Maximum clearance rapid motion planning algorithm

Paliwal, Shubham; Kala, Rahul

doi:10.1017/s0263574718000127

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…To ensure human safety, several approaches have been proposed [48,49] so that robot the becomes aware of the human. Furthermore, path planning method has been developed for motion planning, which is also goal-oriented [53]. In the real world, people interact with each other while walking.…”

Section: Sno Paper Descriptionmentioning

confidence: 99%

Learning-based simulation and modeling of unorganized chaining behavior using data generated from 3D human motion tracking

Malviya

Kala

2021

Robotica

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The paper models the unorganized chaining behavior, where humans need to walk in a chain due to a constrained environment. Detection and tracking are done using a 3D LiDAR, which has the challenges of environmental noises, uncontrolled environment, and occlusions. The Kalman filter is used for tracking. The trajectories are analyzed and used to train a behavioral model. The modeling has applications in socialistic robot motion planning and simulations. Based on the results, we conclude that the trajectory prediction by our approach is more socialistic and has a lesser error when compared to the artificial potential field method.

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Section: Sno Paper Descriptionmentioning

confidence: 99%

Learning-based simulation and modeling of unorganized chaining behavior using data generated from 3D human motion tracking

Malviya

Kala

2021

Robotica

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“…Paliwal and Kala [ 22 ] presented a goal-oriented path planning algorithm that provides high clearance from obstacles by propagating circles in the free configuration space. Nevertheless, this method is limited to maps where the clearance is at least twice the robot, i.e., environments with large passageways.…”

Section: Introductionmentioning

confidence: 99%

Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

Nascimento

Barrios-Aranibar

Santos

et al. 2021

Sensors

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The planning of safe paths is an important issue for autonomous robot systems. The Probabilistic Foam method (PFM) is a planner that guarantees safe paths bounded by a sequence of structures called bubbles that provides safe regions. This method performs the planning by covering the free configuration space with bubbles, an approach analogous to a breadth-first search. To improve the propagation process and keep the safety, we present three algorithms based on Probabilistic Foam: Goal-biased Probabilistic Foam (GBPF), Radius-biased Probabilistic Foam (RBPF), and Heuristic-guided Probabilistic Foam (HPF); the last two are proposed in this work. The variant GBPF is fast, HPF finds short paths, and RBPF finds high-clearance paths. Some simulations were performed using four different maps to analyze the behavior and performance of the methods. Besides, the safety was analyzed considering the new propagation strategies.

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“…13 Although developed for obstacle avoidance, the these algorithms have been put into use in other fields also. The exhaustive searching pattern of the Bug algorithm makes it suitable for developing more realistic path planners, which imitate a biological organism/human searching the path 14,15 rather than heuristics-based path planning. Bug algorithm-based strategies have been utilized in video games and military surveillance also.…”

Section: Introductionmentioning

confidence: 99%

Development and analysis of a novel obstacle avoidance strategy for a multi-robot system inspired by the Bug-1 algorithm

2020

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This paper addresses the development and implementation of an obstacle avoidance strategy for a multi-robot system operating in an unknown environment. This novel strategy is based on the conventional Bug-1 obstacle avoidance algorithm, which is a non-heuristic method for obstacle avoidance in an unknown environment. In the Bug-1 algorithm, a robot circumnavigates the obstacle to find the coordinates of the point, having minimum distance to the goal. In the case of the new strategy, two robots will circumnavigate the obstacle in such a manner that it will reduce both the total travel time and the distance traveled. Information acquired by the individual robots during the circumnavigation is shared across other robots to accomplish the obstacle avoidance efficiently. A theoretical analysis is carried out to show the improvement in travel time and energy expenditure of the robots in implementing the new strategy. Different test scenarios for comparing the performance of the obstacle avoidance strategies using simulations is also identified. The simulation studies using these scenarios suggest that the new algorithm is a better algorithm with respect to multi-robot obstacle avoidance. The experimental study conducted also shows that robots using this new algorithm have a better travel time and less energy expenditure than the conventional Bug-1 algorithm.

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Maximum clearance rapid motion planning algorithm

Cited by 8 publications

References 23 publications

Learning-based simulation and modeling of unorganized chaining behavior using data generated from 3D human motion tracking

Learning-based simulation and modeling of unorganized chaining behavior using data generated from 3D human motion tracking

Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

Development and analysis of a novel obstacle avoidance strategy for a multi-robot system inspired by the Bug-1 algorithm

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