Path Planning with Objectives Minimum Length and Maximum Clearance

This paper presents a new coverage flight path planning algorithm that finds collision-free, minimum length and flyable paths for unmanned aerial vehicle (UAV) navigation in three-dimensional (3D) urban environments with fixed obstacles for coverage missions. The proposed algorithm significantly reduces computational time, number of turns, and path overlapping while finding a path that passes over all reachable points of an area or volume of interest by using sensor footprints’ sweeps fitting and a sparse waypoint graph in the pathfinding process. We devise a novel footprints’ sweep fitting method considering UAV sensor footprint as coverage unit in the free spaces to achieve maximal coverage with fewer and longer footprints’ sweeps. After footprints’ sweeps fitting, the proposed algorithm determines the visiting sequence of footprints’ sweeps by formulating it as travelling salesman problem (TSP), and ant colony optimization (ACO) algorithm is employed to solve the TSP. Furthermore, we generate a sparse waypoint graph by connecting footprints’ sweeps’ endpoints to obtain a complete coverage flight path. The simulation results obtained from various scenarios fortify the effectiveness of the proposed algorithm and verify the aforementioned claims.

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Path planning by fractional differentiation

Robotica ◽

10.1017/s0263574702004319 ◽

2003 ◽

Vol 21 (1) ◽

pp. 59-69 ◽

Cited By ~ 12

Author(s):

A. Oustaloup ◽

B. Orsoni ◽

P. Melchior ◽

H. Linarès

Keyword(s):

Path Planning ◽

Real Time ◽

Charge Distribution ◽

Geodesic Distance ◽

Potential Fields ◽

Minimum Length ◽

Fractional Differentiation ◽

Distance Map ◽

Risk Coefficient ◽

Curvature Continuity

In path planning design, potential fields can introduce force constraints to ensure curvature continuity of trajectories and thus facilitate path-tracking design. The parametric thrift of fractional potentials permits smooth variations of the potential in function of the distance to obstacles without requiring design of geometric charge distribution. In the approach we use, the fractional order of differentiation is the risk coefficient associated to obstacles. A convex danger map towards a target and a convex geodesic distance map are defined. Real-time computation can also lead to the shortest minimum danger trajectory, or to the least dangerous of minimum length trajectories.

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Optimal Multirobot Coverage Path Planning: Ideal-Shaped Spanning Tree

Mathematical Problems in Engineering ◽

10.1155/2018/3436429 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Chunqing Gao ◽

Yingxin Kou ◽

Zhanwu Li ◽

An Xu ◽

You Li ◽

...

Keyword(s):

Path Planning ◽

Spanning Tree ◽

Spanning Trees ◽

Optimal Path ◽

Polynomial Algorithms ◽

Minimum Length ◽

Second Phase ◽

Complete Coverage ◽

Preparation Time ◽

Coverage Path Planning

The present paper attempts to find the optimal coverage path for multiple robots in a given area including obstacles. For single robot coverage path planning (CPP) problem, an improved ant colony optimization (ACO) algorithm is proposed to construct the best spanning tree and then obtain the optimal path, which contributes to minimizing the energy/time consumption. For the multirobot case, first the DARP (Divide Areas based on Robots Initial Positions) algorithm is utilized to divide the area into separate equal subareas, so much so that it transforms the mCPP problem into several CPP problems, degrading the computation complexity. During the second phase, spanning tree in each subarea is constructed by the aforementioned algorithm. In the last phase, the specific end nodes are exchanged among subareas to achieve ideal-shaped spanning trees, which can also decrease the number of turns in coverage path. And the complete algorithms are proven to be approximately polynomial algorithms. Finally, the simulation confirms the complete algorithms’ advantages: complete coverage, nonbacktracks, minimum length, zero preparation time, and the least number of turns.

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Optimal Path Planning for Painting Robots

ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis, Volume 3 ◽

10.1115/esda2010-24259 ◽

2010 ◽

Cited By ~ 3

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.

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Clustering in discrete path planning for approximating minimum length paths

2017 American Control Conference (ACC) ◽

10.23919/acc.2017.7963402 ◽

2017 ◽

Cited By ~ 1

Author(s):

Frank Imeson ◽

Stephen L. Smith

Keyword(s):

Path Planning ◽

Minimum Length

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A Multi-Objective Coverage Path Planning Algorithm for UAVs to Cover Spatially Distributed Regions in Urban Environments

Aerospace ◽

10.3390/aerospace8110343 ◽

2021 ◽

Vol 8 (11) ◽

pp. 343

Author(s):

Abdul Majeed ◽

Seong Oun Hwang

Keyword(s):

Path Planning ◽

Low Cost ◽

Computing Time ◽

Urban Environments ◽

Minimum Length ◽

Multi Objective ◽

Area Of Interest ◽

Spatially Distributed ◽

Planning Algorithm ◽

Path Planning Algorithm

This paper presents a multi-objective coverage flight path planning algorithm that finds minimum length, collision-free, and flyable paths for unmanned aerial vehicles (UAV) in three-dimensional (3D) urban environments inhabiting multiple obstacles for covering spatially distributed regions. In many practical applications, UAVs are often required to fully cover multiple spatially distributed regions located in the 3D urban environments while avoiding obstacles. This problem is relatively complex since it requires the optimization of both inter (e.g., traveling from one region/city to another) and intra-regional (e.g., within a region/city) paths. To solve this complex problem, we find the traversal order of each area of interest (AOI) in the form of a coarse tour (i.e., graph) with the help of an ant colony optimization (ACO) algorithm by formulating it as a traveling salesman problem (TSP) from the center of each AOI, which is subsequently optimized. The intra-regional path finding problem is solved with the integration of fitting sensors’ footprints sweeps (SFS) and sparse waypoint graphs (SWG) in the AOI. To find a path that covers all accessible points of an AOI, we fit fewer, longest, and smooth SFSs in such a way that most parts of an AOI can be covered with fewer sweeps. Furthermore, the low-cost traversal order of each SFS is computed, and SWG is constructed by connecting the SFSs while respecting the global and local constraints. It finds a global solution (i.e., inter + intra-regional path) without sacrificing the guarantees on computing time, number of turning maneuvers, perfect coverage, path overlapping, and path length. The results obtained from various representative scenarios show that proposed algorithm is able to compute low-cost coverage paths for UAV navigation in urban environments.

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