Autonomous search of an airborne release in urban environments using informed tree planning

Abstract

The use of autonomous vehicles for source localisation is a key enabling tool for disaster response teams to safely and efficiently deal with chemical emergencies. Whilst much work has been performed on source localisation using autonomous systems, most previous works have assumed an open environment or employed simplistic obstacle avoidance, separate from the estimation procedure. In this paper, we explore the coupling of the path planning task for both source term estimation and obstacle avoidance in an adaptive framework. The proposed system intelligently produces potential gas sampling locations that will reliably inform the estimation engine by not sampling in the wake of buildings as frequently. Then a tree search is performed to generate paths toward the estimated source location that traverse around any obstacles and still allow for exploration of potentially superior sampling locations.The proposed informed tree planning algorithm is then tested against the standard Entrotaxis and Entrotaxis-Jump techniques in a series of high fidelity simulations. The proposed system is found to reduce source estimation error far more efficiently than its competitors in a feature rich environment, whilst also exhibiting vastly more consistent and robust results.

Scalable probabilistic gas distribution mapping using Gaussian belief propagation

“…The staging for the scheduling tests is the DAPPLE experiment [23], an experimentally validated CFD simulation of a gas release under steady wind in urban London. This scenario has all the properties that we wish to address with GaBP since it is large in scale, structurally complex and models a complicated plume structure which cannot be easily mapped using simple dispersion models (as shown in our previous work [24]).…”

Gas source localization and mapping with mobile robots: A review

“…A limitation of current probabilistic algorithms can be found in the ATD models that they use, which often do not fully represent the real plume dispersal, especially in cluttered environments. There have however been attempts to consider the effect of obstacles on plume dispersal by combining path planning strategies with probabilistic GSL algorithms (Rhodes et al, 2021). However, there are few examples where such algorithms have been evaluated on real robots.…”

“…More recently, Rhodes et al (2021) proposed a 2D STE search strategy using Informed Tree Planning (ITP) to generate obstacle free trajectories between the robot and source location using a known map of the search space. Considering how obstacles affect the distribution of the plume, Dijkstra's search was used to produce a sample distribution where regions behind obstacles are less likely to contain chemical information due to the blocking effect on the particle's flow within the wind direction (Bellingham et al, 2002).…”

Detection and Estimation of Gas Sources With Arbitrary Locations Based on Poisson's Equation

“…Particularly appealing are Bayesian approaches for parameter estimation [17]. These approaches permit accounting for uncertainties pertaining to source parameters [18], [19], [20], accommodating different models [18], [19], [20] and unknown environments [21], [22]. Furthermore, the Bayesian framework facilitates the adoption of infotaxis-based strategies [23] for autonomous robotic navigation, thereby enhancing GSL and directing robots toward sources through distinct information-theoretic criteria (see e.g., [23], [24], [25]).…”