Local Obstacle-Skirting Path Planning for a Fast Bi-steerable Rover using Bézier Curves

Fnadi, Mohamed; Du, Wenqian; Silva, Rafael Gomes da; Plumet, Frédéric; Benamar, Falz

doi:10.1109/icra40945.2020.9197563

Cited by 2 publications

(3 citation statements)

References 20 publications

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“…The trajectory planning method can directly find a path that meets the requirements but is not very efficient. This paper mainly focuses on smoothing methods combined with typical geometric methods, which can be divided into two parts: geometric [24][25][26][27][28][29][30] and optimisation [31][32][33][34][35][36] methods.…”

Section: Introductionmentioning

confidence: 99%

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A multiobjective path-smoothing algorithm based on node adjustment and turn-smoothing

Cao

Zhang

et al. 2023

Measurement and Control

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Presently, mobile robots experience problems of time consumption, poor security, and high computational complexity in global path-smoothing algorithms. This study presents a multiobjective path-smoothing algorithm, including a path point adjustment algorithm and a turn-smoothing method called the point adjustment algorithm and smoothing. First, the proposed path point adjustment algorithm filters and moves the nodes in an original path to decrease the length of the path and increase the angle of the turns in the path. Second, the proposed turn-smoothing method inserts the B-spline curve into the processed path to smooth the discontinuous turns in the path. Subsequently, the positions of the control points are adjusted based on the property of the B-spline to ensure that the smoothed path can avoid obstacles and satisfy the maximum curvature constraint. Simulation results show that the proposed algorithm can quickly calculate the path satisfying the robot dynamic constraints in various environments combined with different global path planning algorithms. Compared with other mainstream path-smoothing algorithms, this algorithm makes a substantial improvement in path length.

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Section: Introductionmentioning

confidence: 99%

“…Fnadi et al 24 designed a local path-smoothing method using cubic Bezier curves. By inserting control points near obstacles, Bezier curves are generated by these control points to meet the requirements of obstacle avoidance and path smoothness.…”

Section: Introductionmentioning

confidence: 99%

A multiobjective path-smoothing algorithm based on node adjustment and turn-smoothing

Cao

Zhang

et al. 2023

Measurement and Control

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“…The existing approaches to off-road navigation appear unsuitable for inexpensive small-sized forest rovers . Large-sized rovers, typically used in studies for off-road navigation, perceive the environment from a high viewpoint of around 1 m (e.g., Ho et al (2013a ; b) ; Ugenti et al (2022) ; Fnadi et al (2020) ; Baril et al (2022) ; Bagnell et al (2010) , also see comparison in Table 1 and Figure 1A ). In comparison, for small-sized forest rovers, discerning the forest scene from a size-proportional low viewpoint on the order of centimeters is relatively difficult due to the limited field-of-view in the vertical direction.…”

Section: Introductionmentioning

confidence: 99%

An embarrassingly simple approach for visual navigation of forest environments

et al. 2023

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Navigation in forest environments is a challenging and open problem in the area of field robotics. Rovers in forest environments are required to infer the traversability of a priori unknown terrains, comprising a number of different types of compliant and rigid obstacles, under varying lighting and weather conditions. The challenges are further compounded for inexpensive small-sized (portable) rovers. While such rovers may be useful for collaboratively monitoring large tracts of forests as a swarm, with low environmental impact, their small-size affords them only a low viewpoint of their proximal terrain. Moreover, their limited view may frequently be partially occluded by compliant obstacles in close proximity such as shrubs and tall grass. Perhaps, consequently, most studies on off-road navigation typically use large-sized rovers equipped with expensive exteroceptive navigation sensors. We design a low-cost navigation system tailored for small-sized forest rovers. For navigation, a light-weight convolution neural network is used to predict depth images from RGB input images from a low-viewpoint monocular camera. Subsequently, a simple coarse-grained navigation algorithm aggregates the predicted depth information to steer our mobile platform towards open traversable areas in the forest while avoiding obstacles. In this study, the steering commands output from our navigation algorithm direct an operator pushing the mobile platform. Our navigation algorithm has been extensively tested in high-fidelity forest simulations and in field trials. Using no more than a 16 × 16 pixel depth prediction image from a 32 × 32 pixel RGB image, our algorithm running on a Raspberry Pi was able to successfully navigate a total of over 750 m of real-world forest terrain comprising shrubs, dense bushes, tall grass, fallen branches, fallen tree trunks, small ditches and mounds, and standing trees, under five different weather conditions and four different times of day. Furthermore, our algorithm exhibits robustness to changes in the mobile platform’s camera pitch angle, motion blur, low lighting at dusk, and high-contrast lighting conditions.

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Local Obstacle-Skirting Path Planning for a Fast Bi-steerable Rover using Bézier Curves

Cited by 2 publications

References 20 publications

A multiobjective path-smoothing algorithm based on node adjustment and turn-smoothing

A multiobjective path-smoothing algorithm based on node adjustment and turn-smoothing

An embarrassingly simple approach for visual navigation of forest environments

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