Collision Avoidance and Rendezvous of Quadric Surfaces Moving on Planar Environments

Dhal, Kashish; Kashyap, Abhishek; Chakravarthy, Animesh

doi:10.1109/cdc45484.2021.9683549

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The traditional algorithms, which were proposed earlier and are based on simple mechanics, have been widely used, especially in path planning for unmanned aerial vehicles (UAVs) [11,12] and driverless cars [13,14]. The fundamental algorithms represented by reactive path-planning algorithms [15][16][17][18] such as potential field, velocity obstacle, collision cone, etc., also play a huge role in solving such problems. Karmokar et al [15] presented a novel vision-based framework for tracking dynamic objects using guidance laws based on a rendezvous cone approach.…”

Section: Introductionmentioning

confidence: 99%

“…Karmokar et al [15] presented a novel vision-based framework for tracking dynamic objects using guidance laws based on a rendezvous cone approach. Furthermore, by performing a series of simulations to show the efficacy of their methods, Dhal et al [16] modelled the shapes of objects using quadric surfaces. A collision/rendezvous cone concept was employed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Multilevel Adaptive Path-Planning Model in Off-Road Environments

Song¹,

Gao²

2022

Applied Sciences

View full text Add to dashboard Cite

Most existing path-planning algorithms are applied in either trafficable environments or non-trafficable environments. Off-road vehicles (ORVs) are often faced with a mix of trafficable and non-trafficable environments. Therefore, trafficability should be considered in path planning for ORVs. Conventional ant colony algorithms (ACAs) are prone to stagnation and often fail to reach the optimal path. To address these problems, an improved ACA that considers trafficability was proposed in this study, which improved the pheromone distribution rules and adaptively adjusted the pheromone volatility coefficient. Based on this improved ACA, a multilevel adaptive path-planning model was proposed to solve path-planning problems with various scales of area. Experiments and comparative studies revealed that the improved ACA was applicable to path-planning problems in complex environments and achieved better performance and a higher computing efficiency than conventional counterparts.

show abstract