Path Planning for Robotic Demining: Robust Sensor-Based Coverage of Unstructured Environments and Probabilistic Methods

Abstract

Demining and unexploded ordnance (UXO) clearance are extremely tedious and dangerous tasks. The use of robots bypasses the hazards and potentially increases the efficiency of both tasks. A first crucial step towards robotic mine/UXO clearance is to locate all the targets. This requires a path planner that generates a path to pass a detector over all points of a mine/UXO field, i.e., a planner that is complete. The current state of the art in path planning for mine/UXO clearance is to move a robot randomly or use simple heuristics. These methods do not possess completeness guarantees which are vital for locating all of the mines/UXOs. Using such random approaches is akin to intentionally using imperfect detectors. In this paper, we first overview our prior complete coverage algorithm and compare it with randomized approaches. In addition to the provable guarantees, we demonstrate that complete coverage achieves coverage in shorter time than random coverage. We also show that the use of complete approaches enables the creation of a filter to reject bad sensor readings, which is necessary for successful deployment of robots. We propose a new approach to handle sensor uncertainty that uses geometrical and topological features rather than sensor uncertainty models. We have verified our results by performing experiments in unstructured indoor environments. Finally, for scenarios where some a priori information about a minefield is available, we expedite the demining process by introducing a probabilistic method so that a demining robot does not have to perform exhaustive coverage.

A Cooperative Switching Algorithm for Multi-Agent Foraging

“…The pheromone evaporates according to a specific rate. The total amount of pheromone that evaporates at the position i in time t is modeled as in Equation 1.…”

“…Multi-Agent Systems are a suitable approach to develop many multi-robot distributed applications such as: mine detecting [1] [2], search in damaged buildings [3] [4], fire fighting [5], and exploration of spaces [6] [7], where neither a map, nor a Global Switching Algorithm for Foraging (NC-SAF) which is the non cooperative version of C-SAF, c-marking which is a cooperative multi-agent foraging algorithm inspired from ant systems, based on the Artificial Potential Field (APF) and uses pseudo random walk for search task; and Non-Cooperative c-marking (NC-c-marking) which is the non cooperative version of the c-marking algorithm, is presented in Section 4. In Section 5, we present the proposed foraging framework.…”

Towards tracking a row of discrete targets with AUVs using Markov Decision Processes and probabilistic techniques

“…Acar and Choset [4] developed probabilistic techniques with which to predict the spacing of a field of discrete targets based upon limited sample data. However, they did not use the estimated field to develope control laws to govern the search actions of a robot.…”

A distributed bio-inspired algorithm for search of moving targets in three dimensional spaces

“…Random search is just opposite to the determined search in that no predefined information is available about the location of targets. In environments where the distribution of targets is unknown a priori or changes over time randomized search strategies are more effective [11], [12]. Viswanathan et al [13] discovered that the foraging behaviour of animals follows a certain random search strategy that is an optimal Markovian search strategy based on Levy processes.…”