Dynamic area approximation based stochastic multi-UAV path planner.

This work presents a novel approach to effectively search for a target using a multi-robot system. The proposed approach augments the conventional swarm-based stochastic search algorithms by dynamically refining the search space to locate the source. Compared to other search algorithms, including PSO, Cuckoo Search Algorithm, Bat Algorithm, Glowworm Swarm Optimization and Random Walk, our algorithm reduces the time taken, and the amount of exploration done is much more succinct. Similarly, the algorithm makes no concessions in terms of success rate. In extreme scenarios, when the number of particles is fewer than five, the search space is enormous, or the search space is unbounded or noise in the sensor readings, our method stands out and performs far better than its competitors.

Particle Swarm Optimization

For a swarm of $n$ particles, each particle $i$ has position $x_i \in \mathbb{R}^d$ and velocity $v_i \in \mathbb{R}^d$. The update equations are:

$\begin{aligned} v_i(t+1) &= \omega v_i(t) + c_1r_1(p_i - x_i(t)) + c_2r_2(g - x_i(t))
x_i(t+1) &= x_i(t) + v_i(t+1) \end{aligned}$

where:

  • $\omega$ is the inertia weight
  • $c_1, c_2$ are acceleration coefficients
  • $r_1, r_2$ are random numbers in $[0,1]$
  • $p_i$ is the particle’s best position
  • $g$ is the global best position

All possible explored paths (left) and final chosen path (right)

Read the paper here: https://ieeexplore.ieee.org/abstract/document/10557107