Design Optimization of Sparse Sensing Array for Extended Aerial Robot Navigation in Deep Hazardous Tunnels

Abstract

In this letter, the design optimization and localization with sparse sensing array of range-based sensors are presented. This optimization focuses on an optimized sparse sensing array for localization and autonomous navigation in tunnel environments with right rectangular prism geometry and the autonomous ascent and descent in vertical cylindrical shafts. An optimization problem is conceived to optimize sensor configuration to traverse both tunnels and shafts with constraints from the field of the view of the camera system and the mechanical layout. The optimized sensor configuration solved using a genetic algorithm performed on average 21 times better than configurations that were placed heuristically. The optimized design and the localization algorithm are implemented on SWIRL: Surveyor with intelligent rotating lens. The proposed localization method with sparse sensing results in at least four-fold reductions in the weight and eight-fold reduction in power consumption of the sensing system compared to existing ones. SWIRL was autonomously flown in a series of experiments, consisting of tests in shaft and tunnels in both indoor mock-up and field environment. Last, in an endurance test in a mock-up shaft, SWIRL achieved over 35 min of autonomous flight using the proposed sensing placement and scheme, making extended aerial robot navigation and inspection of deep hazardous tunnels possible.