Robot navigation in unknown terrains using learned visibility graphs. Part I: The disjoint convex obstacle case

Oommen, B. John; Iyengar, Satish; Rao, Nageswara S. V.; Kashyap, R.L.

doi:10.1109/jra.1987.1087133

Cited by 140 publications

(54 citation statements)

References 12 publications

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“…The shortest path can be found in O(n 2 logn) time using the A* algorithm with the Euclidean distance to the goal as the heuristic function (Latombe, 1991). Works such as (Oommen et al, 1987) and (Yeung & Bekey, 1987) have employed this approach for path planning. The Voronoi Diagram is defined as the set of points that are equidistant from two or more object features.…”

Section: Roadmap Methodsmentioning

confidence: 99%

Composite Models for Mobile Robot Offline Path Planning

Masehian¹,

Amin-Naseri²

2007

Mobile Robots: Perception &Amp; Navigation

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Section: Roadmap Methodsmentioning

confidence: 99%

Composite Models for Mobile Robot Offline Path Planning

Masehian¹,

Amin-Naseri²

2007

Mobile Robots: Perception &Amp; Navigation

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“…A structured environment can be easily converted into a graph (or similar structure) with a limited number of nodes for fast planning, although search-based planning in an unstructured environment would be too computationally expensive. Typical approaches include Voronoi maps [16], velocity obstacles [17] and visibility graphs [18,19].…”

Section: Trajectory Generation and Assistancementioning

confidence: 99%

“…The developed solution is a hybrid of visibility graphs [18,19] and adaptive roadmaps [23][24][25][26]. The visibility graphs are well-suited for structured environments for which they perform fast and effective planning.…”

Section: Proposed Solution and Main Contributionsmentioning

confidence: 99%

Sensor-Based Trajectory Generation for Advanced Driver Assistance System

2013

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This paper investigates the trajectory generation problem for an advanced driver assistance system that could sense the driving state of the vehicle, so that a collision free trajectory can be generated safely. Specifically, the problem of trajectory generation is solved for the safety assessment of the driving state and to manipulate the vehicle in order to avoid any possible collisions. The vehicle senses the environment so as to obtain information about other vehicles and static obstacles ahead. Vehicles may share the perception of the environment via an inter-vehicle communication system. The planning algorithm is based on a visibility graph. A lateral repulsive potential is applied to adaptively maintain a trade-off between the trajectory length and vehicle clearance, which is the greatest problem associated with visibility graphs. As opposed to adaptive roadmap approaches, the algorithm exploits the structured nature of the environment for construction of the roadmap. Furthermore, the mostly organized nature of traffic systems is exploited to obtain orientation invariance, which is another limitation of both visibility graphs and adaptive roadmaps. Simulation results show that the algorithm can successfully solve the problem for a variety of commonly found scenarios.

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“…Potential field method is [11] widely used because of its simple structure and easy execution, but this approach may face the local minima problem, which leads to robot trap situation within its environments. Visibility graph [10] requires more control accuracy, because its search path efficiency is low as described in [12]. In the cell decomposition approach [5], the environment is divided into a number of cells which are predefined in size and shape.…”

Section: Introductionmentioning

confidence: 99%

PSO based path planner of an autonomous mobile robot

Deepak

Parhi

2012

Open Computer Science

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A novel approach based on particle swarm optimization has been presented in this paper for solving mobile robot navigation task. The proposed technique tries to optimize the path generated by an intelligent mobile robot from its source position to destination position in its work space. For solving this problem, a new fitness function has been modelled, which satisfies the obstacle avoidance and optimal path traversal conditions. From the obtained fitness values of each particle in the swarm, the robot moves towards the particle which is having optimal fitness value. Simulation results are provided to validate the feasibility of the developed methodology in various unknown environments.

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Robot navigation in unknown terrains using learned visibility graphs. Part I: The disjoint convex obstacle case

Cited by 140 publications

References 12 publications

Composite Models for Mobile Robot Offline Path Planning

Composite Models for Mobile Robot Offline Path Planning

Sensor-Based Trajectory Generation for Advanced Driver Assistance System

PSO based path planner of an autonomous mobile robot

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