A unified approach to planning, sensing and navigation for mobile robots

Zelinsky, Alexander; Yuta, Shin’ichi

doi:10.1007/bfb0027613

Cited by 13 publications

(5 citation statements)

References 8 publications

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“…For example, Arkin, Fekete, and Mitchell (2000) propose an algorithm that constructs a tour of length at most 2.5 times the length of the optimal tour in a time O(n log n), where n is the number of edges of the polygonal environment. The provably complete coverage methods that approximate the environment with cells of same size and shape reported in (Choset 2001) are not guaranteed to produce shortest coverage paths, as for example the wavefront propagation method of (Zelinsky et al 1993). The method in (Gabriely and Rimon 2001) uses an optimality criterion that is not related to the length of the path but to avoid repetitive coverage.…”

Section: Related Workmentioning

confidence: 99%

Searching for Optimal Off-Line Exploration Paths in Grid Environments for a Robot with Limited Visibility

Amigoni

Basilico

2021

AAAI

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Robotic exploration is an on-line problem in which autonomous mobile robots incrementally discover and map the physical structure of initially unknown environments. Usually, the performance of exploration strategies used to decide where to go next is not compared against the optimal performance obtainable in the test environments, because the latter is generally unknown. In this paper, we present a method to calculate an approximation of the optimal (shortest) exploration path in an arbitrary environment. We consider a mobile robot with limited visibility, discretize a two-dimensional environment with a regular grid, and formulate a search problem for finding the optimal exploration path in the grid, which is solved using A*. Experimental results show the viability of our approach for realistically large environments and its potential for better assessing the performance of on-line exploration strategies.

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Section: Related Workmentioning

confidence: 99%

Searching for Optimal Off-Line Exploration Paths in Grid Environments for a Robot with Limited Visibility

Amigoni

Basilico

2021

AAAI

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“…Therefore, authors in [13] have used a combination of parallel navigation and potential fields guidance methods and then performance metrics have been improved for a mobile robot rendezvous. In addition, The sub-goal method is presented in [14]- [15], sampling-based methods in [16], neural network has been mentioned in [17], Distance Wave Transform can be found in [18], a Start Algorithm is tested in [19], where a D start algorithm is presented by authors in [20]. However, a modified A start algorithm is presented in [21].…”

Section: Introductionmentioning

confidence: 99%

Path Planning of the Manipulator Arm FANUC Based on Soft Computing Techniques

Benchikh¹,

Ahmed‐Foitih²,

Bouzgou³

2020

IREACO

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In this paper, direct and inverse geometric models for a 6 degrees of freedom manipulator robot arm are developed, and a set of homogeneous matrices are generated by Denavit-Hartenberg formalism. Moreover, a path planning method based on soft computing techniques is presented, which consists of using the neural network to model the end-effector workspace, and then determining the optimal trajectory to reach a desired position. The optimization of the trajectory depends on the minimization of the cost function, defined by the sum of two energies. The first one is the collision penalty (E c) generated by each obstacle shape; the second one is the trajectory length penalty (E l). Four steps are considered; at first, the configuration of the robot workspace where any assumption is taken into account. The robot is assimilated to a particle that moves inside a small two-dimensional space, and the obstacle is a polygon. Second, the Multilayer Perceptron Neural Network (MLP neural network) is used for the robot workspace modeling. Model block inputs are robot's previous position values and the outputs are robot's following position values. Then, the proposed approach is applied to optimize the cost function and to determine the end-effector trajectory in case of obstacles. The obtained result is a smooth trajectory, which represents robot motion. Finally, the presented approach is validated on a FANUC robot arm in a virtual environment, where the simulation results show this approach efficiency.

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“…Examples of traditional approaches proposed by previous researchers to solve RPP problems are artificial potential field (Khatib, 1985), neural network (Xin, 2005), distance wave transform (Zelinsky, Oct 1993), heuristic algorithm known as A* algorithm (Charles.W.Warren, 1993) (Hart et al, 1968), and D* algorithm (Yahja, 2000). It has been proven in various researches that these algorithms were able to find global path successfully and that the various methods has its own strengths and limitations over others in certain aspect of path planning.…”

Section: Introductionmentioning

confidence: 99%

GENETIC ALGORITHM VERSUS ANT COLONY OPTIMIZATION ALGORITHM - Comparison of Performances in Robot Path Planning Application

Sariff

Buniyamin

2010

Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics

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This paper presents the results of a research that uses a simulation approach to compare the effectiveness and efficiency of two path planning algorithms. Genetic Algorithm (GA) and Ant Colony Optimization (ACO) Algorithm for Robot Path Planning (RPP) were tested in a global static environment. Both algorithms were applied within a global map that provides feasible nodes from start point to goal. Performances between both algorithms were compared and evaluated in terms of computational efficiency by measuring the speed and number of iterations, accuracy of solution, solution variation and convergence behavior.

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A unified approach to planning, sensing and navigation for mobile robots

Cited by 13 publications

References 8 publications

Searching for Optimal Off-Line Exploration Paths in Grid Environments for a Robot with Limited Visibility

Searching for Optimal Off-Line Exploration Paths in Grid Environments for a Robot with Limited Visibility

Path Planning of the Manipulator Arm FANUC Based on Soft Computing Techniques

GENETIC ALGORITHM VERSUS ANT COLONY OPTIMIZATION ALGORITHM - Comparison of Performances in Robot Path Planning Application

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