Surveillance and Obstacle Avoiding Autonomous Robot

Rooban, S.; Joshitha, C.; Eshwar, Idupulapati Venkata Sai; Gade, Mary Swarna Latha

doi:10.1109/asiancon51346.2021.9544623

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…The IoT-based systems are complicated and comprise smart items [8]. The IoT-based systems are complicated and comprise smart items [9]. IoT supports decentralized algorithms, which are employed to overcome complexities in configuring models due to their dynamic features, such as device mobility, wireless connection, and information provision [10].…”

Section: Figure 3 Swarm Intelligence Capabilitymentioning

confidence: 99%

“…, q(N−1)) ∈ $N indicate the combination status in G then ˜f =,(Q) and Q =,−1( ˜f ).,−1 is no longer is set of M. In one of these situations, using a minimal norm response, it can be design by manipulating laws of agents˙ q˙ = ˙˜f using the least-squares method, where,'˙' stands for the difference of M and N thus, q˙ =˙ −1 ˜f. Each step of all the agents transfers their role statistics to an external agent which calculates the error e = ' ˜fd − ˜f ' and returns error rate in agents [9].…”

Section: Seu Network (Swarm Enabling Unit)mentioning

confidence: 99%

See 1 more Smart Citation

A self-organized shape formation in swarm robots

Rooban,

Bhargav,

Vamsi Reddy

et al. 2023

J. Phys.: Conf. Ser.

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This work proposes a functional method-based approach for shape formation. The core principle in swarm intelligence is that simple swarm behaviour can be established with assigning the rules. To keep all the agents in the given shape and to build the set of motion, desired locations must be assigned to govern the leader and the other followers. Given the restrictions of communication, collision avoidance, and computation challenges, the proposed work is ideal for swarm robots shape formation. The proposed dynamic shape formation uses the shortest path method and the obstacle avoidance algorithm. Natural swarm inspired method based on the Particle Swarm Optimization (PSO) model is used to form the robots in the assigned position. A set of simulation analysis demonstrate that the convergence and scalability of our proposed technique. The effectiveness of pattern formation is increased further by adjusting specific parameters in the PSO model with the aid of the MATLAB simulator.

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Section: Figure 3 Swarm Intelligence Capabilitymentioning

confidence: 99%

Section: Seu Network (Swarm Enabling Unit)mentioning

confidence: 99%

A self-organized shape formation in swarm robots

Rooban,

Bhargav,

Vamsi Reddy

et al. 2023

J. Phys.: Conf. Ser.

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“…A hypothetical working scenario of AUH is shown in Figure 1. Complete Coverage Path Planning algorithms have been extensively used in practical applications such as cleaning of sweeping robots, mine sweeping, surveillance [9], harvesting fruits in agriculture [10], and underwater operations [11]. CCPP algorithms are characterized as classical, heuristic, and, most recently, deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

A Complete Coverage Path Planning Approach for an Autonomous Underwater Helicopter in Unknown Environment Based on VFH+ Algorithm

Ma,

Zou,

2024

JMSE

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An Autonomous Underwater Helicopter (AUH) is a disk-shaped, multi-propelled Autonomous Underwater Vehicle (AUV), which is intended to work autonomously in underwater environments. The near-bottom area sweep in unknown environments is a typical application scenario, in which the complete coverage path planning (CCPP) is essential for AUH. A complete coverage path planning approach for AUH with a single beam echo sounder, including the initial path planning and online local collision avoidance strategy, is proposed. First, the initial path is planned using boustrophedon motion. Based on its mobility, a multi-dimensional obstacle sensing method is designed with a single beam range sonar mounted on the AUH. The VFH+ algorithm is configured for the heading decision-making procedure before encountering obstacles, based on their range information at a fixed position. The online local obstacle avoidance procedure is simulated and analyzed with variations of the desired heading direction and corresponding polar histograms. Finally, several simulation cases are set up, simulated and compared by analyzing the heading decision in front of different obstacle situations. The simulation results demonstrate the feasibility of the complete coverage path planning approach proposed, which proves that AUH completing a full coverage area sweep in unknown environments with a single beam sonar is viable.

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