9,078 research outputs found
Infrastructure-Aided Localization and State Estimation for Autonomous Mobile Robots
A slip-aware localization framework is proposed for mobile robots experiencing wheel slip in dynamic environments. The framework fuses infrastructure-aided visual tracking data (via fisheye lenses) and proprioceptive sensory data from a skid-steer mobile robot to enhance accuracy and reduce variance of the estimated states. The slip-aware localization framework includes: the visual thread to detect and track the robot in the stereo image through computationally efficient 3D point cloud generation using a region of interest; and the ego motion thread which uses a slip-aware odometry mechanism to estimate the robot pose utilizing a motion model considering wheel slip. Covariance intersection is used to fuse the pose prediction (using proprioceptive data) and the visual thread, such that the updated estimate remains consistent. As confirmed by experiments on a skid-steer mobile robot, the designed localization framework addresses state estimation challenges for indoor/outdoor autonomous mobile robots which experience high-slip, uneven torque distribution at each wheel (by the motion planner), or occlusion when observed by an infrastructure-mounted camera. The proposed system is real-time capable and scalable to multiple robots and multiple environmental cameras
Globally Guided Trajectory Planning in Dynamic Environments
Navigating mobile robots through environments shared with humans is
challenging. From the perspective of the robot, humans are dynamic obstacles
that must be avoided. These obstacles make the collision-free space nonconvex,
which leads to two distinct passing behaviors per obstacle (passing left or
right). For local planners, such as receding-horizon trajectory optimization,
each behavior presents a local optimum in which the planner can get stuck. This
may result in slow or unsafe motion even when a better plan exists. In this
work, we identify trajectories for multiple locally optimal driving behaviors,
by considering their topology. This identification is made consistent over
successive iterations by propagating the topology information. The most
suitable high-level trajectory guides a local optimization-based planner,
resulting in fast and safe motion plans. We validate the proposed planner on a
mobile robot in simulation and real-world experiments.Comment: 7 pages, 6 figures, accepted to IEEE International Conference on
Robotics and Automation (ICRA) 202
Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform
In this paper, we provide details of implementing a system for managing a
fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse
premise. While the robots are themselves autonomous in its motion and obstacle
avoidance capability, the target destination for each robot is provided by a
global planner. The global planner and the ground vehicles (robots) constitute
a multi agent system (MAS) which communicate with each other over a wireless
network. Three different approaches are explored for implementation. The first
two approaches make use of the distributed computing based Networked Robotics
architecture and communication framework of Robot Operating System (ROS) itself
while the third approach uses Rapyuta Cloud Robotics framework for this
implementation. The comparative performance of these approaches are analyzed
through simulation as well as real world experiment with actual robots. These
analyses provide an in-depth understanding of the inner working of the Cloud
Robotics Platform in contrast to the usual ROS framework. The insight gained
through this exercise will be valuable for students as well as practicing
engineers interested in implementing similar systems else where. In the
process, we also identify few critical limitations of the current Rapyuta
platform and provide suggestions to overcome them.Comment: 14 pages, 15 figures, journal pape
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