Sensor-based autonomous robot navigation under unknown environments with grid map representation

Luo, Chaomin; Gao, Jiyong; Li, Xinde; Mo, Hongwei; Jiang, Qimi

doi:10.1109/sis.2014.7011782

Cited by 25 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the AUV's current position in the GBNN denoted as P i , P k is the coordinate of the points in k -th P i ’s neighbourhood, and the next moving position P n of AUV can be obtained by Since the neural activity of target positions are positive in the GBNN and the neural activity of obstacle positions are negative, the target positions attract the AUV globally while the obstacle areas just push the AUV away to avoid local collisions. For more detail, the reader can refer to Luo et al (2014a; 2014b).…”

Section: The Glasius Bio-inspired Self-organising Map (Gbsom)mentioning

confidence: 99%

“…Aiming at the problem of speed jump and the absence of an obstacle avoidance capability in the SOM algorithm, a Glasius Bio-inspired Self-Organising Map (GBSOM) (Glasius et al, 1995) algorithm combined with a Glasius Bio-inspired Neural Network (GBNN) (Luo et al, 2014a; 2014b) and SOM is proposed in this paper. The implementation steps are: (1) 3D GBNN model is established to represent the grid of the 3D underwater working environment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Task Assignment and Path Planning of a Multi-AUV System Based on a Glasius Bio-Inspired Self-Organising Map Algorithm

2017

View full text Add to dashboard Cite

For multi-Autonomous Underwater Vehicle (multi-AUV) system task assignment and path planning, a novel Glasius Bio-inspired Self-Organising Map (GBSOM) neural networks algorithm is proposed to solve relevant problems in a Three-Dimensional (3D) grid map. Firstly, a 3D Glasius Bio-inspired Neural Network (GBNN) model is established to represent the 3D underwater working environment. Using this model, the strength of neural activity is calculated at each node within the GBNN. Secondly, a Self-Organising Map (SOM) neural network is used to assign the targets to a set of AUVs and determine the order of the AUVs to access the target point. Finally, according to the magnitude of the neuron activity in the GBNN, the next AUV target point can be autonomously planned when the task assignment is completed. By repeating the above three steps, access to all target points is completed. Simulation and comparison studies are presented to demonstrate that the proposed algorithm can overcome the speed jump problem of SOM algorithms and path planning in the 3D underwater environments with static or dynamic obstacles.

show abstract

Section: The Glasius Bio-inspired Self-organising Map (Gbsom)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Task Assignment and Path Planning of a Multi-AUV System Based on a Glasius Bio-Inspired Self-Organising Map Algorithm

2017

View full text Add to dashboard Cite

show abstract

“…For energy and time efficiency, the robot should travel the shortest path and minimise turning. For a given current robot location, denoted by Lc , the next robot location Ln is obtained by (Luo et al, 2014b) where s is the number of neighbouring neurons of the Lc -th neuron ( s = 26), i.e. all the possible next locations of the current location Lc .…”

Section: Search Algorithmmentioning

confidence: 99%

Multi-AUV Cooperative Target Search Algorithm in 3-D Underwater Workspace

Cao

2017

J. Navigation

View full text Add to dashboard Cite

To improve the efficiency of multiple Autonomous Underwater Vehicles (multi-AUV) cooperative target search in a Three-Dimensional (3D) underwater workspace, an integrated algorithm is proposed by combining a Self-Organising Map (SOM), neural network and Glasius Bioinspired Neural Network (GBNN). With this integrated algorithm, the 3D underwater workspace is first divided into subspaces dependent on the abilities of the AUV team members. After that, tasks are allocated to each subspace for an AUV by SOM. Finally, AUVs move to the assigned subspace in the shortest way and start their search task by GBNN. This integrated algorithm, by avoiding overlapping search paths and raising the coverage rate, can reduce energy consumption of the whole multi-AUV system. The simulation results show that the proposed algorithm is capable of guiding multi-AUV to achieve a multiple target search task with higher efficiency and adaptability compared with a more traditional bioinspired neural network algorithm.

show abstract

“…Neural activations propagate from external input I according to the local connectivity of the neurons, and the entire network can be considered a diffusive model that produces landscapes in which following positive gradients leads to target states. With well-chosen constant multipliers, this method exhibits no undesirable dynamics and has been found to be considerably versatile in a variety of subsequent works, including those of Borg et al (2011) and Luo et al (2014).…”

Section: Neural Architecturementioning

confidence: 99%

Incremental Neuroevolution of Reactive and Deliberative 3D Agents

Stanton¹,

Channon²

2015

07/20/2015-07/24/2015

View full text Add to dashboard Cite

Following earlier work on the neuroevolution of deliberative behaviour to solve increasingly challenging tasks in a twodimensional dynamic world, this paper presents the results of extending the original system to a three-dimensional rigid body simulation. The 3D physically based setting requires that a successful agent continually and deliberately adjust its gait, turning and other motor control over the many stages and sub-stages of these tasks, within its individual evaluation. Achieving such complex interplay between motor control and deliberative control, within a neuroevolutionary framework, is the focus of this work. To this end, a novel neural architecture is presented and an incremental evolutionary approach used to bootstrap the locomotive behaviour of the agents. Agent morphology is fixed as a quadruped with three degrees of freedom per limb. Agent populations have no initial knowledge of the problem domain, and evolve to move around and then solve progressively more difficult challenges in the environment using a tournament-based co-evolutionary algorithm. The results demonstrate not only success at the tasks but also a variety of intricate lifelike behaviours being used, separately and in combination, to achieve this success. Given the problem-agnostic controller architecture, these results indicate a potential for discovering yet more advanced behaviours in yet more complex environments.

show abstract

Sensor-based autonomous robot navigation under unknown environments with grid map representation

Cited by 25 publications

References 10 publications

Task Assignment and Path Planning of a Multi-AUV System Based on a Glasius Bio-Inspired Self-Organising Map Algorithm

Task Assignment and Path Planning of a Multi-AUV System Based on a Glasius Bio-Inspired Self-Organising Map Algorithm

Multi-AUV Cooperative Target Search Algorithm in 3-D Underwater Workspace

Incremental Neuroevolution of Reactive and Deliberative 3D Agents

Contact Info

Product

Resources

About