1,421 research outputs found
Robonaut Mobile Autonomy: Initial Experiments
A mobile version of the NASA/DARPA Robonaut humanoid recently completed initial autonomy trials working directly with humans in cluttered environments. This compact robot combines the upper body of the Robonaut system with a Segway Robotic Mobility Platform yielding a dexterous, maneuverable humanoid ideal for interacting with human co-workers in a range of environments. This system uses stereovision to locate human teammates and tools and a navigation system that uses laser range and vision data to follow humans while avoiding obstacles. Tactile sensors provide information to grasping algorithms for efficient tool exchanges. The autonomous architecture utilizes these pre-programmed skills to form complex behaviors. The initial behavior demonstrates a robust capability to assist a human by acquiring a tool from a remotely located individual and then following the human in a cluttered environment with the tool for future use
The Integration of Fuzzy Logic System for Obstacle Avoidance Behavior of Mobile Robot
A mobile robot has a capability of sensing its location under uncertain environment, planning a real-time path as well as controlling its steering angle and speed to reach the target
location. A robust controller is embedded in mobile robot whilst analyzing the input and output that help it to navigate without colliding with any obstacles. Meanwhile, Fuzzy Logic Controllers
(FLC) is an intelligent technique that proves to be the one of the most reliable controllers that suits well for nonlinear system like robot due to the simple control based on user input without any prior knowledge to the mathematical model. In this paper, the Mamdani and Sugeno FLC are developed for a mobile robot. The smoothness and efficiency that generated from these FLC is analyzed based on simulation of Pioneer P3-DX robot in virtual robotic software for single and multirobot environments under static obstacles environment. Simulation results for the Pioneer P3-DX robot shows the Sugeno FLC able to produce smoother path and reach the goal faster than Mamdani FLC
Behavioural strategy for indoor mobile robot navigation in dynamic environments
PhD ThesisDevelopment of behavioural strategies for indoor mobile navigation has become a challenging
and practical issue in a cluttered indoor environment, such as a hospital or factory, where
there are many static and moving objects, including humans and other robots, all of which
trying to complete their own specific tasks; some objects may be moving in a similar direction
to the robot, whereas others may be moving in the opposite direction. The key requirement
for any mobile robot is to avoid colliding with any object which may prevent it from reaching
its goal, or as a consequence bring harm to any individual within its workspace. This challenge
is further complicated by unobserved objects suddenly appearing in the robots path,
particularly when the robot crosses a corridor or an open doorway. Therefore the mobile
robot must be able to anticipate such scenarios and manoeuvre quickly to avoid collisions.
In this project, a hybrid control architecture has been designed to navigate within dynamic
environments. The control system includes three levels namely: deliberative, intermediate
and reactive, which work together to achieve short, fast and safe navigation. The deliberative
level creates a short and safe path from the current position of the mobile robot to its goal
using the wavefront algorithm, estimates the current location of the mobile robot, and extracts
the region from which unobserved objects may appear. The intermediate level links the
deliberative level and the reactive level, that includes several behaviours for implementing
the global path in such a way to avoid any collision.
In avoiding dynamic obstacles, the controller has to identify and extract obstacles from the
sensor data, estimate their speeds, and then regular its speed and direction to minimize the
collision risk and maximize the speed to the goal. The velocity obstacle approach (VO) is
considered an easy and simple method for avoiding dynamic obstacles, whilst the collision
cone principle is used to detect the collision situation between two circular-shaped objects.
However the VO approach has two challenges when applied in indoor environments. The
first challenge is extraction of collision cones of non-circular objects from sensor data, in
which applying fitting circle methods generally produces large and inaccurate collision cones
especially for line-shaped obstacle such as walls. The second challenge is that the mobile
robot cannot sometimes move to its goal because all its velocities to the goal are located
within collision cones. In this project, a method has been demonstrated to extract the colliii
sion cones of circular and non-circular objects using a laser sensor, where the obstacle size
and the collision time are considered to weigh the robot velocities. In addition the principle
of the virtual obstacle was proposed to minimize the collision risk with unobserved moving
obstacles. The simulation and experiments using the proposed control system on a Pioneer
mobile robot showed that the mobile robot can successfully avoid static and dynamic obstacles.
