270 research outputs found
VIR-SLAM: Visual, Inertial, and Ranging SLAM for single and multi-robot systems
Monocular cameras coupled with inertial measurements generally give high
performance visual inertial odometry. However, drift can be significant with
long trajectories, especially when the environment is visually challenging. In
this paper, we propose a system that leverages ultra-wideband ranging with one
static anchor placed in the environment to correct the accumulated error
whenever the anchor is visible. We also use this setup for collaborative SLAM:
different robots use mutual ranging (when available) and the common anchor to
estimate the transformation between each other, facilitating map fusion Our
system consists of two modules: a double layer ranging, visual, and inertial
odometry for single robots, and a transformation estimation module for
collaborative SLAM. We test our system on public datasets by simulating an
ultra-wideband sensor as well as on real robots. Experiments show our method
can outperform state-of-the-art visual-inertial odometry by more than 20%. For
visually challenging environments, our method works even the visual-inertial
odometry has significant drift Furthermore, we can compute the collaborative
SLAM transformation matrix at almost no extra computation cost
Accurate position tracking with a single UWB anchor
Accurate localization and tracking are a fundamental requirement for robotic
applications. Localization systems like GPS, optical tracking, simultaneous
localization and mapping (SLAM) are used for daily life activities, research,
and commercial applications. Ultra-wideband (UWB) technology provides another
venue to accurately locate devices both indoors and outdoors. In this paper, we
study a localization solution with a single UWB anchor, instead of the
traditional multi-anchor setup. Besides the challenge of a single UWB ranging
source, the only other sensor we require is a low-cost 9 DoF inertial
measurement unit (IMU). Under such a configuration, we propose continuous
monitoring of UWB range changes to estimate the robot speed when moving on a
line. Combining speed estimation with orientation estimation from the IMU
sensor, the system becomes temporally observable. We use an Extended Kalman
Filter (EKF) to estimate the pose of a robot. With our solution, we can
effectively correct the accumulated error and maintain accurate tracking of a
moving robot.Comment: Accepted by ICRA202
Accurate Localization with Ultra-Wideband Ranging for Multi-Robot Systems
RÉSUMÉ : Avec l’avancement de la technologie matérielle et logicielle, l’application de l’automatisation et de la robotique se développe rapidement. Les systèmes multi-robots sont particulièrement prometteurs en raison de leur grande efficacité et robustesse. De tels systèmes peuvent être utilisés pour aider les humains à effectuer efficacement des tâches dangereuses ou pénibles, telles que l’intervention en cas de catastrophe, l’exploration souterraine, etc. Pour déployer un système multi-robot dans un environnement sans GPS, la coordination des robots dans le système est un défi crucial. Chaque robot doit avoir une estimation précise de sa propre position pour permettre aux robots du système de collaborer pour la réalisation de leur tâche. Comme cette direction de recherche est relativement nouvelle, les approches existantes ne sont pas encore abouties. Elles consistent principalement en des systèmes centralisés qui reposent sur des signaux GPS. La dépendance sur un signal GPS limite l’application aux espaces extérieurs ouverts. De plus, les systèmes centralisés sont confrontés au risque d’un point de défaillance unique, qui limite la robustesse du système. Par ailleurs, un système centralisé n’est pas toujours approprié à une taille grandissante, comme lors d’ajout de nouveaux groupes de robots ou lors de la fusion de différents groupes. Par conséquent, une solution distribuée, décentralisée, et adaptée à de larges groupes de tailles variables pouvant produire une estimation et un suivi du positionnement des robots dans un environnement sans GPS est souhaitée. Dans ce travail, nous adoptons une stratégie descendante pour relever ces défis.----------ABSTRACT : With the advancement of hardware and software technology, the everyday applications of automation and robotics are developing rapidly. Multi-robot systems are particularly promising because of their high efficiency and robustness. Such systems can be used to assist humans in performing dangerous or strenuous tasks, such as disaster response, subterranean exploration, etc. To deploy a multi-robot system in an environment without a global positioning system (GPS), coordinating the robots in the system is a crucial challenge. Each robot needs to have the correct tracking of its own and its teammates positions to enable the robots to cooperate. Because this research direction is relatively new, there are not many mature methods: existing approaches are mainly centralized systems that rely on GPS signals. The dependence on GPS restricts the application to the outdoors or indoor spaces with expensive infrastructure. Centralized systems also face the risk of a single point of failure, which is not acceptable for critical systems. In addition, centralized systems can be hard to scale both statically and dynamically (e.g. adding new groups of robots or merging different groups). Therefore, a distributed and scalable solution with accurate positioning and tracking in a GPS-denied environment is desired. In this work, we follow a top-down strategy to address these challenges
