This paper considers the issue of the search and rescue operation of humans after natural or man-made disasters. This problem arises after several calamities, such as earthquakes, hurricanes, and explosions. It usually takes hours to locate the survivors in the debris. In most cases, it is dangerous for the rescue workers to visit and explore the whole area by themselves. Hence, there is a need for speeding up the whole process of locating survivors accurately and with less damage to human life. To tackle this challenge, we present a scalable solution. We plan to introduce the usage of robots for the initial exploration of the calamity site. The robots will explore the site and identify the location of human survivors by examining the video feed (with audio) captured by them. They will then stream the detected location of the survivor to a centralized cloud server. It will also monitor the associated air quality of the selected area to determine whether it is safe for rescue workers to enter the region or not. The human detection model for images that we have used has a mAP (mean average precision) of 70.2%. The proposed approach uses a speech detection technique which has an F1 score of 0.9186 and the overall accuracy of the architecture is 95.83%. To improve the detection accuracy, we have combined audio detection and image detection techniques.

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Acceptability of mobile robots in various applications has led to an increase in mobile robots’ research areas. Path planning is one of the core areas which needs to be improvised at a higher level. Optimization is playing a more prominent role these days. The nature-inspired algorithm is contributing to a greater extent in achieving optimization. This article presents the modified cuckoo search algorithm using tournament selection function for robot path planning. Path length and Path time are the algorithm’s parameters to validate the effectiveness and acceptability of the output. The cuckoo search algorithm’s fundamental working principle is taken as the baseline, and the tournament selection function is adapted to calculate the optimum path for robots while navigating from its initial position to final position. The tournament selection function is replacing the concept of random selection done by the cuckoo search algorithm. The use of tournament selection overcomes local minima for robots while traversing in the configuration space and increases the probability of giving more optimum results. The conventional cuckoo search algorithm whose random selection mechanism may lead to premature convergence may fall into the local minima. The use of tournament selection function increases the probability of giving better results as it allows all the possible solution to take part in the tournament. The results are analysed and compared with other relevant work like cuckoo search algorithm and particle swarm optimization technique and presented in the article. The proposed method produced a better output in terms of path length and path time.

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Robot path planning is one of the core issues in robotics and its application. Optimizing the route discovery becomes more important while dealing with the robot-based application. This paper proposes the concept of early detection of the obstacle present in the workspace of the robots. To early detect the obstacle, this paper proposes the concept of a snake algorithm along with the traditional path planning algorithms. The contour detection part is merged with the different path planning algorithms to optimize the robot traversing and benefit it in producing good results. Obstacle-free optimized path is one of the core requirements for robots in any application. With the help of path planning algorithms, robots are enabled to derive those paths in a specific environment. The presence of an obstacle makes it difficult for any path planning algorithms to derive a smooth path. The purpose of using the snake algorithm is to detect an obstacle early. This method not only perceives the obstacle but also catches out the complete boundary of the obstacle, it, thus, provides the details of obstacle coordinates to the path planning algorithm. Conceiving the complete periphery of obstacles can have multiple advantages in many application areas. A*, PRM, RRT, and RRT Smooth algorithms are considered along with the snake algorithm to validate our work in three different experimental scenarios: Maze, Random Obstacles, and Dense case. Path length, Time-taken, and Move count are parameters taken to observe the results. The result obtained using the snake algorithm with four path planning algorithms is analyzed and compared in detail with the core A*, PRM, RRT, and RRTS. Finally, the result obtained using the proposed methodology gives some encouraging results and also predicts the exploration of the robot’s path planning for more applications and fields.