Simulating Passivity for Robotic Walkers via Authority-Sharing

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Fontanelli

2020

We present a Lloyd-based navigation solution for robots that are required to move in a dynamic environment, where static obstacles (e.g, furnitures, parked cars) and unpredicted moving obstacles (e.g., humans, other robots) have to be detected and avoided on the fly. The algorithm can be computed in real-time and falls in the category of the reactive methods. The simplicity, the small amount of information required for the control inputs synthesis, and the low number of parameters to be tuned are the highlights of this method. Moreover, the multiagent flocking is considered in the same framework and opens to several interesting applications. particular, we show how a team of robots can flocks The goodness of the method is proved through extensive simulations and, for the single agent navigation in human-shared environment, also with experiments on a unicycle-like robot.

Section: B Problem Formulationmentioning

confidence: 99%

Lloyd-based Approach for Robots Navigation in Human-shared environments

Boldrer

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Fontanelli

2020

“…All of them are placed at a height of

m from the ground, except antenna

which is placed at a height

m. The robot adopted in the experiments can be driven manually through a remote control, i.e., a joystick. Since we are interested in actual industrial applications, where the estimate of the set of trajectories followed by the available moving robots is of major relevance either if they are autonomous or actually controlled by human workers, and just focused on the trajectory estimation (how the localisation uncertainty acts on the robot control law can be found, e.g., in [ 43 , 44 ]), we decided to steer the robot in the environment using the remote controller.…”

Section: Methodsmentioning

confidence: 99%

Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach †

Shamsfakhr

Motroni

et al. 2021

Sensors

Self Cite

Autonomous vehicles enable the development of smart warehouses and smart factories with an increased visibility, flexibility and efficiency. Thus, effective and affordable localisation methods for indoor vehicles are attracting interest to implement real-time applications. This paper presents an Extended Kalman Smoother design to both localise a mobile agent and reconstruct its entire trajectory through a sensor-fusion employing the UHF-RFID passive technology. Extensive simulations are carried out by considering the smoother optimal-window length and the effect of missing measurements from reference tags. Monte Carlo simulations are conducted for different vehicle trajectories and for different linear and angular velocities to evaluate the method accuracy. Then, an experimental analysis with a unicycle wheeled robot is performed in real indoor scenario, showing a position and orientation root mean square errors of 15 cm, and 0.2 rad, respectively.

“…As mentioned in Sections I and II, the platform used to validate experimentally the proposed approach is the FriWalk (Fig. 5), a commercial trolley for seniors 1 endowed with sensors [16], [12], as well as processing [37], [31] and guidance functions [38], [39], [40]. The robot can estimate its own speed, enabling odometric trajectory estimation, through two encoders AMT-102V mounted on rear wheels with a resolution of 0.08 mrad per tick.…”

Section: Methodsmentioning

confidence: 99%

Effective Landmark Placement for Robot Indoor Localization With Position Uncertainty Constraints

Magnago

IEEE Trans. Instrum. Meas.

Fontanelli

et al. 2019

Self Cite

A well-known, crucial problem for indoor positioning of mobile agents (e.g., robots) equipped with exteroceptive sensors is related to the need to deploy reference landmarks in a given environment. Normally, anytime a landmark is detected, an agent estimates its own location and attitude with respect to landmark position and/or orientation in the chosen reference frame. When instead no landmark is recognized, other sensors (e.g., odometers in the case of wheeled robots) can be used to track the agent position and orientation from the last detected landmark. At the moment, landmark placement is usually based just on commonsense criteria, which are not formalized properly.As a result, positioning uncertainty tends to grow unpredictably. On the contrary, the purpose of this paper is to minimize the number of landmarks, while ensuring that localization uncertainty is kept within wanted boundaries. The developed approach relies on the following key features: a dynamic model describing agents' motion, a model predicting the agents' paths within a given environment and, finally, a Conjunctive Normal Form (CNF) formalization of the optimization problem, which can be efficiently (although approximately) solved by a greedy algorithm. The effectiveness of the proposed landmark placement technique is first demonstrated through simulations in a variety of conditions and then it is validated through experiments on the field, by using non-Bayesian and Bayesian position tracking algorithms.