scholarly journals Acceleration-Aware Path Planning with Waypoints

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 143
Author(s):  
Rudolf Ortner ◽  
Indrajit Kurmi ◽  
Oliver Bimber
Keyword(s):  
Path Planning ◽  
Probability Distributions ◽  
Search And Rescue ◽  
Optical Sectioning ◽  
Field Methods ◽  
Autonomous Search ◽  
Acceleration And Deceleration ◽  
Probability Accumulation ◽  
Time Critical ◽  
Local Gradient

In this article we demonstrate that acceleration and deceleration of direction-turning drones at waypoints have a significant influence to path planning which is important to be considered for time-critical applications, such as drone-supported search and rescue. We present a new path planning approach that takes acceleration and deceleration into account. It follows a local gradient ascend strategy which locally minimizes turns while maximizing search probability accumulation. Our approach outperforms classic coverage-based path planning algorithms, such as spiral- and grid-search, as well as potential field methods that consider search probability distributions. We apply this method in the context of autonomous search and rescue drones and in combination with a novel synthetic aperture imaging technique, called Airborne Optical Sectioning (AOS), which removes occlusion of vegetation and forest in real-time.

scholarly journals An autonomous drone for search and rescue in forests using airborne optical sectioning

2021 ◽  
Vol 6 (55) ◽  
pp. eabg1188
Author(s):  
D. C. Schedl ◽  
I. Kurmi ◽  
O. Bimber
Keyword(s):  
Adaptive Sampling ◽  
Field Experiments ◽  
Sampling Technique ◽  
Flight Path ◽  
Search And Rescue ◽  
Network Coverage ◽  
Optical Sectioning ◽  
Average Precision ◽  
Time Critical ◽  
Online Sampling

Autonomous drones will play an essential role in human-machine teaming in future search and rescue (SAR) missions. We present a prototype that finds people fully autonomously in densely occluded forests. In the course of 17 field experiments conducted over various forest types and under different flying conditions, our drone found, in total, 38 of 42 hidden persons. For experiments with predefined flight paths, the average precision was 86%, and we found 30 of 34 cases. For adaptive sampling experiments (where potential findings are double-checked on the basis of initial classification confidences), all eight hidden persons were found, leading to an average precision of 100%, whereas classification confidence was increased on average by 15%. Thermal image processing, classification, and dynamic flight path adaptation are computed on-board in real time and while flying. We show that deep learning–based person classification is unaffected by sparse and error-prone sampling within straight flight path segments. This finding allows search missions to be substantially shortened and reduces the image complexity to 1/10th when compared with previous approaches. The goal of our adaptive online sampling technique is to find people as reliably and quickly as possible, which is essential in time-critical applications, such as SAR. Our drone enables SAR operations in remote areas without stable network coverage, because it transmits to the rescue team only classification results that indicate detections and can thus operate with intermittent minimal-bandwidth connections (e.g., by satellite). Once received, these results can be visually enhanced for interpretation on remote mobile devices.

scholarly journals Scalable information-theoretic path planning for a rover-helicopter team in uncertain environments

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142199958
Author(s):  
Larkin Folsom ◽  
Masahiro Ono ◽  
Kyohei Otsu ◽  
Hyoshin Park
Keyword(s):  
Path Planning ◽  
Lower Risk ◽  
Information Gain ◽  
Probability Distributions ◽  
Single Agent ◽  
Uncertain Environments ◽  
Information Theoretic ◽  
Highly Correlated ◽  
Naive Approach

Mission-critical exploration of uncertain environments requires reliable and robust mechanisms for achieving information gain. Typical measures of information gain such as Shannon entropy and KL divergence are unable to distinguish between different bimodal probability distributions or introduce bias toward one mode of a bimodal probability distribution. The use of a standard deviation (SD) metric reduces bias while retaining the ability to distinguish between higher and lower risk distributions. Areas of high SD can be safely explored through observation with an autonomous Mars Helicopter allowing safer and faster path plans for ground-based rovers. First, this study presents a single-agent information-theoretic utility-based path planning method for a highly correlated uncertain environment. Then, an information-theoretic two-stage multiagent rapidly exploring random tree framework is presented, which guides Mars helicopter through regions of high SD to reduce uncertainty for the rover. In a Monte Carlo simulation, we compare our information-theoretic framework with a rover-only approach and a naive approach, in which the helicopter scouts ahead of the rover along its planned path. Finally, the model is demonstrated in a case study on the Jezero region of Mars. Results show that the information-theoretic helicopter improves the travel time for the rover on average when compared with the rover alone or with the helicopter scouting ahead along the rover’s initially planned route.

Coverage path planning for maritime search and rescue using reinforcement learning

2021 ◽  
Vol 241 ◽  
pp. 110098
Author(s):  
Bo Ai ◽  
Maoxin Jia ◽  
Hanwen Xu ◽  
Jiangling Xu ◽  
Zhen Wen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Search And Rescue ◽  
Coverage Path Planning ◽  
Maritime Search And Rescue

scholarly journals Thermal Airborne Optical Sectioning

2019 ◽  
Vol 11 (14) ◽  
pp. 1668 ◽  
Author(s):  
Indrajit Kurmi ◽  
David C. Schedl ◽  
Oliver Bimber
Keyword(s):  
Search And Rescue ◽  
Synthetic Aperture ◽  
Heat Radiation ◽  
Border Control ◽  
Thermal Camera ◽  
Optical Sectioning ◽  
Control Applications ◽  
Precise Estimation ◽  
Rgb Images ◽  
Position And Orientation

We apply a multi-spectral (RGB and thermal) camera drone for synthetic aperture imaging to computationally remove occluding vegetation for revealing hidden objects, as required in archeology, search-and-rescue, animal inspection, and border control applications. The radiated heat signal of strongly occluded targets, such as a human bodies hidden in dense shrub, can be made visible by integrating multiple thermal recordings from slightly different perspectives, while being entirely invisible in RGB recordings or unidentifiable in single thermal images. We collect bits of heat radiation through the occluder volume over a wide synthetic aperture range and computationally combine them to a clear image. This requires precise estimation of the drone’s position and orientation for each capturing pose, which is supported by applying computer vision algorithms on the high resolution RGB images.

Search and Rescue Robot Path Planning in Unknown Environment

2012 ◽  
Vol 241-244 ◽  
pp. 1682-1687 ◽  
Author(s):  
Tao Pang ◽  
Xiao Gang Ruan ◽  
Er Shen Wang ◽  
Rui Yuan Fan
Keyword(s):  
Path Planning ◽  
Learning Algorithm ◽  
Optimal Path ◽  
Local Environment ◽  
Search And Rescue ◽  
Planning Problem ◽  
Self Organizing Map ◽  
Initial State ◽  
Rescue Robot ◽  
Unknown Environment

For the path planning problem of search and rescue robot in unknown environment, a bionic learning algorithm was proposed. The GSOM (Growing Self-organizing Map) algorithm was used to build the environment cognitive map. The heuristic search A* algorithm was used to find the global optimal path from initial state to target state. When the local environment was changed, reinforcement learning algorithm based on sensor information was used to guide the search and rescue robot behavior of local path planning. Simulation results show the method effectiveness.

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