scholarly journals EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs

2020 ◽  
Vol 12 (2) ◽  
pp. 29 ◽  
Author(s):  
Alia Ghaddar ◽  
Ahmad Merei
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Second Phase ◽  
Energy Aware ◽  
Coverage Path Planning ◽  
Scene Information ◽  
Two Phases ◽  
3D Scene ◽  
Top View ◽  
Grid Based

The presence of obstacles like a tree, buildings, or birds along the path of a drone has the ability to endanger and harm the UAV’s flight mission. Avoiding obstacles is one of the critical challenging keys to successfully achieve a UAV’s mission. The path planning needs to be adapted to make intelligent and accurate avoidance online and in time. In this paper, we propose an energy-aware grid based solution for obstacle avoidance (EAOA). Our work is based on two phases: in the first one, a trajectory path is generated offline using the area top-view. The second phase depends on the path obtained in the first phase. A camera captures a frontal view of the scene that contains the obstacle, then the algorithm determines the new position where the drone has to move to, in order to bypass the obstacle. In this paper, the obstacles are static. The results show a gain in energy and completion time using 3D scene information compared to 2D scene information.

scholarly journals Energy-aware Coverage Path Planning for Unmanned Aerial Vehicles

2020 ◽  
Author(s):  
Tauã Cabreira ◽  
Lisane Brisolara ◽  
Paulo Ferreira Jr.
Keyword(s):  
Energy Consumption ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Partial Information ◽  
State Of The Art ◽  
Energy Aware ◽  
Coverage Path Planning ◽  
Aerial Vehicles ◽  
Novel Approaches ◽  
Grid Based

Coverage Path Planning (CPP) problem is a motion planning subtopic in robotics, where it is necessary to build a path for a robot to explore every location in a given scenario. Unmanned Aerial Vehicles (UAV) have been employed in several applications related to the CPP problem. However, one of the significant limitations of UAVs is endurance, especially in multi-rotors. Minimizing energy consumption is pivotal to prolong and guarantee coverage. Thus, this work proposes energy-aware coverage path planning solutions for regular and irregular-shaped areas containing full and partial information. We consider aspects such as distance, time, turning maneuvers, and optimal speed in the UAV’s energy consumption. We propose an energy-aware spiral algorithm called E-Spiral to perform missions over regular-shaped areas. Next, we explore an energy-aware grid-based solution called EG-CPP for mapping missions over irregular-shaped areas containing no-fly zones. Finally, we present an energy-aware pheromone-based solution for patrolling missions called NC-Drone. The three novel approaches successfully address different coverage path planning scenarios, advancing the state-of-the-art in this area.

Coverage Path Planning with Proximal Policy Optimization in a Grid-based Environment

2020 ◽  
Author(s):  
Aleksandr Ianenko ◽  
Alexander Artamonov ◽  
Georgii Sarapulov ◽  
Alexey Safaraleev ◽  
Sergey Bogomolov ◽  
...  
Keyword(s):  
Path Planning ◽  
Coverage Path Planning ◽  
Policy Optimization ◽  
Grid Based

scholarly journals PPS: Energy-Aware Grid-Based Coverage Path Planning for UAVs Using Area Partitioning in the Presence of NFZs

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3742
Author(s):  
Alia Ghaddar ◽  
Ahmad Merei ◽  
Enrico Natalizio
Keyword(s):  
Minimum Energy ◽  
Energy Aware ◽  
Coverage Path Planning ◽  
Minimum Energy Consumption ◽  
Aerial Vehicle ◽  
Monitoring And Surveillance ◽  
Area Monitoring ◽  
Grid Based ◽  
Quality Of Coverage

Area monitoring and surveillance are some of the main applications for Unmanned Aerial Vehicle (UAV) networks. The scientific problem that arises from this application concerns the way the area must be covered to fulfill the mission requirements. One of the main challenges is to determine the paths for the UAVs that optimize the usage of resources while minimizing the mission time. Different approaches rely on area partitioning strategies. Depending on the size and complexity of the area to monitor, it is possible to decompose it exactly or approximately. This paper proposes a partitioning method called Parallel Partitioning along a Side (PPS). In the proposed method, grid-mapping and grid-subdivision of the area, as well as area partitioning are performed to plan the UAVs path. An extra challenge, also tackled in this work, is the presence of non-flying zones (NFZs). These zones are areas that UAVs must not cover or pass over it. The proposal is extensively evaluated, in comparison with existing approaches, to show that it enables UAVs to plan paths with minimum energy consumption, number of turns and completion time while at the same time increases the quality of coverage.

Complete Coverage Path Planning and Obstacle Avoidance Strategy of the Robot

2013 ◽  
Vol 756-759 ◽  
pp. 497-503 ◽  
Author(s):  
Jun Hui Wu ◽  
Tong Di Qin ◽  
Jie Chen ◽  
Hui Ping Si ◽  
Kai Yan Lin ◽  
...  
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
High Efficiency ◽  
Control Flow ◽  
Single Chip ◽  
Single Chip Microcomputer ◽  
Complete Coverage ◽  
Coverage Path Planning ◽  
Avoidance Strategy ◽  
Traversal Algorithm

In order to solve the problems of complete coverage path and obstacle avoidance with the mobile robot, the complete coverage planning was described first, and then the algorithm of the complete coverage path planning was analyzed. The complete traversal algorithm and the obstacle avoidance strategy of the robot around the barrier were put forward. Finally, the traversal control flow chart of the traversal robot implemented in Single Chip Microcomputer (SCM) was obtained. After the above analysis, the algorithm was simple, practical, and low repeatability, and high efficiency. The algorithms could effectively solve the difficulty of complete coverage path and obstacle avoidance with the robot.

scholarly journals Optimal Multirobot Coverage Path Planning: Ideal-Shaped Spanning Tree

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
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.

scholarly journals Hybrid Spiral STC-Hedge Algebras Model in Knowledge Reasonings for Robot Coverage Path Planning and Its Applications

2019 ◽  
Vol 9 (9) ◽  
pp. 1909 ◽  
Author(s):  
Hai Van Pham ◽  
Farzin Asadi ◽  
Nurettin Abut ◽  
Ismet Kandilli
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Research Effort ◽  
Dynamic Environments ◽  
Coverage Path Planning ◽  
Knowledge Reasoning ◽  
Cleaning Robot ◽  
Novel Approach ◽  
Proposed Model ◽  
Tree Coverage

Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.

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