Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms

Abstract

With the growing reliability of modern ad hoc networks, it is encouraging to analyze the potential involvement of autonomous ad hoc agents in critical situations where human involvement could be perilous. One such critical scenario is the Search and Rescue effort in the event of a disaster, in which timely discovery and help deployment is of utmost importance. This paper demonstrates the applicability of a bio-inspired technique, namely Ant Algorithms (AA), in optimizing the search time for a route or path to a trapped victim, followed by the application of Dijkstra’s algorithm in the rescue phase. The inherent exploratory nature of AA is put to use for faster mapping and coverage of the unknown search space. Four different AA are implemented, with different effects of the pheromone in play. An inverted AA, with repulsive pheromones, was found to be the best fit for this particular application. After considerable exploration, upon discovery of the victim, the autonomous agents further facilitate the rescue process by forming a relay network, using the already deployed resources. Hence, the paper discusses a detailed decision-making model of the swarm, segmented into two primary phases that are responsible for the search and rescue, respectively. Different aspects of the performance of the agent swarm are analyzed as a function of the spatial dimensions, the complexity of the search space, the deployed search group size, and the signal permeability of the obstacles in the area.

Quicker Path planning of a collaborative dual-arm robot using Modified BP-RRT* algorithm

“…Algorithms like A* [6][10] [17] and Dijkstras [11][20] [25] comes under search-based path planning. The major disadvantages of search-based path planning are that they are computationally very expensive and hence their operation process time is very high.…”

Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot

“…The ACO process is illustrated in Figure 21 Initially applied to solving the Traveling Salesman Problem (TSP) [120], the principles and mathematical models of the ACO algorithm have since been systematically studied and have undergone significant development, such as in [121] with airport AGV route optimization model based on the ant colony algorithm for optimizing Dijkstra's algorithm in urban systems. In [122], a search and rescue is presented in a maze-like environment with ant and Dijkstra algorithms. The work in [123] describes the application of odometry and Dijkstra's algorithm to warehouse mobile robot navigation and shortest path determination.…”

Force-Based Self-Organizing MANET/FANET with a UAV Swarm

“…As advances in wireless technologies continue, the application range for MANETs grows [20,27,28]. The three use-case scenarios provided in Section 2.3 are just some examples of the possibilities where a drone-based MANET can offer significant value by providing, e.g., vital communication links between relief teams, survivors, and external command centers.…”

“…This can be achieved by, e.g., allowing devices to form and manage their own networks as they come into and move out of each other's communication range. In this context, Vehicular Ad Hoc Networks (VANETs) [2,[9][10][11] deserve special attention: with increasing vehicular autonomy, interconnecting the individual vehicles enables them to share information [22,32], to allocate available resources [5,28] and to collaborate when navigating in close proximity. VANETs [9][10][11] rely on the principles of MANETs to communicate among themselves and with other elements of the transport infrastructure.…”