The necessity of the soft gripping devices is increasing day-by-day in medical robotics especially when safe, gentle motions and soft touch are necessary. In this paper, a novel asymmetric bellow flexible pneumatic actuator (AFPA) has been designed and fabricated to construct a miniaturised soft gripper that could be used to grip small objects. The model of AFPA is designed using solid works and its bending motion is simulated in Abaqus software for optimisation and compared with experimental results. The actuator is fabricated using compression molding process that includes micromachining of the molds. Experiments conducted show the bending characteristics of the actuator at different pressures. The actuator shows excellent bending performance and the eccentricity in its design supports increased bending or curling motion up to a certain extent compared to normal bellows without eccentricity. The effects of profile shape and eccentricity on the actuator performance are analysed and the results are presented.

In the area of Robotics, the gripper plays a very important role as it is required to hold and place the object at the desired location. The requirements of gripper in terms of load capacity, and flexibility to adapt to the form of the object with tactile sensing capability which suit the strength of the object are necessary. Extensive research work is under way in the design of soft gripper or dexterous hand. An exhaustive survey of all such grippers conveys the idea of higher and higher sophistication with innumerable components and elaborate controls with programmable ability has been the outcome of research. Flexible micro actuators (FMA) proposed by earlier researchers are having two or more internal chambers and the internal pressure of each are controlled independently through flexible tubes which are connected to pressure control valves. The proposed actuator has a single internal chamber and is simple, compact and easy to manufacture. In this paper, a flexible microactuator (FMA) driven by a pneumatic/ hydraulic system with single internal chamber has been developed for robotic soft gripper. By proper selection and manufacturing of the asymmetric tube flexible actuator with reinforcement, a versatile dexterous hand can be fabricated which is suited for dynamic application closely approximating to the human hand. The present work has paved the way for extensive research on this innovative technique as it holds out the true potential for innumerable and very interesting application in various areas such as micro robots, pipeline inspection robots, underwater robots and walking robots.

Uncertainty remains a central challenge in robotic navigation, exploration, and coordination. This paper examines how Partially Observable Markov Decision Processes (POMDPs) and their decentralized variants (Dec-POMDPs) provide a rigorous foundation for decision-making under partial observability across tasks such as Active Simultaneous Localization and Mapping (A-SLAM), adaptive informative path planning, and multi-robot coordination. We review recent advances that integrate deep reinforcement learning (DRL) with POMDP formulations, highlighting improvements in scalability and adaptability as well as unresolved challenges of robustness, interpretability, and sim-to-real transfer. To complement learning-driven methods, we discuss emerging strategies that embed probabilistic reasoning directly into navigation, including belief-space planning, distributionally robust control formulations, and probabilistic graph models such as enhanced probabilistic roadmaps (PRMs) and Canadian Traveler Problem-based roadmaps. These approaches collectively demonstrate that uncertainty can be managed more effectively by coupling structured inference with data-driven adaptation. The survey concludes by outlining future research directions, emphasizing hybrid learning–planning architectures, neuro-symbolic reasoning, and socially aware navigation frameworks as critical steps toward resilient, transparent, and human-centered autonomy.