Optimization of Rao Blackwellized Particle Filter SLAM using Firefly algorithm

Navigation accuracy, which is an imperative performance indicator for mobile robots, is intimately associated with the grid mapping algorithm (G-mapping) accuracy. In an unstructured environment, mobile robot positioning accuracy is important to ensure safety. For this reason, in this study G-mapping Algorithm is modelled based on Rao-Blackwellized particle filter (RBPF) offering better results with a low number of sensors and features. To investigate various methods' effectiveness, a comparative analysis of three optimization methods namely Gradient descent, ANT colony, and firefly algorithm was made. The results exhibit that the firefly method performs well in terms of navigation accuracy, particle degradation, and ensuring mobile robot safety in a complex and unstructured environment.

Effect of Samarium Additions on Shape Memory and Thermomechanical Behavior of Iron Based Shape Memory Alloys

In the present work, a comparative study of the shape memory and thermomechanical behavior of four alloys containing different amount of samarium have been carried out at a strain rate of 0.08x10 -6 s -1 . After hot rolling, annealing and solution treatment, the alloy samples were tensile deformed at room temperature from 1% to 5% and were then recovered at 600̊C for 20 minutes repeatedly for six times to complete six training cycles. It has been found that the addition of samarium strengthens the austenitic matrix, increases the c/a ratio and reduces the grain size. It has been noticed that the addition of samarium (0.64 wt. %) improves the shape memory effect when the alloys were strained from 1% to 5%. This improvement in shape memory effect presumably can be regarded as the increase in strength, increase in c/a ratio and absence of ά (martensite). It has been further noticed that when samarium contents are gradually reduced from 0.64 % to 0.21% the shape memory effect decreases. The decrease in shape memory effect by the decrease in samarium contents can be attributed to decrease in strength, decrease in c/a ratio and formation of ά (martensite). It has been further noticed that the shape memory effect decreases with the increase in amount of strain. It is found that thermomechanical treatment (training) results in improvement of shape memory effect and has a significant influence on mechanical parameters like proof stress (σ 0.002 ), critical stress (σ 0.0008 ) and strain hardening exponent. The improvement in shape memory effect by thermomechanical treatment can be regarded as the effect of reduction in the values of proof stress and critical stress during training which facilitates the formation of ε (martensite). It has also been noticed that excessive training may result in the formation of ά (martensite) due to continuous softening of the alloy during training, thus degrading the shape memory effect. Finally, it has also been noticed that the addition of samarium increases the values of proof stress, critical stress and strain hardening exponent. Although the addition of samarium increases the values of proof stress, critical stress and strain hardening exponent yet it has not an adverse effect on shape memory effect.