33,911 research outputs found
User interface and function library for ground robot navigation
Master's Project (M.S.) University of Alaska Fairbanks, 2017A web application user interface and function library were developed to enable a user to program a ground robot to navigate autonomously. The user interface includes modules for generating a grid of obstacles from a map image, setting waypoints for a path through the map, and programming a robot in a code editor to navigate autonomously. The algorithm used for navigation is an A* algorithm modified with obstacle padding to accommodate the width of the robot and path smoothing to simplify the paths. The user interface and functions were designed to be simple so that users without technical backgrounds can use them, and by doing so they can engage in the development process of human-centered robots. The navigation functions were successful in finding paths in test configurations, and the performance of the algorithms was fast enough for user interactivity up to a certain limit of grid cell sizes
In-Network View Synthesis for Interactive Multiview Video Systems
To enable Interactive multiview video systems with a minimum view-switching
delay, multiple camera views are sent to the users, which are used as reference
images to synthesize additional virtual views via depth-image-based rendering.
In practice, bandwidth constraints may however restrict the number of reference
views sent to clients per time unit, which may in turn limit the quality of the
synthesized viewpoints. We argue that the reference view selection should
ideally be performed close to the users, and we study the problem of in-network
reference view synthesis such that the navigation quality is maximized at the
clients. We consider a distributed cloud network architecture where data stored
in a main cloud is delivered to end users with the help of cloudlets, i.e.,
resource-rich proxies close to the users. In order to satisfy last-hop
bandwidth constraints from the cloudlet to the users, a cloudlet re-samples
viewpoints of the 3D scene into a discrete set of views (combination of
received camera views and virtual views synthesized) to be used as reference
for the synthesis of additional virtual views at the client. This in-network
synthesis leads to better viewpoint sampling given a bandwidth constraint
compared to simple selection of camera views, but it may however carry a
distortion penalty in the cloudlet-synthesized reference views. We therefore
cast a new reference view selection problem where the best subset of views is
defined as the one minimizing the distortion over a view navigation window
defined by the user under some transmission bandwidth constraints. We show that
the view selection problem is NP-hard, and propose an effective polynomial time
algorithm using dynamic programming to solve the optimization problem.
Simulation results finally confirm the performance gain offered by virtual view
synthesis in the network
Fast processing of grid maps using graphical multiprocessors
Grid mapping is a very common technique used in mobile robotics to build a continuous 2D representation of the environment useful for navigation purposes. Although its computation is quite simple and fast, this algorithm uses the hypothesis of a known robot pose. In practice, this can require the re-computation of the map when the estimated robot poses change, as when a loop closure is detected. This paper presents a parallelization of a reference implementation of the grid mapping algorithm, which is suitable to be fully run on a graphics card showing huge processing speedups (up to 50×) while fully releasing the main processor, which can be very useful for many Simultaneous Localization and Mapping algorithms
A deep reinforcement learning based homeostatic system for unmanned position control
Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/
Airborne mapping of complex obstacles using 2D Splinegon
This paper describes a recently proposed algorithm in mapping the unknown
obstacle in a stationary environment where the obstacles are represented as
curved in nature. The focus is to achieve a guaranteed performance of sensor
based navigation and mapping. The guaranteed performance is quantified by
explicit bounds of the position estimate of an autonomous aerial vehicle using
an extended Kalman filter and to track the obstacle so as to extract the map of
the obstacle. This Dubins path planning algorithm is used to provide a flyable
and safe path to the vehicle to fly from one location to another. This
description takes into account the fact that the vehicle is made to fly around
the obstacle and hence will map the shape of the obstacle using the 2D-Splinegon
technique. This splinegon technique, the most efficient and a robust way to
estimate the boundary of a curved nature obstacles, can provide mathematically
provable performance guarantees that are achievable in practice
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