Symbiotic Navigation in Multi-Robot Systems with Remote Obstacle Knowledge Sharing

Large scale operational areas often require multiple service robots for coverage and task parallelism. In such scenarios, each robot keeps its individual map of the environment and serves specific areas of the map at different times. We propose a knowledge sharing mechanism for multiple robots in which one robot can inform other robots about the changes in map, like path blockage, or new static obstacles, encountered at specific areas of the map. This symbiotic information sharing allows the robots to update remote areas of the map without having to explicitly navigate those areas, and plan efficient paths. A node representation of paths is presented for seamless sharing of blocked path information. The transience of obstacles is modeled to track obstacles which might have been removed. A lazy information update scheme is presented in which only relevant information affecting the current task is updated for efficiency. The advantages of the proposed method for path planning are discussed against traditional method with experimental results in both simulation and real environments

Autonomous Navigation for Mobile Robots in Crowded Environments

L'abstract è presente nell'allegato / the abstract is in the attachmen

PEIS stol: autonomni robotski stol za kućanstva

There are two main trends in the area of home and service robotics. The classical one aims at the development of a single skilled servant robot, able to perform complex tasks in a passive environment. The second, more recent trend aims at the achievement of complex tasks through the cooperation of a network of simpler robotic devices pervasively embedded in the domestic environment. This paper contributes to the latter trend by describing the PEIS Table, an autonomous robotic table that can be embedded in a smart environment. The robotic table can operate alone, performing simple point-to-point navigation, or it can collaborate with other devices in the environment to perform more complex tasks. Collaboration follows the PEIS Ecology model. The hardware and software design of the PEIS Table are guided by a set of requirements for robotic domestic furniture that differ, to some extent, from the requirements usually considered for service robots.U uslužnoj robotici i robotici za kućanstva postoje dva glavna trenda. Klasičan pristup teži razvoju jednog složenog uslužnog robota koji je sposoban izvršavati složene zadatke u pasivnom okruženju. Dok drugi, nešto noviji pristup, teži rješavanju složenih zadataka kroz suradnju umreženih nešto jednostavnijih robota prožetih kroz cijelo kućanstvo. Ovaj članak svoj doprinos daje drugom pristupu opisujući PEIS stol, autonomni robotski stol koji se može postaviti u inteligentnom okruženju. Robotski stol može djelovati samostalno, navigirajući od točke do točke ili može surađivati s ostalim uređajima u okruženju radi izvršavanja složenijih zadataka. Ta suradnja prati PEIS ekološki model. Dizajn sklopovlja i programske podrške PEIS stola prati zahtjeve za robotsko pokućstvo koji se donekle razlikuju od zahtjeva koji se inače postavljaju za uslužne robote

Near-Optimal Motion Planning Algorithms Via A Topological and Geometric Perspective

Motion planning is a fundamental problem in robotics, which involves finding a path for an autonomous system, such as a robot, from a given source to a destination while avoiding collisions with obstacles. The properties of the planning space heavily influence the performance of existing motion planning algorithms, which can pose significant challenges in handling complex regions, such as narrow passages or cluttered environments, even for simple objects. The problem of motion planning becomes deterministic if the details of the space are fully known, which is often difficult to achieve in constantly changing environments. Sampling-based algorithms are widely used among motion planning paradigms because they capture the topology of space into a roadmap. These planners have successfully solved high-dimensional planning problems with a probabilistic-complete guarantee, i.e., it guarantees to find a path if one exists as the number of vertices goes to infinity. Despite their progress, these methods have failed to optimize the sub-region information of the environment for reuse by other planners. This results in re-planning overhead at each execution, affecting the performance complexity for computation time and memory space usage. In this research, we address the problem by focusing on the theoretical foundation of the algorithmic approach that leverages the strengths of sampling-based motion planners and the Topological Data Analysis methods to extract intricate properties of the environment. The work contributes a novel algorithm to overcome the performance shortcomings of existing motion planners by capturing and preserving the essential topological and geometric features to generate a homotopy-equivalent roadmap of the environment. This roadmap provides a mathematically rich representation of the environment, including an approximate measure of the collision-free space. In addition, the roadmap graph vertices sampled close to the obstacles exhibit advantages when navigating through narrow passages and cluttered environments, making obstacle-avoidance path planning significantly more efficient. The application of the proposed algorithms solves motion planning problems, such as sub-optimal planning, diverse path planning, and fault-tolerant planning, by demonstrating the improvement in computational performance and path quality. Furthermore, we explore the potential of these algorithms in solving computational biology problems, particularly in finding optimal binding positions for protein-ligand or protein-protein interactions. Overall, our work contributes a new way to classify routes in higher dimensional space and shows promising results for high-dimensional robots, such as articulated linkage robots. The findings of this research provide a comprehensive solution to motion planning problems and offer a new perspective on solving computational biology problems

