Generally, the purpose of robotics is to make our lives easier by robots carrying out mundane, repetitive, or dangerous tasks instead of us, to which end a team of robotics specialists have devised a flying robot to handle hazardous building projects.
Indeed, new research led by Imperial College London and co-authored by the University of Bristol has revealed that aerial drones could offer various benefits to the safety, sustainability, and scale in the construction industry, per a study published in Science Robotics on April 23.
Specifically, using drones for mid-air material deposition on-site in the process called Aerial Additive Manufacturing (Aerial AM) might efficiently address challenges in global housing and infrastructure, particularly those pertaining to traditional construction methods and ground-based robotic systems.
How aerial drones perform Aerial Additive Manufacturing (Aerial AM)
As it happens, a newly designed autonomy framework for Aerial AM allows these aerial drones to work at greater heights, challenging or inaccessible terrains, dangerous locations, and hard-to-reach areas, overcoming issues like flight coordination, material deposition precision, and scalability in large-scale tasks.
According to Dr. Basaran Bahadir Kocer, co-author from the University of Bristol’s School of Civil, Aerospace, and Design Engineering, “early-stage demonstrations of Aerial AM have already showcased capabilities such as rapid on-demand repairs and modular assembly techniques, paving the way for broader adoption across industries.”
Elsewhere, scientists have developed finger-shaped tactile sensors that allow robots to accurately detect the direction of applied forces and feel the texture of various materials they touch to correctly discern which they are, inspired by human fingertips.
At the same time, others have created a new robotic framework powered by artificial intelligence (AI), which allows robots to learn how to do particular tasks by watching videos of humans performing them, in an approach called RHyME (Retrieval for Hybrid Imitation under Mismatched Execution).
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