Furthermore the mobile robot was able to reach its target within an indoor environment
without causing any collision or missing the target
Formation Navigation and Relative Localisation of Multi-Robot Systems
When proceeding from single to multiple robots, cooperative action is one of the most relevant topics. The domain of robotic security systems contains typical applications for a multi-robot system (MRS). Possible scenarios are safety and security issues on airports, harbours, large industry plants or museums. Additionally, the field of environmental supervision is an up-coming issue. Inherent to these applications is the need for an organised and coordinated navigation of the robots, and a vital prerequisite for any coordinated movements is a good localisation. This dissertation will present novel approaches to the problems of formation navigation and relative localisation with multiple ground-based mobile robots. It also looks into the question what kind of metric is applicable for multi-robot navigation problems. Thereby, the focus of this work will be on aspects of 1. coordinated navigation and movement A new potential-field-based approach to formation navigation is presented. In contradiction to classical potential-field-based formation approaches, the proposed method also uses the orientation between neighbours in the formation. Consequently, each robot has a designated position within the formation. Therefore, the new method is called directed potential field approach. Extensive experiments prove that the method is capable of generating all kinds of formation shapes, even in the presence of dense obstacles. All tests have been conducted with simulated and real robots and successfully guided the robot formation through environments with varying obstacle configurations. In comparison, the nondirected potential field approach turns out to be unstable regarding the positions of the robots within formations. The robots strive to switch their positions, e.g. when passing through narrow passages. Under such conditions the directed approach shows a preferable behaviour, called “breathing”. The formation shrinks or inflates depending on the obstacle situation while trying to maintain its shape and keep the robots at their desired positions inside the formation. For a more particular comparison of formation algorithms it is important to have measures that allow a meaningful evaluation of the experimental data. For this purpose a new formation metric is developed. If there are many obstacles, the formation error must be scaled down to be comparable to an empty environment where the error would be small. Assuming that the environment is unknown and possibly non-static, only actual sensor information can be used for these calculations. We developed a special weighting factor, which is inverse proportional to the “density” of obstacles and which turns out to model the influence of the environment adequately. 2. relative localisation A new method for relative localisation between the members of a robot group is introduced. This relative localisation approach uses mutual sensor observations to localise the robots with respect to other objects – without having an environment model. Techniques like the Extended Kalman Filter (EKF) have proven to be powerful tools in the field of single robot applications. This work presents extensions to these algorithms with respect to the use in MRS. These aspects are investigated and combined under the topic of improving and stabilising the performance of the localisation and navigation process. Most of the common localisation approaches use maps and/or landmarks with the intention of generating a globally consistent world-coordinate system for the robot group. The aim of the here presented relative localisation approach, on the other hand, is to maintain only relative positioning between the robots. The presented method enables a group of mobile robots to start at an unknown location in an unknown environment and then to incrementally estimate their own positions and the relative locations of the other robots using only sensor information. The result is a robust, fast and precise approach, which does not need any preconditions or special assumptions about the environment. To validate the approach extensive tests with both, real and simulated, robots have been conducted. For a more specific evaluation, the Mean Localisation Error (MLE) is introduced. The conducted experiments include a comparison between the proposed Extended Kalman Filter and a standard SLAM-based approach. The developed method robustly delivered an accuracy better than 2 cm and performed at least as well as the SLAM approach. The algorithm coped with scattered groups of robots while moving on arbitrarily shaped paths. In summary, this thesis presents novel approaches to the field of coordinated navigation in multi-robot systems. The results facilitate cooperative movements of robot groups as well as relative localisation among the group members. In addition, a solid foundation for a non-environment related metric for formation navigation is introduced
Navigation of Automatic Vehicle using AI Techniques
