Multidrone aerial surveys of penguin colonies in Antarctica

2020 ◽  
Vol 5 (47) ◽  
pp. eabc3000
Author(s):  
Kunal Shah ◽  
Grant Ballard ◽  
Annie Schmidt ◽  
Mac Schwager
Keyword(s):  
Path Planning ◽  
Disaster Response ◽  
Shortest Paths ◽  
Weather Conditions ◽  
Satisfiability Modulo Theory ◽  
Geometric Patterns ◽  
Dynamic Movement ◽  
Aerial Surveys ◽  
Current Survey ◽  
Planning Algorithm

Speed is essential in wildlife surveys due to the dynamic movement of animals throughout their environment and potentially extreme changes in weather. In this work, we present a multirobot path-planning method for conducting aerial surveys over large areas designed to make the best use of limited flight time. Unlike current survey path-planning solutions based on geometric patterns or integer programs, we solve a series of satisfiability modulo theory instances of increasing complexity. Each instance yields a set of feasible paths at each iteration and recovers the set of shortest paths after sufficient time. We implemented our planning algorithm with a team of drones to conduct multiple photographic aerial wildlife surveys of Cape Crozier, one of the largest Adélie penguin colonies in the world containing more than 300,000 nesting pairs. Over 2 square kilometers was surveyed in about 3 hours. In contrast, previous human-piloted single-drone surveys of the same colony required over 2 days to complete. Our method reduces survey time by limiting redundant travel while also allowing for safe recall of the drones at any time during the survey. Our approach can be applied to other domains, such as wildfire surveys in high-risk weather conditions or disaster response.

scholarly journals Path planning for automatic guided vehicle with multiple target points in dynamic environment

2018 ◽  
Vol 159 ◽  
pp. 02029 ◽  
Author(s):  
Chang Kyu Kim ◽  
Huy Hung Nguyen ◽  
Dae Hwan Kim ◽  
Hak Kyeong Kim ◽  
Sang Bong Kim
Keyword(s):  
Path Planning ◽  
Shortest Paths ◽  
Initial Point ◽  
Target Point ◽  
Laser Sensor ◽  
Multiple Target ◽  
Hamilton Path ◽  
Automatic Guided Vehicle ◽  
Planning Algorithm ◽  
Path Planning Algorithm

In path planning field, Automatic guided vehicle (AGV) has to move from an initial point towards a target point with capability to avoid obstacles. There are A*, D* and D* lite path planning algorithms in the path planning algorithm. This paper proposes a modified D* lite path planning algorithm using the most efficient D* lite among these algorithms. The modified D* lite path planning algorithm is to improve these D* lite path planning algorithm’s weaknesses such as traversing across obstacles sharp corners, or traversing between two obstacles. To do this task, the followings are done. First, a work space is divided into square cells. Second, cost of each edge connecting current node to neighbor nodes is calculated. Third, the shortest paths from the initial point to all multiple target points are computed and the shortest paths from any target point to remaining target points including the goal point are computed by using Hamilton path. Fourth, a cost-minimal path is re-calculated as soon as the laser sensor detects an obstacle and make an updated list of target points. Finally, the validity of the proposed modified D* lite path planning algorithm is verified through simulation and experimental results.

CWave: Theory and Practice of a Fast Single-source Any-angle Path Planning Algorithm

Robotica ◽  
2019 ◽  
Vol 38 (2) ◽  
pp. 207-234
Author(s):  
Dmitry A. Sinyukov ◽  
Taşkin Padir
Keyword(s):  
Path Planning ◽  
High Performance ◽  
Shortest Paths ◽  
Graph Model ◽  
Theory And Practice ◽  
Single Source ◽  
Planning Algorithm ◽  
Improved Performance ◽  
Path Planning Algorithm ◽  
Future Work

