Path planning techniques for mobile robots: Review and prospect

Liu, Lixing; Wang, Xu; Yang, Xin; Liu, Hongjie; Li, Jianping; Wang, Pengfei

doi:10.1016/j.eswa.2023.120254

Cited by 143 publications

(57 citation statements)

References 154 publications

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“…To begin, 2D laser radar is employed for the initial detection of human legs. This is achieved by defining specific features of human legs, as outlined in [18], including attributes such as data dispersion, geometry, and movement characteristics. The segmentation domains, derived from the point cloud data generated by the laser radar, are subjected to classification.…”

Section: Human Detectionmentioning

confidence: 99%

“…Another crucial aspect of HAN lies in the domain of navigation algorithms, which can be broadly categorized into global path planning [18][19][20] and local path planning [21][22][23] within the field of mobile robot navigation. Global path planning primarily deals with devising an overarching path strategy from the robot's current location to its target destination, making the inclusion of social factors less impactful.…”

Section: Introductionmentioning

confidence: 99%

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A novel Human-Aware Navigation algorithm based on behavioral intention cognition

Li,

Zhang,

2024

Robotica

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In order to ensure safe and comfortable human–robot navigation in close proximity, it is imperative for robots to possess the capability to understand human behavioral intention. With this objective in mind, this paper introduces a Human-Aware Navigation (HAN) algorithm. The HAN system combines insights from studies on human detection, social behavioral model, and behavior prediction, all while incorporating social distance considerations. This information is integrated into a layer dedicated to human behavior intention cognition, achieved through the fusion of data from laser radar and Kinect sensors, employing Gaussian functions to account for individual private space and movement trend. To cater to the mapping requirements of the HAN system, we have reduced the computational complexity associated with traditional multilayer cost map by implementing a “first-come, first-served” expansion method. Subsequently, we have enhanced the trajectory optimization equation by incorporating an improved dynamic triangle window method that integrates human behavior intention cognition, leading to the determination of an appropriate trajectory for the robot. Finally, experimental evaluations have been conducted to assess and validate the efficacy of the human behavior intention cognition and the HAN system. The results clearly demonstrate that the HAN system outperforms the traditional Dynamic Window Approach algorithm in ensuring the safety and comfort of humans in human–robot coexistence environments.

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Section: Human Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel Human-Aware Navigation algorithm based on behavioral intention cognition

Li,

Zhang,

2024

Robotica

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“…Unmanned vehicular technologies turn out to be a key solution to the various robotics applications such as transportation, search and rescue operations, military applications, aviation and warehouse (Azzabi and Nouri, 2019; Dewang et al , 2018; Li et al , 2020; Yaonan et al , 2011). Optimality in path planning mainly focuses on various parameters such as path length minimization, low computational load, smoother trajectories, time lag and the least number of sharp turns (Azzabi and Nouri, 2019; Dewang et al , 2018; Liu et al , 2023; Pattnaik et al , 2022; Raheem and Abdulkareem, 2019). Time-efficient navigation is an essential task of unmanned vehicles, and these stated problems cause a time lag in reaching the destination, which degrades the reliability of the path planning techniques.…”

Section: Introductionmentioning

confidence: 99%

“…The traditional techniques consist of A*, D*, RRT, RRT*, Probabilistic Roadmap (PRM), Smart RRT*, D* lite, etc., and optimization-based techniques consist of ant colony optimization (ACO), particle swarm optimization (PSO), bacterial forging technique, genetic algorithms, etc. (Berglund et al , 2009; Debnath et al , 2019; Ding et al , 2022; Liu et al , 2023; Pattnaik et al , 2022; Raheem and Abdulkareem, 2019; Ravankar et al , 2018; Singh and Nagla, 2019a, 2019b; Singh, 2023; Song et al , 2016; Tang et al , 2017; Wang et al , 2021; Zeng et al , 2016; Zhang et al , 2021). These stated techniques provide efficient results if the complexity of the environment is low because there is sufficient free space for collision-free navigation.…”

