Autonomous robotics plays a pivotal role to simplify humanmachine interaction while meeting the current industrial demands. In that process, machine intelligence plays a dominant role during the decision making in the operational state-space. Primarily, this decision making and control mechanism relies on sensing and actuation. Simultaneous localization and mapping (SLAM) is the most advanced technique that facilitates both sensing and actuation to achieve autonomy for robots. This work aims to collate multi-dimensional aspects of simultaneous localization and mapping techniques primarily in the purview of both deterministic and probabilistic frameworks. This investigation on SLAM classification is further elaborated into different categories such as Feature-based SLAM and Optimization based SLAM. In this work, the chronological evolution of SLAM technique develops a comprehensive understanding among the concerned research community.
The framework of reinforcement learning-based optimal control depends on a mathematical formulation of intelligent decision making. In this article, we demonstrated the comprehensive design framework for offline reinforcement learning algorithms that utilizes sparse and discrete data space for efficient decisionmaking purposes. Learning is often difficult with the sparse reward function under the absence of optimization. Hence, an optimized map can be used in the reward function to improve efficacy. Some reward functions outperform sparse reward, such as "map completeness" and "information gain". "Map completeness" is proportional to the difference between the current time step and the previous time step, while "information gain" utilizes the entropy information for measuring the uncertainty in the map. On a whole. this article proposes a framework for the development of optimized reward function-based reinforcement learning based control strategy.
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