Integration of acoustic compliance and noise mitigation in path planning for drones in human-robot collaborative environments

This work presents a framework aimed at mitigating adverse effects of high-amplitude drone noise ranging from hearing loss to reduced productivity in human-robot collaborative environments by infusing acoustic awareness in a path planning algorithm without imposing any additional design layers or hardware to an operational drone. Following a detailed outline of the proposed approach, it is shown that a significant reduction of noise levels perceived by human workers at noise-sensitive locations is realized via a path planner which generates optimal paths ranging from quietest to shortest paths. The approach is then augmented with a path-correction mechanism which accounts for noise exposure duration to ensure the aforementioned optimal paths are compliant with a given industrial/environmental standard. The correction mechanism enforces an adjustment of subsets of the planned paths inside quiet zones designated around noise-sensitive locations. The presented concepts were verified using numerical simulations conducted for a 2-dimensional rasterized obstacle field followed by a statistical design of experiments. The proposed framework is highly versatile and integrable with widely used industrial path planners, rendering it a highly valuable tool for noisy collaborative workplaces.

Automated summary

This work presents a framework for mitigating the adverse effects of high-amplitude drone noise in human-robot collaborative environments by incorporating acoustic awareness into a path planning algorithm. The proposed approach reduces noise levels perceived by human workers and ensures compliance with industrial/environmental standards through the generation of optimal paths and a path-correction mechanism. Numerical simulations were conducted to verify the presented concepts.