Adaptive Flower Pollination Algorithm-Based Energy Efficient Routing Protocol for Multi-Robot Systems

The exploration and mapping of unknown environments, where the reliable exchange of data between the robots and the base station (BS) also plays a pivotal role, are some of the fundamental problems of mobile robotics. The maximum energy of a robot is utilized for navigation and communication. The communication between the robots and the BS is limited by the transmission range and the battery capacity. This situation inflicts constraints while designing an effective communication strategy for a multi-robot system (MRS). The biggest challenge lies in designing a unified framework for navigation and communication of the robots. The underlying notion is to utilize the minimum energy for communication (without limiting the range/efficiency of communication) to ensure that the maximum energy can be used for navigation (for larger area coverage). In this work, we present a communication strategy by using adaptive flower pollination optimization algorithm for MRS in conjunction with simultaneous localization and mapping (SLAM) technique for navigation and map making. The proposed strategy has been compared with multiple routing algorithms in terms of network life time and energy efficiency. The proposed strategy performs 4% better compared with harmony search algorithm (HSA) and approximately 10% better compared with distance aware residual energy-efficient stable election protocol (DARE-SEP) in terms of the total network lifetime when 50% of robots are alive. The performance drastically improves by 20% till the last robot is alive compared with HSA and approximately 26% compared with DARE-SEP. Hence, the energy saved during communication with the utilization of proposed strategy helps the robots explore more areas, which ultimately elevates the efficacy of the whole system

Map Making in Social Indoor Environment Through Robot Navigation Using Active SLAM

Robotics has come a long way from industrial robotic arms and is all set to enter our homes. The capability of a robot to navigate in an unknown human populated environment with obstacles and making map simultaneously is one of the significant characteristics in the domain of autonomous robotics. Further, the problem of robot navigating in a social environment while ensuring human safety and comfort through social norms needs to be addressed. This article presents a solution for mapping of unknown terrains with dynamic obstacles using simultaneous localization in social environments through Adaptive Squashing Function based artificial neural network training, which is able to track the target orientation angles more efficiently as compared to conventional fixed slope squashing function based backpropagation training algorithm. The performance of different state of the art techniques have been compared with proposed work through simulation models. Simulation results demonstrated the effectiveness of the proposed algorithm in complex environment where the proposed algorithm converged in less than 50% of the iterations taken by the exhaustive search algorithms and approximately 33% of the iterations taken by random search algorithm. Further, the proposed approach was tested in the real-world settings, wherein the robot was deployed to create map for the Kalpana Chawla Center for Research in Space Science and Technology, Chandigarh University with mobile humans