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Obstacle avoidance in a dynamic environment: a collision cone approach

Chakravarthy, A., Ghose, D.·1998·IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans

Abstract

A novel collision cone approach is proposed as an aid to collision detection and avoidance between irregularly shaped moving objects with unknown trajectories. It is shown that the collision cone can be effectively used to determine whether collision between a robot and an obstacle (both moving in a dynamic environment) is imminent. No restrictions are placed on the shapes of either the robot or the obstacle, i.e., they can both be of any arbitrary shape. The collision cone concept is developed in a phased manner starting from existing analytical results that enable prediction of collision between two moving point objects. These results are extended to predict collision between a point and a circular object, between a point and an irregularly shaped object, between two circular objects, and finally between two irregularly shaped objects. Using the collision cone approach, several strategies that the robot can follow in order to avoid collision, are presented. A discussion on how the shapes of the robot and obstacles can be approximated in order to reduce computational burden is also presented. A number of examples are given to illustrate both collision prediction and avoidance strategies of the robot.

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Metadata

Authors
Chakravarthy, A., & Ghose, D.
Venue
IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
Published
1998
Volume
28
Issue
5
Pages
562-574
ISSN
1083-4427 (ISSN-L)
OpenAlex
W2136947569
Cited by
556
References
0
FWCI
2.07
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Obstacle avoidance in a dynamic environment: a collision cone approach. https://app.cochat.ai/papers/10.1109/3468.709600
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Chakravarthy, A., & Ghose, D. (1998). Obstacle avoidance in a dynamic environment: a collision cone approach. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans. https://doi.org/10.1109/3468.709600

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.