Past, Present and Future of Path-Planning Algorithms for Mobile Robot Navigation in Dynamic Environments

Hewawasam, H.S.; Ibrahim, M. Yousef; Kahandawa, Gayan

doi:10.1109/ojies.2022.3179617

Cited by 37 publications

(25 citation statements)

References 54 publications

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“…M OVING a robot in the real world presents several challenges, including adapting the robot's motion to the dynamic nature of the environment. This problem has become very important in recent years with the spread of mobile robotics [1], social robotics [2], and human-robot collaboration (HRC) in industrial settings [3]. Path planners usually plan a path from a start point to a goal point, considering only static obstacles.…”

Section: Introductionmentioning

confidence: 99%

Anytime Informed Multi-Path Replanning Strategy for Complex Environments

et al. 2023

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In many real-world applications (e.g., human-robot collaboration), the environment changes rapidly, and the intended path may become invalid due to moving obstacles. In these situations, the robot should quickly find a new path to reach the goal, possibly without stopping. Planning from scratch or repairing the current graph can be too expensive and time-consuming. This paper proposes a path replanner, MARS, that enables a robot to move in dynamic environments with unpredictable obstacles. The method exploits a set of pre-computed paths to compute a new solution in a few hundred milliseconds when an obstacle obstructs the robot's way. It enhances the search speed using informed sampling and improves the current solution in an anytime fashion to make the robot reactive to environmental changes. In addition, the paper presents a multithread architecture, applicable to several replanning algorithms, to handle the execution of the robot's trajectory with continuous replanning and the collision checking of the traversed path. The paper compares state-of-the-art sampling-based path-replanning algorithms in complex and high-dimensional scenarios, showing that MARS is superior in terms of success rate and quality of solutions found. An open-source ROS-compatible implementation of the algorithm is also provided.

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Section: Introductionmentioning

confidence: 99%

Anytime Informed Multi-Path Replanning Strategy for Complex Environments

et al. 2023

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“…The area of computer science was where the majority of GA’s potential applications were concentrated. On the other hand, strategies based on GA are also utilized within the subject of mobile robot navigation ( Hewawasam et al, 2022 ). The GA begins without any prior knowledge of the optimal solution and is totally dependent on the reactions of both the environment and the evolutionary operators in order to find the optimal solution ( Leena and Saju 2014 ).…”

Section: Introductionmentioning

confidence: 99%

A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

et al. 2023

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Due to the complexity of autonomous mobile robot’s requirement and drastic technological changes, the safe and efficient path tracking development is becoming complex and requires intensive knowledge and information, thus the demand for advanced algorithm has rapidly increased. Analyzing unstructured gain data has been a growing interest among researchers, resulting in valuable information in many fields such as path planning and motion control. Among those, motion control is a vital part of a fast, secure operation. Yet, current approaches face problems in managing unstructured gain data and producing accurate local planning due to the lack of formulation in the knowledge on the gain optimization. Therefore, this research aims to design a new gain optimization approach to assist researcher in identifying the value of the gain’s product with a qualitative comparative study of the up-to-date controllers. Gains optimization in this context is to classify the near perfect value of the gain’s product and processes. For this, a domain controller will be developed based on the attributes of the Fuzzy-PID parameters. The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). It is expected that the domain controller will give a positive impact to the path planning position and angular PID controller algorithm that meet the autonomous demand.

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“…Furthermore, the system development can be parallelised among several expert teams because of its modular nature. Perception, localisation and mapping, path planning and robot control modules may consist of classical, learning-based or hybrid methods [8,28]. Due to algorithmic and modelling limitations, however, the modular approach may not be optimal for general autonomous navigation applications.…”

Section: Introductionmentioning

confidence: 99%

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

Wijayathunga¹,

Rassau²,

Chai³

2023

Preprint

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The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, autonomous robots have been able to achieve relatively high levels of autonomy. In more unstructured environments, however, the realisation of autonomous mobile robots remains challenging due to limitations in the robots’ external environment understanding. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. The classical navigation approach typically includes robot perception, localisation, environmental mapping, path planning and motion control stages. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots need to be able to understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, and rough or smooth terrain) are required for autonomous robot navigation in unstructured outdoor environments. A wide number of alternative approaches have been proposed in recent years to attempt to address these scene understanding requirements. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments, and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges.

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Past, Present and Future of Path-Planning Algorithms for Mobile Robot Navigation in Dynamic Environments

Cited by 37 publications

References 54 publications

Anytime Informed Multi-Path Replanning Strategy for Complex Environments

Anytime Informed Multi-Path Replanning Strategy for Complex Environments

A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

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