Collision avoidance for a 6-DOF manipulator based on empty space analysis of the 3-D real world

2002 ◽  
Author(s):  
H. Onda ◽  
T. Hasegawa ◽  
T. Matsui
Keyword(s):  
Collision Avoidance ◽  
Real World ◽  
Empty Space ◽  
Space Analysis

Collision Avoidance for a Multiple-DOF Manipulator Based on Empty Space Analysis of the 3-D Real World

1992 ◽  
Vol 4 (5) ◽  
pp. 430-436 ◽  
Author(s):  
Hiromu Onda ◽  
◽  
Tsutomu Hasegawa ◽  
Toshihiro Matsui ◽  
Keyword(s):  
Collision Avoidance ◽  
Real World ◽  
Degrees Of Freedom ◽  
Empty Space ◽  
Joint Space ◽  
Degree Of Freedom ◽  
New Method ◽  
Promising Direction ◽  
Space Analysis ◽  
Path Search

This paper describes a new method for finding collisionfree paths for a multiple-degree of freedom (DOF) manipulator with rotational joints and a grasped object. The method first analyzes the structure of empty space in the 3-D workspace. Based on this space analysis, the path search is divided and direction which appears to be most promising is determined in the 3-D workspace. Finally, the path search is systematically executed in the joint space in the direction equivalent to the promising direction. This method is applicable to various problems regardless of the number of degrees of freedom of the manipulator, its structure, and the presence of a grasped object.

An Empirical Design Space Analysis of Doorway Tracking Systems for Real-World Environments

2017 ◽  
Vol 13 (4) ◽  
pp. 1-34 ◽  
Author(s):  
Erin Griffiths ◽  
Avinash Kalyanaraman ◽  
Juhi Ranjan ◽  
Kamin Whitehouse
Keyword(s):  
Real World ◽  
Design Space ◽  
Tracking Systems ◽  
Space Analysis ◽  
Empirical Design

scholarly journals iADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV

2021 ◽  
Vol 11 (9) ◽  
pp. 3948
Author(s):  
Aye Aye Maw ◽  
Maxim Tyan ◽  
Tuan Anh Nguyen ◽  
Jae-Woo Lee
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Real World ◽  
Mission Planning ◽  
Planning System ◽  
Local Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.

scholarly journals Lessons Learned in Designing Ubiquitous Augmented Reality User Interfaces

2011 ◽  
pp. 218-235
Author(s):  
Christian Sandor ◽  
Gudrun Klinker
Keyword(s):  
Augmented Reality ◽  
Human Computer Interaction ◽  
Design Process ◽  
User Interfaces ◽  
Real World ◽  
Lessons Learned ◽  
Digital Information ◽  
Space Analysis ◽  
Research Problems ◽  
Design Options

Ubiquitous augmented reality (UAR) is an emerging human-computer interaction technology, arising from the convergence of augmented reality and ubiquitous computing. In UAR, visualizations can augment the real world with digital information. Interactions can follow a tangible metaphor. Both should adapt according to the user’s context and are distributed on a possibly changing set of devices. Current research problems for user interfaces in UAR are software infrastructures, authoring tools and a supporting design process. We present case studies of how we have used a systematic design space analysis to carefully narrow the amount of available design options. The next step in our approach is to use interactive, possibly immersive tools to support interdisciplinary brainstorming sessions. Several tools are presented. We conclude by summarizing the lessons we have learned while applying our method.

USING A MOBILE ROBOT TO STUDY LOCUST COLLISION AVOIDANCE RESPONSES

1999 ◽  
Vol 09 (05) ◽  
pp. 405-410 ◽  
Author(s):  
MARK BLANCHARD ◽  
PAUL F. M. J. VERSCHURE ◽  
F. CLAIRE RIND
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Collision Avoidance ◽  
Neural Network Model ◽  
Real World ◽  
Biological System ◽  
Neural Circuits ◽  
Movement Detector ◽  
Visual Systems

The visual systems of insects perform complex processing using remarkably compact neural circuits, yet these circuits are often studied using simplified stimuli which fail to reveal their behaviour in more complex visual environments. We address this issue by testing models of these circuits in real-world visual environments using a mobile robot. In this paper we focus on the lobula giant movement detector (LGMD) system of the locust which responds selectively to objects which approach the animal on a collision course and is thought to trigger escape behaviours. We show that a neural network model of the LGMD system shares the preference for approaching objects and detects obstacles over a range of speeds. Our results highlight aspects of the basic response properties of the biological system which have important implications for the behavioural role of the LGMD.

scholarly journals Evaluation of PNT Error Limits Using Real World Close Encounters from AIS Data

2021 ◽  
Vol 9 (2) ◽  
pp. 149
Author(s):  
Evelin Engler ◽  
Paweł Banyś ◽  
Hans-Georg Engler ◽  
Michael Baldauf ◽  
Frank Sill Torres
Keyword(s):  
Collision Avoidance ◽  
Real World ◽  
Simplified Model ◽  
Automatic Identification ◽  
Identification System ◽  
Traffic Data ◽  
Close Encounters ◽  
Data Provision ◽  
Erroneous Data ◽  
Better Than

Collision avoidance is one of the main tasks on board ships to ensure safety at sea. To comply with this requirement, the direct ship environment, which is often modelled as the ship’s domain, has to be kept free of other vessels and objects. This paper addresses the question to which extent inaccuracies in position (P), navigation (N), and timing (T) data impact the reliability of collision avoidance. Employing a simplified model of the ship domain, the determined error bounds are used to derive requirements for ship-side PNT data provision. For this purpose, vessel traffic data obtained in the western Baltic Sea based on the automatic identification system (AIS) is analysed to extract all close encounters between ships considered as real-world traffic situations with a potential risk of collision. This study assumes that in these situations, erroneous data can lead to an incorrect assessment of the situation with regard to existing collision risks. The size of the error determines whether collisions are detected, spatially incorrectly assigned, or not detected. Therefore, the non-recognition of collision risks ultimately determines the limits of tolerable errors in the PNT data. The results indicate that under certain conditions, the probability of non-recognition of existing collision risks can reach non-negligible values, e.g., more than 1%, even though position accuracies are better than 10 m.

Sign in / Sign up

Export Citation Format

Share Document