Enhanced UAS Surveillance Using a Video Utility Metric

2013 ◽  
Vol 01 (02) ◽  
pp. 277-296 ◽  
Author(s):  
Peter C. Niedfeldt ◽  
Brandon T. Carroll ◽  
Joel A. Howard ◽  
Randal W. Beard ◽  
Bryan S. Morse ◽  
...  
Keyword(s):  
Planning Process ◽  
Optimal Path ◽  
Optimal Solution ◽  
Flight Test ◽  
Video Stream ◽  
Flight Path ◽  
Optimal Path Planning ◽  
Planning Approach ◽  
Human Operators ◽  
Imaging Sensors

A successful mission for an Unmanned Air System (UAS) often depends on the ability of human operators to utilize data collected from onboard imaging sensors. Many hours are spent preparing and executing flight objectives, putting a tremendous burden on human operators both before and during the flight. We seek to automate the planning process to reduce the workload for UAS operators while also optimizing the quality of the collected video stream. We first propose a metric based on an existing image utility metric to estimate the utility of video captured by onboard cameras. We then use this metric to not only plan the UAS flight path, but also the path of the camera's optical axis projected along the terrain and the zoom level. Since computing an optimal solution is NP-hard and therefore infeasible, we subsequently describe a staged sub-optimal path planning approach to autonomously plan the UAS flight path and sensor schedule. We apply these algorithms to precompute UAS and sensor paths for a surveillance mission over a specified region. Simulated and actual flight test results are included.

CELLULAR AUTOMATA AND OPTIMAL PATH PLANNING

1996 ◽  
Vol 06 (03) ◽  
pp. 603-610 ◽  
Author(s):  
M. STÄMPFLE
Keyword(s):  
Cellular Automata ◽  
Path Planning ◽  
Optimal Path ◽  
Shortest Paths ◽  
Optimal Solution ◽  
Starting Position ◽  
Optimal Path Planning ◽  
Time Space ◽  
Neural Network Theory ◽  
Optimal Configurations

Cellular automata are deterministic dynamical systems in which time, space, and state values are discrete. Although they consist of uniform elements, which interact only locally, cellular automata are capable of showing complex behavior. This property is exploited for solving path planning problems in workspaces with obstacles. A new automaton rule is presented which calculates simultaneously all shortest paths between a starting position and a target cell. Based on wave propagation, the algorithm ensures that the dynamics settles down in an equilibrium state which represents an optimal solution. Rule extensions include calculations with multiple starts and targets. The method allows applications on lattices and regular, weighted graphs of any finite dimension. In comparison with algorithms from graph theory or neural network theory, the cellular automaton approach has several advantages: Convergence towards optimal configurations is guaranteed, and the computing costs depend only linearly on the lattice size. Moreover, no floating-point calculations are involved.

Optimal path planning approach to Grid environment

2011 ◽  
Vol 6 (1) ◽  
pp. 131-140 ◽  
Author(s):  
Shanmugasundaram Suresh ◽  
Jeeva Poornaselvan ◽  
Chidambaram Divyapreya
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Grid Environment ◽  
Optimal Path Planning ◽  
Planning Approach

scholarly journals AGV optimal path planning based on improved ant colony algorithm

2018 ◽  
Vol 232 ◽  
pp. 03052 ◽  
Author(s):  
Chengwei He ◽  
Jian Mao
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Ant Colony ◽  
Undirected Network ◽  
Optimal Path Planning ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Improved Ant Colony Algorithm

Using the traditional Ant Colony Algorithm for AGV path planning is easy to fall into the local optimal solution and lacking the capability of obstacle avoidance in the complex storage environment. In this paper, by constructing the MAKLINK undirected network routes and the heuristic function is optimized in the Ant Colony Algorithm, then the AGV path reaches the global optimal path and has the ability to avoid obstacles. According to research, the improved Ant Colony Algorithm proposed in this paper is superior to the traditional Ant Colony Algorithm in terms of convergence speed and the distance of optimal path planning.

scholarly journals An adaptive dual-optimal path-planning technique for unmanned air vehicles