Research on a semiautonomous mobile robot for loosely structured environments focused on transporting mail trolleys

In this thesis is presented a novel approach to model, control, and planning the motion of a nonholonomic wheeled mobile robot that applies stable pushes and pulls to a nonholonomic cart (York mail trolley) in a loosely structured environment. The method is based on grasping and ungrasping the nonholonomic cart, as a result, the robot changes its kinematics properties. In consequence, two robot configurations are produced by the task of grasping and ungrasping the load, they are: the single-robot configuration and the robot-trolley configuration. Furthermore, in order to comply with the general planar motion law of rigid bodies and the kinematic constraints imposed by the robot wheels for each configuration, the robot has been provided with two motorized steerable wheels in order to have a flexible platform able to adapt to these restrictions. [Continues.

RUR53: an Unmanned Ground Vehicle for Navigation, Recognition and Manipulation

This paper proposes RUR53: an Unmanned Ground Vehicle able to autonomously navigate through, identify, and reach areas of interest; and there recognize, localize, and manipulate work tools to perform complex manipulation tasks. The proposed contribution includes a modular software architecture where each module solves specific sub-tasks and that can be easily enlarged to satisfy new requirements. Included indoor and outdoor tests demonstrate the capability of the proposed system to autonomously detect a target object (a panel) and precisely dock in front of it while avoiding obstacles. They show it can autonomously recognize and manipulate target work tools (i.e., wrenches and valve stems) to accomplish complex tasks (i.e., use a wrench to rotate a valve stem). A specific case study is described where the proposed modular architecture lets easy switch to a semi-teleoperated mode. The paper exhaustively describes description of both the hardware and software setup of RUR53, its performance when tests at the 2017 Mohamed Bin Zayed International Robotics Challenge, and the lessons we learned when participating at this competition, where we ranked third in the Gran Challenge in collaboration with the Czech Technical University in Prague, the University of Pennsylvania, and the University of Lincoln (UK).Comment: This article has been accepted for publication in Advanced Robotics, published by Taylor & Franci

PATH PLANNING ALGORITHM, BASED ON USER-DEFINED MAXIMAL LOCALIZATION ERROR

A model-based path planning algorithm will be presented in this paper. The whole model, like the algorithm, is divided into 2 parts. The 1st part begins with a map-building process over an unknown environment, which is based on the construction of the so-called Potential Field (PF) of the environment. In this part, the above mentioned PF will be created by three autonomous robots, equipped with US sensors, which will cooperate and update the global potential map on the remote host. The 2nd part begins with the calculation of the environment´s 2D mathematical model. The calculation is realized through the ythresholding of the global potential map. Furthermore, a landmark arrangement will be defined on this model. The yArtificial Error Field (AEF), which covers the entire workspace, will be calculated and the result will depend on the sensory system of the mobile robot/robots and on the landmark arrangement. Actually, the three-dimensional yAEF contains the ylocalization errors corresponding to each `x, y´ position of the work space´s free space. In respect of the yuser-defined maximal localization error (ϵ\max), some ynavigation paths (NP) can be generated. These paths serve as the base for the calculation of the possible routes in form of directed and yweighted graph-map. The route with yminimal complexity between the start and the ydocking positions on this graph-map will be selected. Each point of the mobile robot´s exact trajectory must fit in the selected navigation path (NP). This maintains the allowed position error below the defined limit. The shape of the trajectory is calculated by the use of cubic B-splines