Motion and Operation Planning of Robotic Systems

Carbone, Giuseppe; Gómez-Bravo, Fernando

doi:10.1007/978-3-319-14705-5

Cited by 39 publications

(2 citation statements)

References 36 publications

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“…This paper shows the potential field method application. However, other motion planners, such as the rapidly exploring random tree [15] and the fast marching square method [16] can use our method. In other words, it can facilitate consideration of the stochastic environment in relation to existing motion planners: the only constraint is that object position should be normally distributed.…”

Section: Resultsmentioning

confidence: 99%

Distance Determination Method for Normally Distributed Obstacle Avoidance of Mobile Robots in Stochastic Environments

Noh

Huh

2016

International Journal of Advanced Robotic Systems

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Obstacle avoidance methods require knowledge of the distance between a mobile robot and obstacles in the environment. However, in stochastic environments, distance determination is difficult because objects have position uncertainty. The purpose of this paper is to determine the distance between a robot and obstacles represented by probability distributions. Distance determination for obstacle avoidance should consider position uncertainty, computational cost and collision probability. The proposed method considers all of these conditions, unlike conventional methods. It determines the obstacle region using the collision probability density threshold. Furthermore, it defines a minimum distance function to the boundary of the obstacle region with a Lagrange multiplier method. Finally, it computes the distance numerically. Simulations were executed in order to compare the performance of the distance determination methods. Our method demonstrated a faster and more accurate performance than conventional methods. It may help overcome position uncertainty issues pertaining to obstacle avoidance, such as low accuracy sensors, environments with poor visibility or unpredictable obstacle motion.

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Section: Resultsmentioning

confidence: 99%

Distance Determination Method for Normally Distributed Obstacle Avoidance of Mobile Robots in Stochastic Environments

Noh

Huh

2016

International Journal of Advanced Robotic Systems

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“…As such, the development and evaluation of search algorithms for path planning have garnered significant attention. [1] In this study, we conducted a comparative analysis to evaluate the performance of three popular search algorithms: Depth-First Search (DFS), Breadth-First Search (BFS), and A* (A-star). These algorithms have been widely studied and employed in various fields due to their effectiveness in finding optimal paths.…”

Section: Introductionmentioning

confidence: 99%

Exploring Maze Navigation: A Comparative Study of DFS, BFS, and A* Search Algorithms.

ELKARI,

OURABAH,

SEKKAT

et al. 2024

Stat., optim. inf. comput.

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A comparative study was conducted using Python and Pmaze to evaluate the performance of DFS, BFS, and A* search algorithms in path planning for a maze. The main objective of the project was to compare the efficiency of these algorithms in terms of path cost and algorithmic complexity. No physical robot was used in this study as it focused solely on the analysis of search algorithms. This study contributes to the advancement of knowledge in the field of path planning by exploring different approaches for mazes using Python and Pmaze.

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