Groups of interacting active particles, insects, or humans can form clusters that hinder the goals of the collective; therefore, development of robust strategies for control of such clogs is essential, particularly in confined environments. Our biological and robophysical excavation experiments, supported by computational and theoretical models, reveal that digging performance can be robustly optimized within the constraints of narrow tunnels by individual idleness and retreating. Tools from the study of dense particulate ensembles elucidate how idleness reduces the frequency of flow-stopping clogs and how selective retreating reduces cluster dissolution time for the rare clusters that still occur. Our results point to strategies by which dense active matter and swarms can become task capable without sophisticated sensing, planning, and global control of the collective.
At the macroscale, controlling robotic swarms typically uses substantial memory, processing power, and coordination unavailable at the microscale, e.g., for colloidal robots, which could be useful for fighting disease, fabricating intelligent textiles, and designing nanocomputers. To develop principles that can leverage physical interactions and thus be used across scales, we take a two-pronged approach: a theoretical abstraction of self-organizing particle systems and an experimental robot system of active cohesive granular matter that intentionally lacks digital electronic computation and communication, using minimal (or no) sensing and control. As predicted by theory, as interparticle attraction increases, the collective transitions from dispersed to a compact phase. When aggregated, the collective can transport non-robot “impurities,” thus performing an emergent task driven by the physics underlying the transition. These results reveal a fruitful interplay between algorithm design and active matter robophysics that can result in principles for programming collectives without the need for complex algorithms or capabilities.
With the advancements in wireless technology, wireless augmented reality have seen a growth in the recent years and their applications have magnified tremendously. Also the concept of vehicular Ad-hoc networks (VANET) and its applications have aggrandized the field of traffic and navigation in a similar way. This paper basically proposes the integration of both these technologies in the creation of a smarter and safer road traffic system. The review of the current and previous researches in these fields provides the design outline for the proposed theory.
Over the years, a lot has been done in the field of medical technology to assist the elderly and physically challenged people. One such development is the robotic wheelchair. Many designs of the same have been proposed throughout the recent past. But such models still face difficulties in covering difficult terrains like steps, etc. We propose a design of wheelchair which will be able to mimic easy locomotion techniques available in nature. For example: serpentine locomotion, aquatic locomotion, etc. Various types of robots mimicking such movements have been made in the past. We suggest the inclusion of such robotic locomotors in the already existing robotic wheelchair designs to assist more efficient movement. A peep into the previous researches done in this field lays the foundation of our proposed design.
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