Path Planning for Wheeled Mobile Robot in Partially Known Uneven Terrain

Zhang, Bo; Li, Guobin; Zheng, Qiang; Bai, Xiaoshan; Ding, Yu; Khan, Awais Gul

doi:10.3390/s22145217

Cited by 34 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We want to show the advantages of our system by comparing it with the algorithm presented in [ 40 ]. The paper proposes a hierarchical path-planning method for a wheeled robot.…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Imaging for Mobile Robot Navigation

Jakubczyk

Siemiątkowska

Więckowski

et al. 2022

Sensors

View full text Add to dashboard Cite

The article presents the application of a hyperspectral camera in mobile robot navigation. Hyperspectral cameras are imaging systems that can capture a wide range of electromagnetic spectra. This feature allows them to detect a broader range of colors and features than traditional cameras and to perceive the environment more accurately. Several surface types, such as mud, can be challenging to detect using an RGB camera. In our system, the hyperspectral camera is used for ground recognition (e.g., grass, bumpy road, asphalt). Traditional global path planning methods take the shortest path length as the optimization objective. We propose an improved A* algorithm to generate the collision-free path. Semantic information makes it possible to plan a feasible and safe path in a complex off-road environment, taking traveling time as the optimization objective. We presented the results of the experiments for data collected in a natural environment. An important novelty of this paper is using a modified nearest neighbor method for hyperspectral data analysis and then using the data for path planning tasks in the same work. Using the nearest neighbor method allows us to adjust the robotic system much faster than using neural networks. As our system is continuously evolving, we intend to examine the performance of the vehicle on various road surfaces, which is why we sought to create a classification system that does not require a prolonged learning process. In our paper, we aimed to demonstrate that the incorporation of a hyperspectral camera can not only enhance route planning but also aid in the determination of parameters such as speed and acceleration.

show abstract

“…We want to show the advantages of our system by comparing it with the algorithm presented in [ 40 ]. The paper proposes a hierarchical path-planning method for a wheeled robot.…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Imaging for Mobile Robot Navigation

Jakubczyk

Siemiątkowska

Więckowski

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…(1) Global path planning To ensure that the patrol robot can effectively avoid obstacles globally and locally, and considering that the grid map of the actual road scene is relatively simple, the Astar algorithm is used as the global path planning method to provide accurate obstacle avoidance directions for the robot through real-time planning [22,23]. A-star combines heuristic search with breadth-first algorithm to select the search direction through the cost function f (n), and expands around the starting point.…”

Section: Path Planningmentioning

confidence: 99%

Research on SLAM and Path Planning Method of Inspection Robot in Complex Scenarios

Wang¹,

Ma²,

Li³

2023

Preprint

View full text Add to dashboard Cite

Factory safety inspections are crucial for ensuring safe production. However, manual inspections present issues such as low efficiency and high workload. Inspection robots provide an efficient and reliable solution for completing patrol tasks. The development of robot localization and path planning technologies provides guarantees for factory inspection robots to autonomously complete inspection tasks in complex environments. This paper studies mapping and localization, as well as path planning methods for robots in order to meet the application requirements of factory inspections. Two SLAM application systems based on multiple-line laser radar and vision are designed for different scenarios in consideration of the limitations of cameras and laser sensors in terms of their own characteristics and applicability in different environments. To address the issue of low efficiency in inspection tasks, a hybrid path planning algorithm that integrates the A-star algorithm and time elasticity band algorithm is proposed. This algorithm effectively solves the problem of path planning in complex environments that is prone to falling into local optimal solutions, thereby improving the inspection efficiency of robots. Experimental tests show that the designed SLAM and path planning methods can meet the needs of robot inspection in complex scenes and have good reliability and stability. The code used in this article is open source and can be accessed at https://github.com/Mxiii99/RSPP_CS.git.

show abstract

“…Path planning methods that consider slope (on 2.5D map) have been studied for years, and researchers are concentrating on finding suitable paths for GVs in complex terrain environments [9][10][11]. In early research, most studies focused on single-object optimization problems, such as traversability or avoiding obstacles.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based 2.5D Multi-Objective Path Planning for Ground Vehicles: Considering Distance and Energy Consumption

Wu,

Huang,

Huang

2023

Electronics

View full text Add to dashboard Cite

Due to the vastly different energy consumption between up-slope and down-slope, a path with the shortest length in a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicle, realizing a good trade-off between distance and energy consumption in 2.5D path planning is significantly meaningful. In this paper, we propose a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP). The DMOP can efficiently find the desired path in three steps: (1) transform the high-resolution 2.5D map into a small-size map, (2) use a trained deep Q network (DQN) to find the desired path on the small-size map, and (3) build the planned path to the original high-resolution map using a path-enhanced method. In addition, the hybrid exploration strategy and reward-shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, and border. The simulation results show that the proposed method can finish the multi-objective 2.5D path planning task with significantly high efficiency and quality. Also, simulations prove that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks.

show abstract

Path Planning for Wheeled Mobile Robot in Partially Known Uneven Terrain

Cited by 34 publications

References 28 publications

Hyperspectral Imaging for Mobile Robot Navigation

Hyperspectral Imaging for Mobile Robot Navigation

Research on SLAM and Path Planning Method of Inspection Robot in Complex Scenarios

Deep Reinforcement Learning-Based 2.5D Multi-Objective Path Planning for Ground Vehicles: Considering Distance and Energy Consumption

Contact Info

Product

Resources

About