Section: Introductionmentioning

confidence: 99%

“…However, if the environmental characteristics become unstructured and complex, the reliability of these stated path planning techniques becomes degraded because the unstructured occupied segment area becomes high that resulted in collisions or task failure (Liu et al , 2023; Pattnaik et al , 2022; Raheem and Abdulkareem, 2019; Ravankar et al , 2018; Tang et al , 2017). To clarify this statement, consider an example, the conventional A* always generates a path to reach the destination (if the path is available) but the generated path consists of an immense number of sharp turns with high time/space complexity; the path selected by RRT* is zig-zag in nature because of random deploying of nodes, the reliability of the PRM depends upon number of nodes and the optimization-based techniques suffer from a problem of high computational load that resulted in time lag (Azzabi and Nouri, 2019; Dewang et al , 2018; Li et al , 2020; Liu et al , 2023; Pattnaik et al , 2022; Raheem and Abdulkareem, 2019; Yaonan et al , 2011). The conventional PRM greatly relies on the selection of number of nodes as the generation of path w.r.t PRM required sufficient nodes to find a suitable path by interlinking the various nodes.…”

Section: Introductionmentioning

confidence: 99%

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Trajectory optimization with hybrid probabilistic roadmap approach to achieve time efficient navigation of unmanned vehicles in unstructured environment

Singh

2024

RIA

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Purpose This paper aims to focus on solving the path optimization problem by modifying the probabilistic roadmap (PRM) technique as it suffers from the selection of the optimal number of nodes and deploy in free space for reliable trajectory planning. Design/methodology/approach Traditional PRM is modified by developing a decision-making strategy for the selection of optimal nodes w.r.t. the complexity of the environment and deploying the optimal number of nodes outside the closed segment. Subsequently, the generated trajectory is made smoother by implementing the modified Bezier curve technique, which selects an optimal number of control points near the sharp turns for the reliable convergence of the trajectory that reduces the sum of the robot’s turning angles. Findings The proposed technique is compared with state-of-the-art techniques that show the reduction of computational load by 12.46%, the number of sharp turns by 100%, the number of collisions by 100% and increase the velocity parameter by 19.91%. Originality/value The proposed adaptive technique provides a better solution for autonomous navigation of unmanned ground vehicles, transportation, warehouse applications, etc.

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Artificial Intelligence Technology for Path Planning of Automated Earthwork Machinery

Zhou,

Wang,

et al. 2024

Journal of Field Robotics

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The challenging characteristics of earthwork environments—complex, unstructured, and constantly evolving—pose significant challenges for the path planning of automated earthwork machinery. Recent advancements in artificial intelligence (AI) technology have opened new avenues to address these challenges, which are crucial for improving the intelligence level of automated earthwork machinery. However, there is a notable lack of comprehensive analyses on AI‐based path planning in earthwork operations. Consequently, we provide a systematic review of four AI technologies currently employed in path planning for earthwork machinery, including (1) evolutionary computation, (2) swarm intelligence, (3) machine learning, and (4) other AI‐based technologies. We analyzed the application and performance evaluation results of these technologies across various construction machinery. Through this systematic analysis, we identified several key challenges: (1) multiconstraint earthwork environments, (2) generalization across 3D unstructured sites, (3) adaptability to dynamically uncertain environments, and (4) shortage of on‐site validation. We then outline potential future directions: (1) integration of generative AI with reinforcement learning, (2) use of large model technology, (3) adoption of embodied intelligence technology, and (4) conduction of more on‐site experiments.

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Path planning techniques for mobile robots: Review and prospect

Cited by 143 publications

References 154 publications

A novel Human-Aware Navigation algorithm based on behavioral intention cognition

A novel Human-Aware Navigation algorithm based on behavioral intention cognition

Trajectory optimization with hybrid probabilistic roadmap approach to achieve time efficient navigation of unmanned vehicles in unstructured environment

Artificial Intelligence Technology for Path Planning of Automated Earthwork Machinery

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