Robot Goes Back Home Despite All the People

We have developed a navigation system for a mobile robot that enables it to autonomously return to a start point after completing a route. It works efficiently even in complex, low structured and populated indoor environments. A point-based map of the environment is built as the robot explores new areas; it is employed for localization and obstacle avoidance. Points corresponding to dynamical objects are removed from the map so that they do not affect navigation in a wrong way. The algorithms and results we deem more relevant are explained in the paper

MPC-based humanoid pursuit-evasion in the presence of obstacles

We consider a pursuit-evasion problem between humanoids in the presence of obstacles. In our scenario, the pursuer enters the safety area of the evader headed for collision, while the latter executes a fast evasive motion. Control schemes are designed for both the pursuer and the evader. They are structurally identical, although the objectives are different: the pursuer tries to align its direction of motion with the line- of-sight to the evader, whereas the evader tries to move in a direction orthogonal to the line-of-sight to the pursuer. At the core of the control architecture is a Model Predictive Control scheme for generating a stable gait. This allows for the inclusion of workspace obstacles, which we take into account at two levels: during the determination of the footsteps orientation and as an explicit MPC constraint. We illustrate the results with simulations on NAO humanoids

Mobile robots and vehicles motion systems: a unifying framework

Robots perform many different activities in order to accomplish their tasks. The robot motion capability is one of the most important ones for an autonomous be- havior in a typical indoor-outdoor mission (without it other tasks can not be done), since it drastically determines the global success of a robotic mission. In this thesis, we focus on the main methods for mobile robot and vehicle motion systems and we build a common framework, where similar components can be interchanged or even used together in order to increase the whole system performance

Autonomna navigacija za invalidska kolica s detekcijom prepreka u stvarnom vremenu korištenjem 3D senzora

Autonomous wheelchairs operating in dynamic environments need to sense its surrounding environment and adapt the control signal, in real-time, to avoid collisions and protect the user. In this paper we propose a robust, simple and real-time autonomous navigation module that drives a wheelchair toward a desired target, along with its capability to avoid obstacles in a 3D dynamic environment. To command the mobile robot to the target, we use a Fuzzy Logic Controller (FLC). For obstacle avoidance, we use the Kinect Xbox 360 to provide an actual map of the environment. The generated map is fed to the reactive obstacle avoidance control Deformable Virtual Zone (DVZ). Simulations and real world experiments results are reported to show the feasibility and the performance of the proposed control system.Autonomna invalidska kolica koja se kreću u dinamičkim okruženjima moraju biti sposobna detektirati prepreke u svojoj okolini, te prilagoditi upravljački signal u stvarnom vremenu kako bi se izbjegli sudari i zaštitio korisnik. U ovom radu predlaže se jednostavan, robustan modul za autonomnu navigaciju u stvarnom vremenu koji vodi invalidska kolica prema željenom odredištu, te omogućuje izbjegavanje prepreka u 3D okruženju. Za upravljanje koristi se regulator baziran na neizravnoj logici (FLC). Za izbjegavanje prepreka koristi se Kinect Xbox 360 senzor koji gradi kartu okoline. Generirana karta se predaje reaktivnoj kontroli za izbjegavanje prepreka Deformiranoj Virutalnoj Zoni (DVZ). Prikazani su rezultati simulacija i eksperimenata u stvarnom svijetu kako bi se pokazala izvedivost i kvaliteta izvođenja predloženog sustava upravljanja

A mosaic of eyes

Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties

Path Planning Based on Parametric Curves

Parametric curves are extensively used in engineering. The most commonly used parametric curves are, Bézier, B-splines, (NURBSs), and rational Bézier. Each and every one of them has special features, being the main difference between them the complexity of their mathematical definition. While Bézier curves are the simplest ones, B-splines or NURBSs are more complex. In mobile robotics, two main problems have been addressed with parametric curves. The first one is the definition of an initial trajectory for a mobile robot from a start location to a goal. The path has to be a continuous curve, smooth and easy to manipulate, and the properties of the parametric curves meet these requirements. The second one is the modification of the initial trajectory in real time attending to the dynamic properties of the environment. Parametric curves are capable of enhancing the trajectories produced by path planning algorithms adapting them to the kinematic properties of the robot. In order to avoid obstacles, the shape modification of parametric curves is required. In this chapter, an algorithm is proposed for computing an initial Bézier trajectory of a mobile robot and subsequently modifies it in real time in order to avoid obstacles in a dynamic environment

Fast and adaptive fractal tree-based path planning for programmable bevel tip steerable needles

© 2016 IEEE. Steerable needles are a promising technology for minimally invasive surgery, as they can provide access to difficult to reach locations while avoiding delicate anatomical regions. However, due to the unpredictable tissue deformation associated with needle insertion and the complexity of many surgical scenarios, a real-time path planning algorithm with high update frequency would be advantageous. Real-time path planning for nonholonomic systems is commonly used in a broad variety of fields, ranging from aerospace to submarine navigation. In this letter, we propose to take advantage of the architecture of graphics processing units (GPUs) to apply fractal theory and thus parallelize real-time path planning computation. This novel approach, termed adaptive fractal trees (AFT), allows for the creation of a database of paths covering the entire domain, which are dense, invariant, procedurally produced, adaptable in size, and present a recursive structure. The generated cache of paths can in turn be analyzed in parallel to determine the most suitable path in a fraction of a second. The ability to cope with nonholonomic constraints, as well as constraints in the space of states of any complexity or number, is intrinsic to the AFT approach, rendering it highly versatile. Three-dimensional (3-D) simulations applied to needle steering in neurosurgery show that our approach can successfully compute paths in real-time, enabling complex brain navigation