Neural network-based approaches for mobile robot navigation in static and moving obstacles environments

Singh, Ngangbam Herojit; Thongam, Khelchandra

doi:10.1007/s11370-018-0260-2

Cited by 52 publications

(24 citation statements)

References 27 publications

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“…In [19], the Ant Colony Optimization (ACO) algorithm improved with FL is used to obtain cost-effective paths with an unmanned aerial vehicle in dynamic environments. Another example is the work in [20], where a NN is used to dynamically predict and avoid collisions in the path-planning for mobile robots. On the other side, the meta-heuristic approaches are referred to as algorithms from evolutionary computation and swarm intelligence that solve a formal optimization problem to find the path elements that minimize its overall cost.…”

Section: Introduction 1a Review Of Path-planning Methodsmentioning

confidence: 99%

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

et al. 2021

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Mobile robots are currently exploited in various applications to enhance efficiency and reduce risks in hard activities for humans. The high autonomy in those systems is strongly related to the path-planning task. The path-planning problem is complex and requires in its formulation the adjustment of path elements that take the mobile robot from a start point to a target one at the lowest cost. Nevertheless, the identity or the number of the path elements to be adjusted is unknown; therefore, the human decision is necessary to determine this information reducing autonomy. Due to the above, this work conceives the path-planning as a Variable-Length-Vector optimization problem (VLV-OP) where both the number of variables (path elements) and their values must be determined. For this, a novel variant of Differential Evolution for Variable-Length-Vector optimization named VLV-DE is proposed to handle the path-planning VLV-OP for mobile robots. VLV-DE uses a population with solution vectors of different sizes adapted through a normalization procedure to allow interactions and determine the alternatives that better fit the problem. The effectiveness of this proposal is shown through the solution of the path-planning problem in complex scenarios. The results are contrasted with the well-known A* and the RRT*-Smart path-planning methods.

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Section: Introduction 1a Review Of Path-planning Methodsmentioning

confidence: 99%

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

et al. 2021

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“…Fuzzy logic algorithms [55] have been used to learn to navigate, and Aouf et al [4] demonstrated that their fuzzy logic approach outperformed three metaheuristic (swarm intelligence) algorithms: particle swarm optimisation, artificial bee colony and a meta-heuristic Firefly algorithm, for navigation time and path length. However, fuzzy logic algorithms struggle in dynamic environments as they are too slow to recompute the path on the fly when the environment changes [46]. Patle et al [38] review a number of techniques including these metaheuristic algorithms such as GAs and swarm intelligence (including particle swarm optimisation, artificial bee colony, firefly algorithm and ant colony optimisation) for robot navigation.…”

Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

“…Patle et al [38] conclude that GAs and swarm intelligence can navigate in uncertain environments, but they are complex and not suitable for low-cost robots. Regular neural networks such as multilayer perceptrons can be used to train a navigation model [38,46], but they do not have the computational power of deep learning algorithms and would be restricted to simpler environments. In contrast, deep reinforcement learning (deep RL) uses a trial and error approach which generates rewards and penalties as the drone navigates.…”

Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

Deep reinforcement learning for drone navigation using sensor data

Hodge

Hawkins

Alexander

2020

Neural Comput & Applic

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Mobile robots such as unmanned aerial vehicles (drones) can be used for surveillance, monitoring and data collection in buildings, infrastructure and environments. The importance of accurate and multifaceted monitoring is well known to identify problems early and prevent them escalating. This motivates the need for flexible, autonomous and powerful decision-making mobile robots. These systems need to be able to learn through fusing data from multiple sources. Until very recently, they have been task specific. In this paper, we describe a generic navigation algorithm that uses data from sensors on-board the drone to guide the drone to the site of the problem. In hazardous and safety-critical situations, locating problems accurately and rapidly is vital. We use the proximal policy optimisation deep reinforcement learning algorithm coupled with incremental curriculum learning and long short-term memory neural networks to implement our generic and adaptable navigation algorithm. We evaluate different configurations against a heuristic technique to demonstrate its accuracy and efficiency. Finally, we consider how safety of the drone could be assured by assessing how safely the drone would perform using our navigation algorithm in real-world scenarios.

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“…The model of backpropagation neuron and the two-layer structure diagram are shown in Figure 3. BPNN needs to adjust the weights according to each training sample, which requires a massive amount of training data in practical applications, resulting in reduced efficiency of weight adjustment and failure to meet the real-time requirements (Singh and Thongam, 2019). Therefore, the traditional BPNN is divided into several smaller sub-networks, which are trained separately to improve computational efficiency.…”

Section: Mathematical Kinematics Models Of Non-holonomic Mobile Robotsmentioning

confidence: 99%

The Analysis of Trajectory Control of Non-holonomic Mobile Robots Based on Internet of Things Target Image Enhancement Technology and Backpropagation Neural Network

Zhao

Wang²,

Fan³

et al. 2021

Front. Neurorobot.

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The trajectory tracking and control of incomplete mobile robots are explored to improve the accuracy of the trajectory tracking of the robot controller. First, the mathematical kinematics model of the non-holonomic mobile robot is studied. Then, the improved Backpropagation Neural Network (BPNN) is applied to the robot controller. On this basis, a mobile robot trajectory tracking controller combining the fuzzy algorithm and the neural network is designed to control the linear velocity and angular velocity of the mobile robot. Finally, the robot target image can be analyzed effectively based on the Internet of Things (IoT) image enhancement technology. In the MATLAB environment, the performances of traditional BPNN and improved BPNN in mobile robots' trajectory tracking are compared. The tracking accuracy before and after the improvement shows no apparent differences; however, the training speed of improved BPNN is significantly accelerated. The fuzzy-BPNN controller presents significant improvements in tracking speed and tracking accuracy compared with the improved BPNN. The trajectory tracking controller of the mobile robot is designed and improved based on the fuzzy BPNN. The designed controller combining the fuzzy algorithm and the improved BPNN can provide higher accuracy and tracking efficiency for the trajectory tracking and control of the non-holonomic mobile robots.

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Neural network-based approaches for mobile robot navigation in static and moving obstacles environments

Cited by 52 publications

References 27 publications

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

Deep reinforcement learning for drone navigation using sensor data

The Analysis of Trajectory Control of Non-holonomic Mobile Robots Based on Internet of Things Target Image Enhancement Technology and Backpropagation Neural Network

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