Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning

Almazrouei, Khawla; Kamel, Ibrahim; Rabie, Tamer

doi:10.3390/app13148174

Cited by 20 publications

(4 citation statements)

References 61 publications

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“…The dynamic and unforeseeable state of edge cases in the real world makes the application of the navigation task challenging. Ensuring that the systems can detect and respond effectively to changing and unstructured scenarios is essential for safe and reliable navigation [ 32 ]. The conventional approach, which works best in a static environment, is known to be computationally intensive and must be adjusted to varying environment states and motion dynamics.…”

Section: Learning-based Navigation Techniques (Methods)mentioning

confidence: 99%

“…Shabbir et al [ 31 ] reviewed the capabilities of deep learning through environmental perception and modelling for an efficient navigation experience. Most models integrate Q-learning techniques to solve navigation task challenges through path planning and obstacle avoidance using discreet actions [ 32 , 33 ]. The authors in [ 34 ] used a reward function and continuous action space to achieve safe navigation tasks.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

Adiuku,

Avdelidis,

Tang

et al. 2024

Sensors

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The field of learning-based navigation for mobile robots is experiencing a surge of interest from research and industry sectors. The application of this technology for visual aircraft inspection tasks within a maintenance, repair, and overhaul (MRO) hangar necessitates efficient perception and obstacle avoidance capabilities to ensure a reliable navigation experience. The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment. The challenging environment limits the practical application of conventional methods and real-time adaptability to changes. In response to these challenges, recent years research efforts have witnessed advancement with machine learning integration aimed at enhancing navigational capability in both static and dynamic scenarios. However, most of these studies have not been specific to the MRO hangar environment, but related challenges have been addressed, and applicable solutions have been developed. This paper provides a comprehensive review of learning-based strategies with an emphasis on advancements in deep learning, object detection, and the integration of multiple approaches to create hybrid systems. The review delineates the application of learning-based methodologies to real-time navigational tasks, encompassing environment perception, obstacle detection, avoidance, and path planning through the use of vision-based sensors. The concluding section addresses the prevailing challenges and prospective development directions in this domain.

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Section: Learning-based Navigation Techniques (Methods)mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

Adiuku,

Avdelidis,

Tang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Significant advances have been made in LiDAR SLAM, which can often provide more robust and simultaneous localization and mapping in indoor navigation systems using 3D spatial information directly captured by LiDAR point clouds, and have been employed in robots and automated guided vehicles for industrial applications [15]. The autonomous obstacle avoidance and trajectory planning control strategy with low computational complexity, high cost-effectiveness, closed-loop stability verification, and the ability to quickly plan a collision-free smooth trajectory curve has been used in the overall control system of autonomous mobile robots [16][17][18][19][20].…”

Section: Literature Backgroundmentioning

confidence: 99%

Linear Actuators in a Haptic Feedback Joystick System for Electric Vehicles

Daniel,

Kowol,

Lo Sciuto

2024

Computers

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Several strategies for navigation in unfamiliar environments have been explored, notably leveraging advanced sensors and control algorithms for obstacle recognition in autonomous vehicles. This study introduces a novel approach featuring a redesigned joystick equipped with stepper motors and linear drives, facilitating WiFi communication with a four-wheel omnidirectional electric vehicle. The system’s drive units integrated into the joystick and the encompassing control algorithms are thoroughly examined, including analysis of stick deflection measurement and inter-component communication within the joystick assembly. Unlike conventional setups in which the joystick is tilted by the operator, two independent linear drives are employed to generate ample tensile force, effectively “overpowering” the operator’s input. Running on a Raspberry Pi, the software utilizes Python programming to enable joystick tilt control and to transmit orientation and axis deflection data to an Arduino unit. A fundamental haptic effect is achieved by elevating the minimum pressure required to deflect the joystick rod. Test measurements encompass detection of obstacles along the primary directions perpendicular to the electric vehicle’s trajectory, determination of the maximum achievable speed, and evaluation of the joystick’s maximum operational range within an illuminated environment.

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“…Nevertheless, the research acknowledges the need for additional validation in different realistic scenarios and highlights potential challenges associated with the integration of neural network models into robotic systems. Reinforcement learning (RL)-based techniques for dynamic obstacle avoidance and path planning are widely used [17]. Nonetheless, limited validation under diverse scenarios may limit the generality of these findings.…”

Section: Introductionmentioning

confidence: 99%

Bi-directional adaptive enhanced A* algorithm for mobile robot navigation

Gharbi

2024

ACI

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PurposeThe present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.Design/methodology/approachThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.FindingsThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.Research limitations/implicationsThe rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.Originality/valueThe originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

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Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning

Cited by 20 publications

References 61 publications

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

Linear Actuators in a Haptic Feedback Joystick System for Electric Vehicles

Bi-directional adaptive enhanced A* algorithm for mobile robot navigation

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