Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

Abdulsaheb, Jaafar Ahmed; Kadhim, Dheyaa Jasim

doi:10.3390/robotics12040093

Cited by 33 publications

(7 citation statements)

References 175 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Centralized approaches include such methods as ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO). Ding and Abdulsaheb optimized multiple terrestrial robots using ACO to find a specific target location [20,21]. Majeed exploited the TSP using ACO to find the most efficient traversal of the tour [22].…”

Section: Algorithms Of Uav Cooperative Task Allocation In Disaster Re...mentioning

confidence: 99%

Collaborative Task Allocation and Optimization Solution for Unmanned Aerial Vehicles in Search and Rescue

Han,

Jiang,

Wang

et al. 2024

Drones

View full text Add to dashboard Cite

Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique requirements of post-earthquake rescue missions, the model aims to minimize the number of UAVs deployed, reduce rescue costs, and shorten the duration of rescue operations. We propose an innovative hybrid algorithm combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), called the PSOGWO algorithm, to achieve the objectives of the model. This algorithm is enhanced by various strategies, including interval transformation, nonlinear convergence factor, individual update strategy, and dynamic weighting rules. A practical case study illustrates the use of our model and algorithm in reality and validates its effectiveness by comparing it to PSO and GWO. Moreover, a sensitivity analysis on UAV capacity highlights its impact on the overall rescue time and cost. The research results contribute to the advancement of vehicle-routing problem (VRP) models and algorithms for post-earthquake relief in SAR. Furthermore, it provides optimized relief distribution strategies for rescue decision-makers, thereby improving the efficiency and effectiveness of SAR operations.

show abstract

Section: Algorithms Of Uav Cooperative Task Allocation In Disaster Re...mentioning

confidence: 99%

Collaborative Task Allocation and Optimization Solution for Unmanned Aerial Vehicles in Search and Rescue

Han,

Jiang,

Wang

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…So, it is suggested that using Genetic Algorithms (GA) [47] [48] will help overcome these limitations. Evolutionary algorithms [49], for instance, provide the advantage of covering a large search field while requiring little memory and CPU power. They have the capacity to adapt to their environment.…”

Section: Robots Routing and Path Planning Literature Reviewmentioning

confidence: 99%

Collision Avoidance and Routing based on Location Access (CARLA) of mobile robots

El-sayyad,

Saleh,

Ali

et al. 2023

Preprint

View full text Add to dashboard Cite

In this paper, a novel robotic system methodology known as Collision Avoidance and Routing based on Location Access (CARLA) is proposed. It is intended for use in environments like hospitals or crisis situations where quick action and the preservation of human life are crucial, and when the primary actuator is to protect the lives of healthcare workers and to prevent the high potential for viral transmission among patients, such as in COVID-19 isolation hospitals. Our framework is primarily concerned with accuracy and quick responses. E.g. delivering tools or items in a specific district while avoiding collisions with other robots or any surrounding obstacles. The suggested CARLA can also be used for a variety of related applications, including those found in factories, warehouses, and any other situation where mobile robots must be chosen and routed using a distant centralized control. The Robot Knowledge Module (RKM), Robot Selection Module (RSM), and Route Reservation Module (RRM) are the three core modules that make up the proposed framework, which will be explained in detail throughout the paper. In comparison to conventional techniques used in similar situations, the simulation results using the suggested framework demonstrate the flexible and effective mobility of the robots in terms of the robots' path computation time and the request's success rate without collision.

show abstract

“…Then, local optimization is performed between adjacent key nodes by improving and extending the evaluation function of the traditional dynamic window method (IDWA), which further improves path security and smoothness. An overview of navigation strategies for mobile robots that utilize three classical approaches, roadmap (RM), cell decomposition (CD), and artificial potential fields (APF), in addition to eleven heuristic approaches, including GA, ACO, artificial bee colony (ABC), gray wolf optimization (GWO), etc., which may be used in various environmental situations is presented in [27].…”

Section: Related Workmentioning

confidence: 99%

Path Optimization Using Metaheuristic Techniques for a Surveillance Robot

Peñacoba,

Sierra-García,

Santos

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

This paper presents an innovative approach to optimize the trajectories of a robotic surveillance system, employing three different optimization methods: genetic algorithm (GA), particle swarm optimization (PSO), and pattern search (PS). The research addresses the challenge of efficiently planning routes for a LiDAR-equipped mobile robot to effectively cover target areas taking into account the capabilities and limitations of sensors and robots. The findings demonstrate the effectiveness of these trajectory optimization approaches, significantly improving detection efficiency and coverage of critical areas. Furthermore, it is observed that, among the three techniques, pattern search quickly obtains feasible solutions in environments with good initial trajectories. On the contrary, in cases where the initial trajectory is suboptimal or the environment is complex, PSO works better. For example, in the high complexity map evaluated, PSO achieves 86.7% spatial coverage, compared to 85% and 84% for PS and GA, respectively. On low- and medium-complexity maps, PS is 15.7 and 18 s faster in trajectory optimization than the second fastest algorithm, which is PSO in both cases. Furthermore, the fitness function of this proposal has been compared with that of previous works, obtaining better results.

show abstract

Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

Cited by 33 publications

References 175 publications

Collaborative Task Allocation and Optimization Solution for Unmanned Aerial Vehicles in Search and Rescue

Collaborative Task Allocation and Optimization Solution for Unmanned Aerial Vehicles in Search and Rescue

Collision Avoidance and Routing based on Location Access (CARLA) of mobile robots

Path Optimization Using Metaheuristic Techniques for a Surveillance Robot

Contact Info

Product

Resources

About