An Enhanced Experimental Study of GPS Based Path Tracking Non-holonomic Robot with SAWOA Algorithm

Mukherjee, Subhradip; Kumar, R.; Borah, Siddhanta; Bhattacharjee, Rituraj

doi:10.1109/iccsea49143.2020.9132956

Cited by 2 publications

(10 citation statements)

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“…We have also calculated the percentage deviations of different algorithms in environment1. The result in environment1 shows that, the proposed algorithm has 9.31%, 4.80%, 12.61%, 22.46% and 18.36% lower deviation in travel time and 0.53%, 0.39%, 0.63%, 1.06% and 1.33% lower deviation in path length compared to OAIA [7] , DFMB [9] , SAWOA [11] , MLD [19] and SRN [20] approaches. The comparative studies of different algorithms in environment2 are presented in table 8.…”

Section: Table 4 Different Environments For Simulationmentioning

confidence: 99%

“…The overall percentage of successful iterations is 66 percent. The overall accuracy percentage We have compared the final results of the proposed algorithm with OAIA [7] , DFMB [9] , SAWOA [11] , MLD [19] and SRN [20] approaches. The comparative studies in environment1 are presented in table 7.…”

Section: Table 4 Different Environments For Simulationmentioning

confidence: 99%

“…DFMB [9] 378.753 9.620 68 55 SAWOA [11] 379.741 10.493 58 50 MLD [19] 381.548 11.850 55 48 SRN [20] 382.663 11.245 52 46…”

Section: Table 4 Different Environments For Simulationmentioning

confidence: 99%

“…The comparative studies of different algorithms in environment2 are presented in table 8. OAIA [7] 379.847 16.235 56 48 DFMB [9] 379.164 15.639 59 51 SAWOA [11] 380.221 16.672 49 46…”

Section: Table 4 Different Environments For Simulationmentioning

confidence: 99%

“…The percentage deviations of different algorithms in environment2 have been calculated and it can be seen that, the proposed algorithm has 6.46%, 2.96%, 8.87%, 21.58% and 15.66% lower deviation in travel time and 0.51%, 0.36%, 0.59%, 1.15% and 1.34% lower deviation in path length compared to OAIA [7] , DFMB [9] , SAWOA [11] , MLD [19] and SRN [20] approaches. The comparative percentage deviation with other algorithms is shown in figure 14.…”

Section: Table 4 Different Environments For Simulationmentioning

confidence: 99%

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A Leader Follower Algorithm Based Path Navigation Approach for Multiple Autonomous Vehicles

Mukherjee,

Kumar,

Borah

2023

Preprint

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Multi-agent path navigation in unknown environments is a challenging task where an effective device-to-device communication-based algorithm needs to be designed with minimum error. A leader-follower algorithm has been proposed in this literature for multi-agent communication. The proposed algorithm has improved the percentage deviation in travel time and the percentage deviation in path length. The proposed algorithm is designed in two parts. One part is for leader path navigation, and the other is used to update the followers. The obstacle avoidance mechanism is used in the first part of the algorithm. The computation speed of the algorithm is mainly dependent on the guidance that the leader provides to the follower agent. Path update from the leader side to the follower side is the most important task in this proposed method. One leader and two follower agents were considered in simulation environments to establish the feasibility of the algorithm. In the simulation, each follower agent was made to commute with a few seconds' time gap after the leader had started. After several simulations, the proposed approach has a 2.82% average deviation in travel time and a 31% average deviation in path length. The average success rate of the proposed technique in different environments was found equal to 66%. Studies show that the accuracy of this technique is more than 54% on average.

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Section: Table 4 Different Environments For Simulationmentioning

confidence: 99%

Section: Table 4 Different Environments For Simulationmentioning

confidence: 99%

“…DFMB [9] 378.753 9.620 68 55 SAWOA [11] 379.741 10.493 58 50 MLD [19] 381.548 11.850 55 48 SRN [20] 382.663 11.245 52 46…”

Section: Table 4 Different Environments For Simulationmentioning

confidence: 99%

“…The comparative studies of different algorithms in environment2 are presented in table 8. OAIA [7] 379.847 16.235 56 48 DFMB [9] 379.164 15.639 59 51 SAWOA [11] 380.221 16.672 49 46…”

Section: Table 4 Different Environments For Simulationmentioning

confidence: 99%