In the field of mobile robot navigation have been studied as important task for the new generation of mobile robot i.e. Corobot. For this mobile robot navigation has been viewed for unknown environment. We consider the 4-wheeled vehicle (Corobot) for Path Planning, an autonomous robot and an obstacle and collision avoidance to be used in sensor based robot. We propose that the predefined distance from the robot to target and make the robot follow the target at this distance and improve the trajectory tracking characteristics. The robot will then navigate among these obstacles without hitting them and reach the specified goal point. For these goal achieving we use different techniques radial basis function and back-propagation algorithm under the study of neural network. In this Corobot a robotic arm are assembled and the kinematic analyses of Corobot arm and help of Phidget Control Panel a wheeled to be moved in both forward and reverse direction by 2-motor controller have to be done. Under kinematic analysis propose the relationships between the positions and orientation of the links of a manipulator. In these studies an artificial techniques and their control strategy are shown with potential applications in the fields of industry, security, defense, investigation, and others. Here finally, the simulation result using the webot neural network has been done and this result is compared with experimental data for different training pattern
Mobile Robots Navigation
Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described
Language and Sketching: An LLM-driven Interactive Multimodal Multitask Robot Navigation Framework
The socially-aware navigation system has evolved to adeptly avoid various
obstacles while performing multiple tasks, such as point-to-point navigation,
human-following, and -guiding. However, a prominent gap persists: in
Human-Robot Interaction (HRI), the procedure of communicating commands to
robots demands intricate mathematical formulations. Furthermore, the transition
between tasks does not quite possess the intuitive control and user-centric
interactivity that one would desire. In this work, we propose an LLM-driven
interactive multimodal multitask robot navigation framework, termed LIM2N, to
solve the above new challenge in the navigation field. We achieve this by first
introducing a multimodal interaction framework where language and hand-drawn
inputs can serve as navigation constraints and control objectives. Next, a
reinforcement learning agent is built to handle multiple tasks with the
received information. Crucially, LIM2N creates smooth cooperation among the
reasoning of multimodal input, multitask planning, and adaptation and
processing of the intelligent sensing modules in the complicated system.
Extensive experiments are conducted in both simulation and the real world
demonstrating that LIM2N has superior user needs understanding, alongside an
enhanced interactive experience
Micro and macro quadcopter drones for indoor mapping to support disaster management
In this paper we present the operations and mapping techniques of two drones that are different in terms of size, the sensors deployed, and the positioning and mapping techniques used. The first drone is a low-cost commercial quadcopter microdrone, a Crazyflie, while the second drone is a relatively expensive research quadcopter macrodrone, called MAX. We investigated their feasibility in mapping areas where satellite positioning is not available, such as indoor spaces
Microdrone-Based Indoor Mapping with Graph SLAM
Unmanned aerial vehicles offer a safe and fast approach to the production of three-dimensional spatial data on the surrounding space. In this article, we present a low-cost SLAM-based drone for creating exploration maps of building interiors. The focus is on emergency response mapping in inaccessible or potentially dangerous places. For this purpose, we used a quadcopter microdrone equipped with six laser rangefinders (1D scanners) and an optical sensor for mapping and positioning. The employed SLAM is designed to map indoor spaces with planar structures through graph optimization. It performs loop-closure detection and correction to recognize previously visited places, and to correct the accumulated drift over time. The proposed methodology was validated for several indoor environments. We investigated the performance of our drone against a multilayer LiDAR-carrying macrodrone, a vision-aided navigation helmet, and ground truth obtained with a terrestrial laser scanner. The experimental results indicate that our SLAM system is capable of creating quality exploration maps of small indoor spaces, and handling the loop-closure problem. The accumulated drift without loop closure was on average 1.1% (0.35 m) over a 31-m-long acquisition trajectory. Moreover, the comparison results demonstrated that our flying microdrone provided a comparable performance to the multilayer LiDAR-based macrodrone, given the low deviation between the point clouds built by both drones. Approximately 85 % of the cloud-to-cloud distances were less than 10 cm
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