SummaryPath planning on a two-dimensional grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid given that some of the grid cells are impassable (occupied by obstacles). Single-source path planning finds shortest paths from a given source vertex to all other vertices of the grid. Singles-source path planning enhances robot autonomy by calculating multiple possible paths for various navigation scenarios when the destination state is unknown. A high-performance algorithm for single-source any-angle path planning on a grid called CWave is proposed here. Any-angle attribute implies that the algorithm calculates paths which can include line segments at any angle, as opposed to standard A* that runs on an 8-connected graph, which permits turns with 45° increments only. The key idea of CWave is to abandon the graph model and operate directly on the grid geometry using discrete geometric primitives (instead of individual vertices) to represent the wave front. In its most basic form (CWaveInt), CWave requires only integer arithmetics. CWaveInt, however, can accumulate the distance error at turning points. A modified version of CWave (CWaveFpuSrc) with minimal usage of floating-point calculations is also developed to eliminate any accumulative errors, which is proven mathematically and experimentally on several maps. The performance of the algorithm on most of the tested maps is demonstrated to be significantly faster than that of Theta*, Lazy Theta*, Field A*, ANYA, Block A*, and A* adapted for single-source planning (on maps with lower number of isolated obstacles, CWaveFpuSrc is 2−3 times faster than its fastest tested alternative Block A*). An N-threaded implementation (CWaveN) of CWave is presented and tested to demonstrate an improved performance (multithreaded implementation is 1.5−3 times faster than single-threaded CWave). The paper discusses foundations and experimental validation of CWave, and presents future work to address the limitations of the current implementations and obtain further performance enhancements.

scholarly journals GPS-Free, Error Tolerant Path Planning for Swarms of Micro Aerial Vehicles with Quality Amplification

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4731
Author(s):  
Michel Barbeau ◽  
Joaquin Garcia-Alfaro ◽  
Evangelos Kranakis ◽  
Fillipe Santos
Keyword(s):  
Path Planning ◽  
A Priori ◽  
Weather Conditions ◽  
System Quality ◽  
A Priori Information ◽  
Visual Landmarks ◽  
Planning Algorithm ◽  
Priori Information ◽  
Recognition Errors ◽  
Path Planning Algorithm

We present an error tolerant path planning algorithm for Micro Aerial Vehicle (MAV) swarms. We assume navigation without GPS-like techniques. The MAVs find their path using sensors and cameras, identifying and following a series of visual landmarks. The visual landmarks lead the MAVs towards their destination. MAVs are assumed to be unaware of the terrain and locations of the landmarks. They hold a priori information about landmarks, whose interpretation is prone to errors. Errors are of two types, recognition or advice. Recognition errors follow from misinterpretation of sensed data or a priori information, or confusion of objects, e.g., due to faulty sensors. Advice errors are consequences of outdated or wrong information about landmarks, e.g., due to weather conditions. Our path planning algorithm is cooperative. MAVs communicate and exchange information wirelessly, to minimize the number of recognition and advice errors. Hence, the quality of the navigation decision process is amplified. Our solution successfully achieves an adaptive error tolerant navigation system. Quality amplification is parameterized with respect to the number of MAVs. We validate our approach with theoretical proofs and numeric simulations.

Improved RRT-Connect path planning algorithm for biped robot

2013 ◽  
Vol 33 (8) ◽  
pp. 2289-2292 ◽  
Author(s):  
Dongcheng MO ◽  
Guodong LIU
Keyword(s):  
Path Planning ◽  
Biped Robot ◽  
Planning Algorithm ◽  
Path Planning Algorithm

scholarly journals A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

2021 ◽  
Vol 9 (3) ◽  
pp. 252
Author(s):  
Yushan Sun ◽  
Xiaokun Luo ◽  
Xiangrui Ran ◽  
Guocheng Zhang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Small Scale ◽  
Target Point ◽  
Safe Driving ◽  
Policy Gradient ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

scholarly journals Intelligent Path Planning Algorithm for UAV Group Based on Machine Learning

2021 ◽  
Vol 1865 (4) ◽  
pp. 042118
Author(s):  
Ronghao Li ◽  
Yuhan Xiong ◽  
Tao Zhang
Keyword(s):  
Machine Learning ◽  
Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm
Sign in / Sign up

Export Citation Format

Share Document