Approach of Kinematic Control for a Nonholonomic Wheeled Robot using Artificial Neural Networks and Genetic Algorithms

Cáceres, Camilo; Rosário, João Maurício; Amaya, Darío

doi:10.1109/iwobi.2017.7985533

Cited by 19 publications

(8 citation statements)

References 18 publications

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“…Early methods used Dynamics Mode Decomposition (DMD) [41] and Sparse Identification of Non-Linear Dynamics (SINDy) [42] to learn data-driven models based on system identification and performed terrain navigation [43], [44]. Later, evolutionary algorithms were developed to optimize parameters of a robot model in an online learning fashion for robust navigation [45], [46]. For robots with multiple degrees of freedom, methods were developed that use a combination of iterative Linear Quadratic Regulators (iLQR) and machine learning search to explore multiple robot configurations and plan self-adaptive navigation [47].…”

Section: Related Workmentioning

confidence: 99%

NAUTS: Negotiation for Adaptation to Unstructured Terrain Surfaces

Siva¹,

Wigness²,

Rogers³

et al. 2022

Preprint

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When robots operate in real-world off-road environments with unstructured terrains, the ability to adapt their navigational policy is critical for effective and safe navigation. However, off-road terrains introduce several challenges to robot navigation, including dynamic obstacles and terrain uncertainty, leading to inefficient traversal or navigation failures. To address these challenges, we introduce a novel approach for adaptation by negotiation that enables a ground robot to adjust its navigational behaviors through a negotiation process. Our approach first learns prediction models for various navigational policies to function as a terrain-aware joint local controller and planner. Then, through a new negotiation process, our approach learns from various policies' interactions with the environment to agree on the optimal combination of policies in an online fashion to adapt robot navigation to unstructured off-road terrains on the fly. Additionally, we implement a new optimization algorithm that offers the optimal solution for robot negotiation in real-time during execution. Experimental results have validated that our method for adaptation by negotiation outperforms previous methods for robot navigation, especially over unseen and uncertain dynamic terrains.

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Section: Related Workmentioning

confidence: 99%

NAUTS: Negotiation for Adaptation to Unstructured Terrain Surfaces

Siva¹,

Wigness²,

Rogers³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…NEAT was devised to overcome this limitation and is capable of optimizing both the weight and the topology at the same time. The evolution of the ANN is implemented through a specially tuned genetic algorithm, similarly to other works, e.g., from Buk et al [42] and Caceres et al [43] especially, where the implementation allowed the navigation of a structured environment with static obstacles.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Crowded Environment Navigation with NEAT: Impact of Perception Resolution on Controller Optimization

Seriani

Marcini

Caruso

et al. 2021

J Intell Robot Syst

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Crowd navigation with autonomous systems is a topic which has seen a rapid increase in interest recently. While it appears natural to humans, being able to reach a target can prove difficult or impossible to a mobile robot because of the safety issues related to collisions with people. In this work we propose an approach to control a robot in a crowded environment; the method employs an Artificial Neural Network (ANN) that is trained with the NeuroEvolution of Augmented Topologies (NEAT) method. Models for the kinematics, perception, and cognition of the robot are presented. In particular, perception is based on a raycasting model which is tailored on the ANN. An in-depth analysis of a number of parameters of the environment and the robot is performed and a comparative analysis is presented; finally, results of the performance of the controller trained with NEAT are compared to those of a human driver who takes over the controller itself. Results show that the intelligent controller is able to perform on par with the human, within the simulated environment.

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“…The NEAT algorithm enables genetic evolution of a neural network to optimize the behavior for a given problem. The authors of [15] describe such an implementation for a nonholonomic wheeled robot.…”

Section: Introduction-neural Network In Engineering Just a Modern Buzzword?mentioning

confidence: 99%

A NEAT Based Two Stage Neural Network Approach to Generate a Control Algorithm for a Pultrusion System

Pommer

Sinapius

Brysch

et al. 2021

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Controlling complex systems by traditional control systems can sometimes lead to sub-optimal results since mathematical models do often not completely describe physical processes. An alternative approach is the use of a neural network based control algorithm. Neural Networks can approximate any function and as such are able to control even the most complex system. One challenge of this approach is the necessity of a high speed training loop to facilitate enough training rounds in a reasonable time frame to generate a viable control network. This paper overcomes this problem by employing a second neural network to approximate the output of a relatively slow 3D-FE-Pultrusion-Model. This approximation is by orders of magnitude faster than the original model with only minor deviations from the original models behaviour. This new model is then employed in a training loop to successfully train a NEAT based genetic control algorithm.

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Approach of Kinematic Control for a Nonholonomic Wheeled Robot using Artificial Neural Networks and Genetic Algorithms

Cited by 19 publications

References 18 publications

NAUTS: Negotiation for Adaptation to Unstructured Terrain Surfaces

NAUTS: Negotiation for Adaptation to Unstructured Terrain Surfaces

Crowded Environment Navigation with NEAT: Impact of Perception Resolution on Controller Optimization

A NEAT Based Two Stage Neural Network Approach to Generate a Control Algorithm for a Pultrusion System

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