Global stabilization for triangular formations under mixed distance and bearing constraints
This paper addresses the triangular formation control problem for a system of three agents under mixed distance and bearing constraints. The main challenge is to find a fully distributed control law for each agent to guarantee the global convergence towards a desired triangular formation. To solve this problem, we invoke the property that a triangle can be uniquely determined by the lengths of its two sides together with the magnitude of the corresponding included angle. Based on this feature, we design a class of control strategies, under which each agent is only responsible for a single control variable, i.e., a distance or an angle, such that the control laws can be implemented in local coordinate frames. The global convergence is shown by analyzing the dynamics of the closed-loop system in its cascade form. Then we discuss some extensions on more general formation shapes and give the quadrilateral formation as an example. Simulation results are provided to validate the effectiveness of the proposed control strategies
Robo-centric ESDF: A Fast and Accurate Whole-body Collision Evaluation Tool for Any-shape Robotic Planning
For letting mobile robots travel flexibly through complicated environments,
increasing attention has been paid to the whole-body collision evaluation. Most
existing works either opt for the conservative corridor-based methods that
impose strict requirements on the corridor generation, or ESDF-based methods
that suffer from high computational overhead. It is still a great challenge to
achieve fast and accurate whole-body collision evaluation. In this paper, we
propose a Robo-centric ESDF (RC-ESDF) that is pre-built in the robot body frame
and is capable of seamlessly applied to any-shape mobile robots, even for those
with non-convex shapes. RC-ESDF enjoys lazy collision evaluation, which retains
only the minimum information sufficient for whole-body safety constraint and
significantly speeds up trajectory optimization. Based on the analytical
gradients provided by RC-ESDF, we optimize the position and rotation of robot
jointly, with whole-body safety, smoothness, and dynamical feasibility taken
into account. Extensive simulation and real-world experiments verified the
reliability and generalizability of our method.Comment: Accepted at IROS 202
CoNi-MPC: Cooperative Non-inertial Frame Based Model Predictive Control
This paper presents a novel solution for UAV control in cooperative
multi-robot systems, which can be used in various scenarios such as
leader-following, landing on a moving base, or specific relative motion with a
target. Unlike classical methods that tackle UAV control in the world frame, we
directly control the UAV in the target coordinate frame, without making motion
assumptions about the target. In detail, we formulate a non-linear model
predictive controller of a UAV, referred to as the agent, within a non-inertial
frame (i.e., the target frame). The system requires the relative states (pose
and velocity), the angular velocity and the accelerations of the target, which
can be obtained by relative localization methods and ubiquitous MEMS IMU
sensors, respectively. This framework eliminates dependencies that are vital in
classical solutions, such as accurate state estimation for both the agent and
target, prior knowledge of the target motion model, and continuous trajectory
re-planning for some complex tasks. We have performed extensive simulations to
investigate the control performance with varying motion characteristics of the
target. Furthermore, we conducted real robot experiments, employing either
simulated relative pose estimation from motion capture systems indoors or
directly from our previous relative pose estimation devices outdoors, to
validate the applicability and feasibility of the proposed approach
Collaborative Planning for Catching and Transporting Objects in Unstructured Environments
Multi-robot teams have attracted attention from industry and academia for
their ability to perform collaborative tasks in unstructured environments, such
as wilderness rescue and collaborative transportation.In this paper, we propose
a trajectory planning method for a non-holonomic robotic team with
collaboration in unstructured environments.For the adaptive state collaboration
of a robot team to catch and transport targets to be rescued using a net, we
model the process of catching the falling target with a net in a continuous and
differentiable form.This enables the robot team to fully exploit the kinematic
potential, thereby adaptively catching the target in an appropriate
state.Furthermore, the size safety and topological safety of the net, resulting
from the collaborative support of the robots, are guaranteed through geometric
constraints.We integrate our algorithm on a car-like robot team and test it in
simulations and real-world experiments to validate our performance.Our method
is compared to state-of-the-art multi-vehicle trajectory planning methods,
demonstrating significant performance in efficiency and trajectory quality
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