maximum utility
derived using a utility maximization approach. Thorough evaluations with and
without edge servers on simulation and real-world multi-robot systems
demonstrate that a lower task latency, a large throughput gain, and better
frame rate processing may be achieved compared to the conventional edge-based
systems.Comment: Accepted to the IEEE Intelligent Robotic Computing (IRC) Conference
202
Cart-O-matic project : autonomous and collaborative multi-robot localization, exploration and mapping
International audienceThe aim of the Cart-O-matic project was to design and build a multi-robot system able to autonomously map an unknown building. This work has been done in the framework of a French robotics contest called Defi CAROTTE organized by the General Delegation for Armaments (DGA) and the French National Research Agency (ANR). The scientific issues of this project deal with Simultaneous Localization And Mapping (SLAM), multi-robot collaboration and object recognition. In this paper, we will mainly focussed on the two first topics : after a general introduction, we will briefly describe the innovative simultaneous localization and mapping algorithm used during the competition. We will next explain how this algorithm can deal with multi-robots systems and 3D mapping. The next part of the paper will be dedicated to the multi-robot pathplanning and exploration strategy. The last section will illustrate the results with 2D and 3D maps, collaborative exploration strategies and example of planned trajectories
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
An Auto-Adaptive Multi-Objective Strategy for Multi-Robot Exploration of Constrained-Communication Environments
The exploration problem is a fundamental subject in autonomous mobile robotics that deals with achieving the complete coverage of a previously unknown environment. There are several scenarios where completing exploration of a zone is a main part of the mission. Due to the efficiency and robustness brought by the multi-robot systems, exploration is usually done cooperatively. Wireless communication plays an important role in collaborative multi-robot strategies. Unfortunately, the assumption of stable communication and end-to-end connectivity may be easily compromised in real scenarios. In this paper, a novel auto-adaptive multi-objective strategy is followed to support the selection of tasks regarding both exploration performance and connectivity level. Compared with others, the proposed approach shows effectiveness and flexibility to tackle the multi-robot exploration problem, being capable of decreasing the last of disconnection periods without noticeable degradation of the completion exploration time
Search and Rescue under the Forest Canopy using Multiple UAVs
We present a multi-robot system for GPS-denied search and rescue under the
forest canopy. Forests are particularly challenging environments for
collaborative exploration and mapping, in large part due to the existence of
severe perceptual aliasing which hinders reliable loop closure detection for
mutual localization and map fusion. Our proposed system features unmanned
aerial vehicles (UAVs) that perform onboard sensing, estimation, and planning.
When communication is available, each UAV transmits compressed tree-based
submaps to a central ground station for collaborative simultaneous localization
and mapping (CSLAM). To overcome high measurement noise and perceptual
aliasing, we use the local configuration of a group of trees as a distinctive
feature for robust loop closure detection. Furthermore, we propose a novel
procedure based on cycle consistent multiway matching to recover from incorrect
pairwise data associations. The returned global data association is guaranteed
to be cycle consistent, and is shown to improve both precision and recall
compared to the input pairwise associations. The proposed multi-UAV system is
validated both in simulation and during real-world collaborative exploration
missions at NASA Langley Research Center.Comment: IJRR revisio
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