Sensor Fusion For Autonomous Navigation Using Neural Networks

1997 ◽  
pp. 145-157
Author(s):  
Ian Lane Davis ◽  
Anthony Stentz
Keyword(s):  
Neural Networks ◽  
Sensor Fusion ◽  
Autonomous Navigation

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

2020 ◽  
pp. 095965182097483
Author(s):  
Mahamat Loutfi Imrane ◽  
Achille Melingui ◽  
Joseph Jean Baptiste Mvogo Ahanda ◽  
Fredéric Biya Motto ◽  
Rochdi Merzouki
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Potential Field ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Artificial Potential Field ◽  
Type Reduction ◽  
Interval Type

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

scholarly journals SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications

2020 ◽  
Vol 34 (07) ◽  
pp. 10901-10908 ◽  
Author(s):  
Abdullah Hamdi ◽  
Matthias Mueller ◽  
Bernard Ghanem
Keyword(s):  
Neural Networks ◽  
Recent Work ◽  
Autonomous Navigation ◽  
General Framework ◽  
Deep Neural Networks ◽  
Autonomous Driving ◽  
Black Box ◽  
Semantic Meaning ◽  
Safety Critical ◽  
Adversarial Attack

One major factor impeding more widespread adoption of deep neural networks (DNNs) is their lack of robustness, which is essential for safety-critical applications such as autonomous driving. This has motivated much recent work on adversarial attacks for DNNs, which mostly focus on pixel-level perturbations void of semantic meaning. In contrast, we present a general framework for adversarial attacks on trained agents, which covers semantic perturbations to the environment of the agent performing the task as well as pixel-level attacks. To do this, we re-frame the adversarial attack problem as learning a distribution of parameters that always fools the agent. In the semantic case, our proposed adversary (denoted as BBGAN) is trained to sample parameters that describe the environment with which the black-box agent interacts, such that the agent performs its dedicated task poorly in this environment. We apply BBGAN on three different tasks, primarily targeting aspects of autonomous navigation: object detection, self-driving, and autonomous UAV racing. On these tasks, BBGAN can generate failure cases that consistently fool a trained agent.

The adaptability of assistive mobility devices and the role of the internet of medical things: A literature review (Preprint)

2021 ◽  
Author(s):  
Daniel OLADELE ◽  
Elisha Didam Markus ◽  
Adnan M. Abu-Mahfouz
Keyword(s):  
Sensor Fusion ◽  
Autonomous Navigation ◽  
The Internet ◽  
Training Time ◽  
Mobility Devices ◽  
The Status ◽  
Internet Of Medical Things ◽  
And Performance ◽  
Human Device

UNSTRUCTURED With the projected upsurge in the percentage of persons with some form of disability, there is a significant increase in the need for assistive mobility devices. However, these mobility aids are hardly effective without their ability to adapt to the user’s needs. This is achieved by improving the confidence of the information used or interaction between the user and his device also referred to as adaptation. In the recent past, there has been little effort to provide literature reviews on the adaptability of assistive mobility devices (AMDs). This paper systematically reviews the recent assistive mobility technologies, over the past decade, according to their adaptation and the role that the Internet of Medical Things (IoMT) has played in the adaptability of these technologies. The information gathered in the study provides awareness of the status of adaptive mobility technology and serves as a source and reference of information to healthcare professionals, and researchers. The paper starts by highlighting recent technologies according to the user system interface (human/device interface), then presents some recent technologies in perception and sensor fusion (autonomous navigation) for adaptability, and finally, IoMT frameworks for AMDs. Some notable limitations are also discussed. The findings of the review reveal that an improvement in the adaptation of assistive mobility systems would require a reduction in the training time and avoidance of cognitive overload. Furthermore, sensor fusion and classification accuracy are critical to achieving real-world testing requirements. Finally, the trade-off between cost and performance needs to be considered in the commercialization of these devices.

scholarly journals Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3096 ◽  
Author(s):  
Junfeng Xin ◽  
Shixin Li ◽  
Jinlu Sheng ◽  
Yongbo Zhang ◽  
Ying Cui
Keyword(s):  
Particle Swarm Optimization ◽  
Real Time ◽  
Sensor Fusion ◽  
Autonomous Navigation ◽  
Particle Swarm ◽  
Sensor Data ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Unmanned Surface Vehicles ◽  
Inertia Weight

Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion.

scholarly journals Efficient Training of Artificial Neural Networks for Autonomous Navigation

1991 ◽  
Vol 3 (1) ◽  
pp. 88-97 ◽  
Author(s):  
Dean A. Pomerleau
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Autonomous Navigation ◽  
Training Techniques ◽  
Human Driver ◽  
Backpropagation Network ◽  
Artificial Neural ◽  
Network Project

The ALVINN (Autonomous Land Vehicle In a Neural Network) project addresses the problem of training artificial neural networks in real time to perform difficult perception tasks. ALVINN is a backpropagation network designed to drive the CMU Navlab, a modified Chevy van. This paper describes the training techniques that allow ALVINN to learn in under 5 minutes to autonomously control the Navlab by watching the reactions of a human driver. Using these techniques, ALVINN has been trained to drive in a variety of circumstances including single-lane paved and unpaved roads, and multilane lined and unlined roads, at speeds of up to 20 miles per hour.

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