Metaheuristic Algorithm for Robotic Path Planning

Gigras, Yogita; Gupta, Kusum

doi:10.5120/14822-3056

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…In Ref. [57], a metaheuristic optimization-based algorithm to solve path planning problem of mobile in an unknown environment consisting of 20 randomly generated obstacles. The path-planning algorithm was based on ant colony optimization, and its performance was compared with that of a particle swarm optimization algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Path Planning Algorithms for Mobile Robots: A Survey

Haruna,

Bashir Mu’azu,

Umar

et al. 2023

Motion Planning for Dynamic Agents

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Mobile robots have applications in military (for reconnaissance, search and rescue operations, bomb detection, surveillance), transportation (for cargo and packet delivery), data acquisition, etc. For the mobile robots to be able to execute these tasks with minimum or no human intervention, they need to be autonomous and intelligent. Path planning (PP) is one of the most critical areas of concern in the field of autonomous mobile robots. It is about obtaining a collision-free motion optimal path based on either time, distance, energy or cost in a static or dynamic environment containing obstacles. However, power limitation hinders the mobile robots to accomplish their task of reaching the target location as there are several paths they can follow. Each of these paths has its own path length, cost (i.e., time to reach destination), and energy constraint, thus, the need to plan for an optimal path according to a certain performance criterion. Significant research has been conducted in recent years to address the PP problem. Hence, this chapter is aimed at presenting the different approaches for PP of mobile robots with respect to different optimality criteria (time, distance, energy and cost), challenges and making recommendations on possible areas of future research.

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Section: Literature Reviewmentioning

confidence: 99%

Path Planning Algorithms for Mobile Robots: A Survey

Haruna,

Bashir Mu’azu,

Umar

et al. 2023

Motion Planning for Dynamic Agents

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“…According to (1), ( 5), and (6), the derivative of vehicle heading angle can be deduced, as shown in equation (7):…”

Section: Establish the Kinematic Modelmentioning

confidence: 99%

“…In order to reduce the complexity, many researchers use different algorithms to solve path planning problems, including genetic algorithm (GA), 1,2 particle swarm optimization (PSO), 3,4 ant colony optimization (ACO), 5,6 and other artificial intelligence planning algorithm. 7 The most typical search algorithms are artificial potential field method, 8 optimal path-planning based on machine learning, 9 A* algorithm, 10 and D* algorithm. 11 In autonomous driving, the algorithm of map grid search is gradually applied to trajectory planning.…”

Section: Introductionmentioning

confidence: 99%

Improved A-star algorithm based on multivariate fusion heuristic function for autonomous driving path planning

Wang

Liu

Yao

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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Path planning is a fundamental problem in the aspect of autonomous driving. A-star (A*) algorithm is a heuristic algorithm for path planning. However, there are two problems need to be solved in the traditional A-star algorithm: firstly, the tracking error caused by vehicle speed and vehicle size are not considered in path planning; secondly, the kinematics constraints of the vehicle itself and the smoothness of the path in the actual driving process are not considered. Therefore, this paper proposes an improved A-star algorithm to address the above deficiencies. By designing the collision cost heuristic function based on the position of obstacles, the vehicle contour and speed are considered and the vehicle safety is improved by setting a safe space around the vehicle, and establishing a steering cost heuristic function based on the control of heading angle difference, the vehicle dynamics constraints are satisfied, so as to improve the feasibility of path planning. The experimental results show that the improved A-star algorithm can avoid vehicle contours collision at different speeds and output a smoother path, and effectively generate higher quality path.

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“…D* algorithm can be considered as dynamic A* which is able reforming the path according to new information coming from sensors [5]. Meta-heuristic methods include particle swarm optimization (PSO), and ant colony optimization (ACO) [6]. In addition, there are intelligent methods, like artificial potential field (APF), genetic algorithm (GA), APF is used in different types of robots where the field of forces is applied on robot.…”

Section: Introductionmentioning

confidence: 99%

Development of Path Planning Algorithm Using Probabilistic Roadmap Based on Ant Colony Optimization

Abdulakareem¹,

Raheem²

2020

ETJ

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n this paper, a unique combination among probabilistic roadmap, ant colony optimization, and third order B-spline curve has been proposed to solve path-planning problem in complex and very complex environments. This proposed method can be divided into three stages. First stage is to construct a random map depending on the environment complexity using probabilistic roadmap algorithm. This could be done by sampling N nodes randomly in complex and very complex static environments, then connecting these nodes together according to some criteria or conditions. The constructed roadmap contains huge number of possible random paths that may connect the start and the goal points together. Second stage includes finding path within the pre-constructed roadmap. Ant colony optimization is selected to find or to search the best path between start and goal points. Finally, the third stage uses B-spline curve to smooth and reduce total length of the found path in the previous stage where path’s length has been reduced by 1% in first environment and by 15% in second environment. The results of the proposed approach ensure feasible path between start and goal points in complex and very complex environment. In addition, the path is guaranteed to be shortest, smooth, continues and safe.

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Metaheuristic Algorithm for Robotic Path Planning

Cited by 8 publications

References 7 publications

Path Planning Algorithms for Mobile Robots: A Survey

Path Planning Algorithms for Mobile Robots: A Survey

Improved A-star algorithm based on multivariate fusion heuristic function for autonomous driving path planning

Development of Path Planning Algorithm Using Probabilistic Roadmap Based on Ant Colony Optimization

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