  In this paper, we study path planning algorithms of resource constrained
mobile agents in unknown cluttered environments, which include but are not
limited to various terrestrial missions e.g., search and rescue missions by
drones in jungles, and space missions e.g., navigation of rovers on the Moon.
In particular, we focus our attention on artificial potential field (APF) based
methods, in which the target is attractive while the obstacles are repulsive to
the mobile agent. In this paper, we propose two major updates to the classical
APF algorithm which significantly improve the performance of path planning
using APF. First, we propose to improve an existing classical method that
replaces the gradient descent optimization of the potential field cost function
on a continuous domain with a combinatorial optimization on a set of predefined
points (called bacteria points) around the agent's current location. Our
proposition includes an adaptive hyperparameter that changes the value of the
potential function associated to each bacteria point based on the current
environmental measurements. Our proposed solution improves the navigation
performance in terms of convergence to the target at the expense of minimal
increase in computational complexity. Second, we propose an improved potential
field cost function of the bacteria points by introducing a new branching cost
function which further improves the navigation performance. The algorithms were
tested on a set of Monte Carlo simulation trials where the environment changes
for each trial. Our simulation results show 25% lower navigation time and
around 300% higher success rate compared to the conventional potential field
method, and we present future directions for research.
