Multi-Objective Exploration Strategies for Search and Rescue Robots

Multi-objective exploration and search, constitutes one of the most challenging tasks for autonomous robots performing in rescue operations. However, the efficiency of the exploration depends heavily on the constraints that such operations introduce. In this work, we model a set of typical search and rescue constraints, such as limited-bandwidth communications and time-critical goals, in a strategy that can be adapted in several exploration scenarios. The proposed approach is applied in a case of a first-responder robot, which goal is to generate a sufficiently accurate map of the environment for reporting target location and possible traversable paths, in a strictly defined time window. Since the maximization of the area to be explored under multiple constraints and objectives, is a non-convex optimization problem, we utilize the Cognitive-based Adaptive Optimization (CAO) algorithm.

The desired robot multi-objective scenario will look as follows: “Given an initial unexplored map, your initial position and a certain time window, try to find the subject around a target position. If you find the subject, explore as much of the target region as you can and then return to the exit. If you did not find the subject, resume the search. When returning to exit, explore as much of the unexplored region map. In any case, do not lose your communication link”

The explored area in di
erent time steps T

In this work we use 2 different cost functions:

(1)   \begin{eqnarray*} CF_1 &=& \frac{\left({w_{1}F_1 + w_{2}F_2 + w_{3}F_3} \right)}{1 + e^{(\left| {D(\textbf{R},\textbf{H})} \right| - C_r)}} \end{eqnarray*}

(2)   \begin{eqnarray*} CF_2 &=& \frac{\left({w_{1}F_1 + w_{3}F_4 } \right)}{1 + e^{(\left| {D(\textbf{R},\textbf{H})} \right| - C_r)}} \end{eqnarray*}

More details regarding these cost functions are given in [1]. We have also considered the physical constraints which apply in the aforementioned case, which include the following:

  • the robot remains within the terrains limits, i.e. within
    [x_{min}, x_{max}] and [y_{min}, y_{max}] in the x- and y-axes, respectively;
  • the robot do not approach the obstacles closer than a minimum allowable safety distance dr.
  •  the robot can move only towards to a fully estimated and within the line of sight position
Related Publication(s):

A. Amanatiadis, S. A. Chatzichristofis, K. Charalampous, L. Doitsidis, E. B. Kosmatopoulos, P. Tsalides, A. Gasteratos and S. Roumeliotis, “A MULTI-OBJECTIVE EXPLORATION STRATEGY FOR MOBILE ROBOTS UNDER OPERATIONAL CONSTRAINTS”, «IEEE Access», Volume 1, October 2013, pp 691–702, IEEE. [bib][pdf]

 

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