Reinforcement based mobile robot path planning with improved dynamic window approach in unknown environment

Chang, Long-Wen; Shan, Liang; Jiang, Chao; Dai, Yuewei

doi:10.1007/s10514-020-09947-4

Cited by 165 publications

(77 citation statements)

References 50 publications

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“…The robot path can be the same as the simulations run until 100 times with the best individual genes and better fitness function production for each population. In line with [19]- [20], the average speed for manufacturing a mobile robot is 0.5 with a maximum load of 1 kilogram. This setting is due to some limitations of the robot speed due to the safety in the industrial environment 2 shows the time average of the mobile robot with constant speed from start to end position based on the percentage of obstacles.…”

Section: Resultsmentioning

confidence: 96%

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Industry 4.0 with intelligent manufacturing 5G mobile robot based on genetic algorithm

Hawari

Apandi²

2021

IJEECS

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A manufacturing fifth-generation (5G) mobile robot is a new development of industry 4.0 application, deploying an unmanned system. This study aims to implement a robot system for industrial applications in real-time with remote sensors to enable humans. Moreover, there is still some obstacle to cope with the better optimization solution for manufacturing 5G robot. This paper proposed a latency network algorithm for the manufacturing 5G mobile robot. An improved genetic algorithm (GA) by restructuring the genes is applied to plan a mobile robot path. The process of the robot path in a complex workspace is proposed, considering the node's collision-free constraint in the moving phase of a robot. The proposed scheme improves the robot path and delivery efficiency of the robot on average at 68% by moving on the industrial environment's shortest path and time average of the mobile robot reach its destination.

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Section: Resultsmentioning

confidence: 96%

“…The proposed genetic algorithm (GA) for path planning effectively optimizes a robot navigation system's local and global planners, as demonstrated in [19]. The GA is a method of the quest for global optimization, and it can model the evolution cycle and its behaviour in nature.…”

Section: Robot Path Planning Based On Genetic Algorithmmentioning

confidence: 99%

Industry 4.0 with intelligent manufacturing 5G mobile robot based on genetic algorithm

Hawari

Apandi²

2021

IJEECS

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“…The performance of LVD-NMPC was benchmarked against a baseline nonlearning approach, coined DWA-NMPC, as well as against PilotNet of Bojarski et al 2 DWA-NMPC uses the DWA proposed by Fox et al 25 and Chang et al 26 for path planning and a constraint NMPC for motion control, relying for perception on the YoloV3 algorithm of Redmon and Farhadi. 27 LVD-NMPC has been tested on three different environments: (I) in the GridSim simulator, (II) for indoor navigation using the 1:8 scaled model car from Figure 4(a) and (III) on real-world driving with the full scale autonomous driving test vehicle from Figure 4(b), as well as on the nuScenes computer vision dataset.…”

Section: Methodsmentioning

confidence: 99%

LVD-NMPC: A learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles

Grigorescu

Ginerica²,

Zaha³

et al. 2021

International Journal of Advanced Robotic Systems

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In this article, we introduce a learning-based vision dynamics approach to nonlinear model predictive control (NMPC) for autonomous vehicles, coined learning-based vision dynamics (LVD) NMPC. LVD-NMPC uses an a-priori process model and a learned vision dynamics model used to calculate the dynamics of the driving scene, the controlled system’s desired state trajectory, and the weighting gains of the quadratic cost function optimized by a constrained predictive controller. The vision system is defined as a deep neural network designed to estimate the dynamics of the image scene. The input is based on historic sequences of sensory observations and vehicle states, integrated by an augmented memory component. Deep Q-learning is used to train the deep network, which once trained can also be used to calculate the desired trajectory of the vehicle. We evaluate LVD-NMPC against a baseline dynamic window approach (DWA) path planning executed using standard NMPC and against the PilotNet neural network. Performance is measured in our simulation environment GridSim, on a real-world 1:8 scaled model car as well as on a real size autonomous test vehicle and the nuScenes computer vision dataset.

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“…However, it was difficult to ensure that the planned path was globally optimal and time-consuming, and it was not suitable for UAV to make long-distance path planning in mountainous environment. Chang introduced Qlearning to improve the dynamic window algorithm and increased the success rate of the dynamic window algorithm for path planning in the unknown mountainous environment [32]. However, the calculation of the algorithm is more complex, and it is not applicable to low computational power of the UAV, and the path of local path planning does not have the global optimal.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Path Planning Based on Fusion of Sparrow Search Algorithm and Improved Bioinspired Neural Network

Liu¹,

Zhang

et al. 2021

IEEE Access

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Aiming at the problems of low stability of path planning, inability to avoid dynamic obstacles and long path planning for multi unmanned aerial vehicles (UAV) in mountainous environment, a path planning method for UAV was proposed based on the fusion of Sparrow Search Algorithm (SSA) and Bioinspired Neural Network (BINN). The method first scans the flight environment and smoothes the surface, then raises it to obtain the safe surface, and uses SSA to find a series of nodes with the lowest comprehensive cost on the safe surface. Then, B-spline curves are used to fit these nodes, so that the planned path is smooth to meet the flight requirements of the UAV. When the dynamic obstacle is detected in the predetermined trajectory, the improved BINN method is used to carry out local path replanning to achieve the purpose of dynamic obstacle avoidance. Computer simulation results demonstrate that the fusion algorithm can plan a collision free path in mountainous environment, and the planned path is smooth and short. Compared with Artificial Bee Colony Algorithm (ABC) and Dragonfly Algorithm (DA), the fusion algorithm has obvious advantages in stability of path planning and planned path length, and has the ability of dynamic obstacle avoidance.INDEX TERMS Path planning,multi-UAV,sparrow search algorithm,bioinspired neural network,safe surface.

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Reinforcement based mobile robot path planning with improved dynamic window approach in unknown environment

Cited by 165 publications

References 50 publications

Industry 4.0 with intelligent manufacturing 5G mobile robot based on genetic algorithm

Industry 4.0 with intelligent manufacturing 5G mobile robot based on genetic algorithm

LVD-NMPC: A learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles

Multi-UAV Path Planning Based on Fusion of Sparrow Search Algorithm and Improved Bioinspired Neural Network

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