Innovative Path Planning Algorithm for an Autonomous Robot with Low Computational Cost

2019 ◽  
Author(s):  
Sachintha Balasooriya ◽  
Ilya Kavalchuk ◽  
Eric Dimla
Keyword(s):  
Path Planning ◽  
Computational Cost ◽  
Autonomous Robot ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Low Computational Cost

scholarly journals Environment Mapping Robot Using Supersonic Sensor

1999 ◽  
Vol 11 (6) ◽  
pp. 468-472
Author(s):  
Masafumi Uchida ◽  
◽  
Tanaka Hisaya ◽  
Hideto Ide ◽  
Keyword(s):  
Computer Simulation ◽  
Path Planning ◽  
Autonomous Robot ◽  
Working Environment ◽  
Occupancy Grid ◽  
Environment Map ◽  
Planning Algorithm ◽  
And Performance ◽  
Path Planning Algorithm ◽  
Range Sensors

We studied an automapping algorithm for an autonomous robot having ultrasonic range sensors. A robot with a working environment map operates smoothly. The robot consisted of an automapping algorithm using ultrasonic range sensors and a path planning algorithm. Ultrasonic range sensors are basic, inexpensive, and compact. We proposed an automapping algorithm introducing a parameter, valid length, for a robot with ultrasonic range sensors. The map was based on an occupancy grid. Computer simulation confirmed the effectiveness of introducing valid length in mapping by an autonomous robot. We discuss proposed distinctions and performance.

scholarly journals HCTNav: A Path Planning Algorithm for Low-Cost Autonomous Robot Navigation in Indoor Environments

2013 ◽  
Vol 2 (3) ◽  
pp. 729-748 ◽  
Author(s):  
Marco Pala ◽  
Nafiseh Eraghi ◽  
Fernando López-Colino ◽  
Alberto Sanchez ◽  
Angel de Castro ◽  
...  
Keyword(s):  
Path Planning ◽  
Robot Navigation ◽  
Low Cost ◽  
Autonomous Robot ◽  
Indoor Environments ◽  
Planning Algorithm ◽  
Autonomous Robot Navigation ◽  
Path Planning Algorithm

scholarly journals Ellipsoidal Path Planning for Unmanned Aerial Vehicles

2021 ◽  
Vol 11 (17) ◽  
pp. 7997
Author(s):  
Carlos Villaseñor ◽  
Alberto A. Gallegos ◽  
Gehova Lopez-Gonzalez ◽  
Javier Gomez-Avila ◽  
Jesus Hernandez-Barragan ◽  
...  
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Clustering Algorithms ◽  
Computational Cost ◽  
Mapping Algorithm ◽  
Planning Strategy ◽  
Aerial Vehicles ◽  
Planning Algorithm ◽  
Trained Neural Network ◽  
Path Planning Algorithm

The research in path planning for unmanned aerial vehicles (UAV) is an active topic nowadays. The path planning strategy highly depends on the map abstraction available. In a previous work, we presented an ellipsoidal mapping algorithm (EMA) that was designed using covariance ellipsoids and clustering algorithms. The EMA computes compact in-memory maps, but still with enough information to accurately represent the environment and to be useful for robot navigation algorithms. In this work, we develop a novel path planning algorithm based on a bio-inspired algorithm for navigation in the ellipsoidal map. Our approach overcomes the problem that there is no closed formula to calculate the distance between two ellipsoidal surfaces, so it was approximated using a trained neural network. The presented path planning algorithm takes advantage of ellipsoid entities to represent obstacles and compute paths for small UAVs regardless of the concavity of these obstacles, in a very geometrically explicit way. Furthermore, our method can also be used to plan routes in dynamical environments without adding any computational cost.

scholarly journals Value Iteration Networks with Double Estimator for Planetary Rover Path Planning

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8418
Author(s):  
Xiang Jin ◽  
Wei Lan ◽  
Tianlin Wang ◽  
Pengyao Yu
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Computational Cost ◽  
Poor Performance ◽  
Value Iteration ◽  
Training Strategy ◽  
Large Size ◽  
Planning Algorithm ◽  
Value Estimation ◽  
Path Planning Algorithm

Path planning technology is significant for planetary rovers that perform exploration missions in unfamiliar environments. In this work, we propose a novel global path planning algorithm, based on the value iteration network (VIN), which is embedded within a differentiable planning module, built on the value iteration (VI) algorithm, and has emerged as an effective method to learn to plan. Despite the capability of learning environment dynamics and performing long-range reasoning, the VIN suffers from several limitations, including sensitivity to initialization and poor performance in large-scale domains. We introduce the double value iteration network (dVIN), which decouples action selection and value estimation in the VI module, using the weighted double estimator method to approximate the maximum expected value, instead of maximizing over the estimated action value. We have devised a simple, yet effective, two-stage training strategy for VI-based models to address the problem of high computational cost and poor performance in large-size domains. We evaluate the dVIN on planning problems in grid-world domains and realistic datasets, generated from terrain images of a moon landscape. We show that our dVIN empirically outperforms the baseline methods and generalize better to large-scale environments.

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