Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omnidirectional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground

Suboptimal Omnidirectional Wheel Design and Implementation

The optimal design of an omnidirectional wheel is usually focused on the minimization of the gap between the free rollers of the wheel in order to minimize contact discontinuities with the floor in order to minimize the generation of vibrations. However, in practice, a fast, tall, and heavy-weighted mobile robot using optimal omnidirectional wheels may also need a suspension system in order to reduce the presence of vibrations and oscillations in the upper part of the mobile robot. This paper empirically evaluates whether a heavy-weighted omnidirectional mobile robot can take advantage of its passive suspension system in order to also use non-optimal or suboptimal omnidirectional wheels with a non-optimized inner gap. The main comparative advantages of the proposed suboptimal omnidirectional wheel are its low manufacturing cost and the possibility of taking advantage of the gap to operate outdoors. The experimental part of this paper compares the vibrations generated by the motion system of a versatile mobile robot using optimal and suboptimal omnidirectional wheels. The final conclusion is that a suboptimal wheel with a large gap produces comparable on-board vibration patterns while maintaining the traction and increasing the grip on non-perfect planar surfaces.This research was funded by the MCI program, grant number PID2020-118874RB-I00

A STUDY OF NEW LOCALIZATION METHOD USING OMNIDIRECTIONAL CAMERA FOR AUTONOMOUS MOBILE ROBOT

This paper describes the development of a self-localization for an autonomous mobile robot using an omnidirectional camera. An omnidirectional camera can acquire surrounding landmarks at one time without dead angle. By comparing a sequence of detecting landmarks in omnidirectional images, we rebuild a localized map for a self-localization of mobile robots. The validity of the proposed method is confirmed by actual experiment using a mobile robot in the outdoor environment

Analysis, design, and control of an omnidirectional mobile robot in rough terrain

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.Includes bibliographical references (leaves 49-52).An omnidirectional mobile robot is able, kinematically, to move in any direction regardless of current pose. To date, nearly all designs and analyses of omnidirectional mobile robots have considered the case of motion on flat, smooth terrain. In this thesis, an investigation of the suitability of an active split offset caster driven omnidirectional mobile robot for use in rough terrain is presented. Kinematic and geometric properties of the drive mechanism are investigated along with guidelines for designing the robot. An optimization method is implemented to explore the design space. These analyses can be used as design guidelines for development of an omnidirectional mobile robot that can operate in unstructured environments. A simple kinematic controller that considers the effects of terrain unevenness via an estimate of the wheel-terrain contact angles is also presented. It is shown in simulation that under the proposed control method, near-omnidirectional tracking performance is possible even in rough, uneven terrain.by Martin Richard Udengaard.S.M

Code migration from a realistic simulator to a real wheeled mobile robot

This paper describes the code migration from a realistic simulator to a real wheeled mobile robot. The robot software consists in the localization and navigation of an omnidirectional robot in a structured environment. The localization estimate is achieved by fusing odometry and infra-red distance sensors data, applying an extended Kalman filter

The cubic root unscented kalman filter to estimate the position and orientation of mobile robot trajectory

In this paper we introduce a Cubic Root Unscented Kalman Filter (CRUKF) compared to the Unscented Kalman Filter (UKF) for calculating the covariance cubic matrix and covariance matrix within a sensor fusion algorithm to estimate the measurements of an omnidirectional mobile robot trajectory. We study the fusion of the data obtained by the position and orientation with a good precision to localize the robot in an external medium; we apply the techniques of Kalman Filter (KF) to the estimation of the trajectory. We suppose a movement of mobile robot on a plan in two dimensions. The sensor approach is based on the Cubic Root Unscented Kalman Filter (CRUKF) and too on the standard Unscented Kalman Filter (UKF) which are modified to handle measurements from the position and orientation. A real-time implementation is done on a three-wheeled omnidirectional mobile robot, using a dynamic model with trajectories. The algorithm is analyzed and validated with simulations

A Real-Time Solver For Time-Optimal Control Of Omnidirectional Robots with Bounded Acceleration

We are interested in the problem of time-optimal control of omnidirectional robots with bounded acceleration (TOC-ORBA). While there exist approximate solutions for such robots, and exact solutions with unbounded acceleration, exact solvers to the TOC-ORBA problem have remained elusive until now. In this paper, we present a real-time solver for true time-optimal control of omnidirectional robots with bounded acceleration. We first derive the general parameterized form of the solution to the TOC-ORBA problem by application of Pontryagin's maximum principle. We then frame the boundary value problem of TOC-ORBA as an optimization problem over the parametrized control space. To overcome local minima and poor initial guesses to the optimization problem, we introduce a two-stage optimal control solver (TSOCS): The first stage computes an upper bound to the total time for the TOC-ORBA problem and holds the time constant while optimizing the parameters of the trajectory to approach the boundary value conditions. The second stage uses the parameters found by the first stage, and relaxes the constraint on the total time to solve for the parameters of the complete TOC-ORBA problem. We further implement TSOCS as a closed loop controller to overcome actuation errors on real robots in real-time. We empirically demonstrate the effectiveness of TSOCS in simulation and on real robots, showing that 1) it runs in real time, generating solutions in less than 0.5ms on average; 2) it generates faster trajectories compared to an approximate solver; and 3) it is able to solve TOC-ORBA problems with non-zero final velocities that were previously unsolvable in real-time