2016 ◽  
Vol 7 ◽  
pp. A2 ◽  
Author(s):  
Clifford A. Whitfield
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Optimal Path ◽  
Flight Path ◽  
Flight Duration ◽  
Multi Objective ◽  
Optimal Path Planning ◽  
Area Of Interest ◽  
Conflicting Objectives ◽  
Planning Technique

A multi-objective technique for unmanned air vehicle path-planning generation through task allocation has been developed. The dual-optimal path-planning technique generates real-time adaptive flight paths based on available flight windows and environmental influenced objectives. The environmentally-influenced flight condition determines the aircraft optimal orientation within a downstream virtual window of possible vehicle destinations that is based on the vehicle’s kinematics. The intermittent results are then pursued by a dynamic optimization technique to determine the flight path. This path-planning technique is a multi-objective optimization procedure consisting of two goals that do not require additional information to combine the conflicting objectives into a single-objective. The technique was applied to solar-regenerative high altitude long endurance flight which can benefit significantly from an adaptive real-time path-planning technique. The objectives were to determine the minimum power required flight paths while maintaining maximum solar power for continual surveillance over an area of interest (AOI). The simulated path generation technique prolonged the flight duration over a sustained turn loiter flight path by approximately 2 months for a year of flight. The potential for prolonged solar powered flight was consistent for all latitude locations, including 2 months of available flight at 60° latitude, where sustained turn flight was no longer capable.

Optimal Path Planning for Painting Robots

2010 ◽  
Author(s):  
A. Gasparetto ◽  
R. Vidoni ◽  
E. Saccavini ◽  
D. Pillan
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Optimal Solution ◽  
Minimum Length ◽  
Planning Problem ◽  
Optimal Path Planning ◽  
Chinese Postman ◽  
Velocity Motion ◽  
Learning Programming ◽  
Robotic Painting

In this work, a robotic painting task is addressed in order to automate and improve the efficiency of the process. Usually, path planning in robotic painting is done through self learning programming. Recently, different automated and semi-automated systems have been developed in order to avoid this procedure by using a CAD-drawing to create a CAD-guided trajectory for the paint gun, or by acquiring and recognizing the overall shape of the object to be painted within a library of prestored shapes with associated pre-defined paths. However, a general solution is still lacking, which enables one to overcome the need for a CAD-drawing and to deal with any kind of shapes. In this paper, graph theory and operative research techniques are applied to provide a general and optimal solution of the path planning problem for painting robots. The object to be painted is partitioned into primitives that can be represented by a graph. The Chinese Postman algorithm is then run on the graph in order to obtain a minimum length path covering all the arcs (Eulerian path). However, this path is not always optimal with respect to the constraints imposed by the painting process, hence dedicated algorithms have been developed in order to generate the optimal path in such cases. Based on the optimal path, the robot trajectories are planned by imposing a constant velocity motion of the spray gun, in order to ensure a uniform distribution of the paint over the object surface. The proposed system for optimal path planning has been implemented in a Matlab environment and extensively tested with excellent results in terms of time, costs and usability.

Method of evolving junctions: A new approach to optimal path-planning in 2D environments with moving obstacles

2017 ◽  
Vol 36 (4) ◽  
pp. 403-413 ◽  
Author(s):  
Wuchen Li ◽  
Shui-Nee Chow ◽  
Magnus Egerstedt ◽  
Jun Lu ◽  
Haomin Zho
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Optimal Solution ◽  
Optimization Method ◽  
Planning Problem ◽  
Optimal Path Planning ◽  
Moving Obstacles ◽  
Finite Dimensional ◽  
General Convex ◽  
Global Optimal

We propose a novel algorithm to find the global optimal path in 2D environments with moving obstacles, where the optimality is understood relative to a general convex continuous running cost. By leveraging the geometric structures of optimal solutions and using gradient flows, we convert the path-planning problem into a system of finite dimensional ordinary differential equations, whose dimensions change dynamically. Then a stochastic differential equation based optimization method, called intermittent diffusion, is employed to obtain the global optimal solution. We demonstrate, via numerical examples, that the new algorithm can solve the problem efficiently.

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