4,730 research outputs found
Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters
Dynamic Camera Clusters (DCCs) are multi-camera systems where one or more
cameras are mounted on actuated mechanisms such as a gimbal. Existing methods
for DCC calibration rely on joint angle measurements to resolve the
time-varying transformation between the dynamic and static camera. This
information is usually provided by motor encoders, however, joint angle
measurements are not always readily available on off-the-shelf mechanisms. In
this paper, we present an encoderless approach for DCC calibration which
simultaneously estimates the kinematic parameters of the transformation chain
as well as the unknown joint angles. We also demonstrate the integration of an
encoderless gimbal mechanism with a state-of-the art VIO algorithm, and show
the extensions required in order to perform simultaneous online estimation of
the joint angles and vehicle localization state. The proposed calibration
approach is validated both in simulation and on a physical DCC composed of a
2-DOF gimbal mounted on a UAV. Finally, we show the experimental results of the
calibrated mechanism integrated into the OKVIS VIO package, and demonstrate
successful online joint angle estimation while maintaining localization
accuracy that is comparable to a standard static multi-camera configuration.Comment: ICRA 201
Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search
In principle, reinforcement learning and policy search methods can enable
robots to learn highly complex and general skills that may allow them to
function amid the complexity and diversity of the real world. However, training
a policy that generalizes well across a wide range of real-world conditions
requires far greater quantity and diversity of experience than is practical to
collect with a single robot. Fortunately, it is possible for multiple robots to
share their experience with one another, and thereby, learn a policy
collectively. In this work, we explore distributed and asynchronous policy
learning as a means to achieve generalization and improved training times on
challenging, real-world manipulation tasks. We propose a distributed and
asynchronous version of Guided Policy Search and use it to demonstrate
collective policy learning on a vision-based door opening task using four
robots. We show that it achieves better generalization, utilization, and
training times than the single robot alternative.Comment: Submitted to the IEEE International Conference on Robotics and
Automation 201
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC
We introduce Tempered Geodesic Markov Chain Monte Carlo (TG-MCMC) algorithm
for initializing pose graph optimization problems, arising in various scenarios
such as SFM (structure from motion) or SLAM (simultaneous localization and
mapping). TG-MCMC is first of its kind as it unites asymptotically global
non-convex optimization on the spherical manifold of quaternions with posterior
sampling, in order to provide both reliable initial poses and uncertainty
estimates that are informative about the quality of individual solutions. We
devise rigorous theoretical convergence guarantees for our method and
extensively evaluate it on synthetic and real benchmark datasets. Besides its
elegance in formulation and theory, we show that our method is robust to
missing data, noise and the estimated uncertainties capture intuitive
properties of the data.Comment: Published at NeurIPS 2018, 25 pages with supplement
Distributed Formation Control for Ground Vehicles with Visual Sensing Constraint
Formation control combined with different tasks enables a group of robots to reach a geographical location, avoid a collision, and simultaneously maintain the designed formation pattern. The connection and perception are critical for a multi-agent formation system, mainly when the robots only use vision as a communication method. However, most visual sensors have limited Field-of-view (FOV), which leaves some blind zones. In this case, a gradient-based distributed control law can be designed to keep every robot in the visible zones of other robots during the formation. This control strategy is designed to be processed independently on each vehicle with no network connection. This thesis assesses the feasibility of applying the gradient descent method to the problem of visual constraint vehicle formation
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Image-Based Visual-Impedance Control of a Dual-Arm Aerial Manipulator
Three new image-based visual-impedance control laws are proposed in this paper allowing physical interaction of a dual-arm unmanned aerial manipulator equipped with a camera and a force/torque sensor. Namely, two first-order impedance behaviours are designed based on the transpose and the inverse of the system Jacobian matrix, respectively, while a second-order impedance behaviour is carried out as well. Visual information is employed both to coordinate the camera motion in an eye- in-hand configuration with the assigned task executed by the other robot arm, and to define the elastic wrench component of the proposed hybrid impedance equations directly in the image plane
A Survey on Aerial Swarm Robotics
The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas
CPG-based Controllers can Trigger the Emergence of Social Synchrony in Human-Robot Interactions
International audienceSynchronization is an indissociable part of social interactions between humans, especially in gestural communication. With the emergence of social robotics and assistance robots, it becomes paramount for robots to be socially accepted and for humans to be able to connect with them. As a consequence, synchronization mechanisms should be inherent to any robot controllers, allowing the adaption to the interacting partner in any rhythmic way necessary. In this paper, plastic Central Pattern Generators (CPG) have been implemented in the joints of the robot Pepper that has to learn to wave back at a human partner. Results show that the CPG-based controller leads to adaptive waving synchronized with the human partner, thus proving that the CPG-based controller can achieve synchronization
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