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Urban search and rescue robots are playing an increasingly important role during disasters and with their ability to search within hazardous and dangerous environments to assist the first respond teams. Surveying and remote sensing the hazardous areas are two of the urgent needs of the rescue team to identify the risks before the intervention of the emergency teams. With such time-critical missions, the path planning and autonomous navigation of the robot is one of the primary concerns due to the need of fast and feasible path that is comprehensive enough to assess the associated risks. This paper presents a path planning method for navigating an unmanned ground vehicle within in an indoor hazardous area with minimum priori information. The algorithm can be generalized to any given map and is based on probabilistic roadmap path planning method with spiral dynamics optimization algorithm to obtain the optimal navigating path. Simulations of the algorithm are presented in this paper, and the results promising results are illustrated using Matlab and Simulink simulation environments.

Urban search and rescue (USR) are time-critical missions, failing to find and rescue the victims in time within the hazardous areas may lead to a tragedy. Search and rescue robots have been proven to be useful in disasters such as hurricanes, volcanos, collapses and earth quakes [

Surveying and remote sensing in an urban hazardous indoor area is a challenging problem due to the restricted accessibility for personnel and robots. Indoor navigation is difficult for robots due to the loss of GPS signals and would require a localization system to position the robot within the indoor environment [

This paper presents an approach for optimal path planning for a remote sensing autonomous robot in a cluttered and hazardous indoor environment. The operating scenario of this robot is applicable during the search and rescue missions, where unmanned ground vehicles (UGVs) are favored, to survey and sense the environment for various detectable phenomena such as gases, fire or smoke detection and etcetera. The proposed algorithm can be generalized to any given map and as an example simulation scenario; we present an application of path planning of a mobile robot in an urban search and rescue mission to navigate through an indoor hazardous building for remote-sensing and assessment of the hazardous situation. The sensing of the environment would enable the first responders’ team to determine the severity of the emergency and would help them to decide on rescuing the victims with the least risk towards the team. With the proposed system, the robot will navigate autonomously by utilizing probabilistic roadmaps (PRM) to find out all the possible navigation paths for autonomous navigation of the robot, given the building map. With the various solutions of the probabilistic roadmaps, an optimal path that would be selected, based on particle swarm optimization algorithm, that covers most of the indoor area to provide the best possible assessment of the hazard situation.

Probabilistic roadmap (PRM) methods have been known for their efficient approach in path planning complex motions for a wide discipline of applications including various types of robots such as manipulators, unmanned robotic vehicles as well as predicting the motion and transitions of biological systems such as proteins and molecules [

PRM is based on representing an approximation of the free space (F) in a sample-based approach that is referred to as a configuration space and is composed of nodes and local paths or segments. The approximation of the free space is based on a probability measure to avoid computing an exact shape of the free space, thus the “probabilistic” term of the method. PRM algorithm is based on two steps; roadmap construction and the roadmap query. The algorithm requires start and goal points to calculate collision-free paths from the constructed representation of the free space. The basic PRM pseudocode used in this research is presented in

With its simple pseudo code, the PRM algorithm could calculate many feasible paths in any given map. However, with the limited and constrained time of the rescue missions, an optimal path among all the calculated paths would be needed within a fast and reliable time to facilitate the given tasks of surveying or remote-sensing of the environment. Thus, a fast optimization algorithm would be needed to obtain an optimal comprehensive path with the given constraints.

Spiral dynamics optimization algorithm (SDA) has been inspired by the common feature of the logarithmic spirals found in nature such as whirling currents and was introduced by Tamura and Yasuda [

The strength of the algorithm relies in the diversification and intensification of the search stages that mimics the whirling current spiral where the diversification covers a wider area of search and the intensification improves the cost accuracy around good solutions. With its fast convergence towards optimal cost

PRM Pseudocode |
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1: Generate a set of n configurations in free space S from some distribution 2: Let G = f, ethe empty graph on S. 3: for each configuration s_{i}ÎS do 4: Find k neighbors for s_{i}, N_{k}(s_{i}) 5: for each configuration s_{i}ÎN_{k}(s_{i}) do 6: if j > i and the local planner can find a collision-free path from s_{i} to s_{j} 7: then 8: add an edge (i,j) in G. 9: end if 10: end for 11: end for 12: return the graph G and the set S. |

functions, SDA would enhance the search for optimal path among all the calculated PRM paths and would result in the optimal navigation path. The nomenclature of the SDA optimization is presented in

The rotation matrix for the n-dimension SDA algorithm is defined as

i j R i , j n ( θ i , j ) = i j [ 1 ⋱ 1 cos θ i , j ⋯ − sin θ i , j 1 ⋱ 1 sin θ i , j ⋯ cos θ i , j 1 ⋱ 1 ] (1)

The n-dimension spiral dynamic model is expressed using the rotational matrix as:

x i ( k + 1 ) = S n ( r , θ ) x i ( k ) − ( S n ( r , θ ) − I n ) x ∗ (2)

where

S n ( r , θ ) x ( k ) = ∏ i = 1 n − 1 ( ∏ j = 1 i R n − i , n + 1 − j n ( θ n − i , n + 1 − j ) ) (3)

The simple structure both the PRM and SDA algorithms and would enable having an onboard processing unit to calculate the optimal path online. In the upcoming section, we demonstrate the feasibility of the proposed algorithm over a given operating scenario that involves an indoor cluttered map with obstacles.

The simulation scenario assumed in this paper is to have the robot navigate through some hazardous chemical laboratories where a gas leak has been detected.

Parameter | Description |
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θ | Rotation angle, 0 ≤ θ ≤ 2 π |

k m a x | Maximum iteration number. |

r | Convergence rate of distance between a point and the origin, 0 ≤ r ≤ 1 |

R i , j | Rotation matrix between xi ? xj planes |

m | Dimension of the search space |

SDA Pseudocode |
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Step 0: Preparation Select the number of search points m ≥ 2 , parameters 0 ≤ θ < 2 π , 0 < r < 1 of S n ( r , θ ) , and maximum number of iterations k m a x Set k = 0 . Step 1: Initialization Set initial points x i ( 0 ) ∈ R n , i = 1 , 2 , ⋯ m in the feasible region at random and centre x ∗ as x ∗ = x i g ( 0 ) , i g = arg min i f ( x i ( 0 ) ) , i = 1 , 2 , ⋯ , m . Step 2: Updating x_{i} x i ( k + 1 ) = S n ( r , θ ) x i ( k ) − ( S n ( r , θ ) − I n ) x ∗ i = 1 , 2 , ⋯ m . Step 3: Updating x ∗ x ∗ = x i g ( k + 1 ) , i g = arg min i f ( x i ( k + 1 ) ) , i = 1 , 2 , ⋯ , m . Step 4: Checking termination criterion If k = k max then terminate. Otherwise set k = k + 1 , and return to step 2 |

and autonomously navigate throughout the building corridors to reach the exit at the restricted loading area as in

To start simulating the PRM algorithm, the building map need to be converted into binary occupancy grid matrices in order to define the area constraints for the algorithm.

Simulations start with the SDA optimization algorithm to find out the best navigating path where the robot can record most of the sensory data throughout

the corridors until reaching the end of the path. The objective function is defined to have the best connection distance between the PRM nodes.

Parameter | Lower limit | Upper limit |
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Number of nodes | 80 | 700 |

Connection distance | 50 | 200 |

Parameter | Value |
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P | 15 |

R | 0.95 |

θ | π 4 |

Initial points | 10 |

Iterations | 250 |

The SDA has converged to the optimum number of nodes of 419 within 250 iterations as shown in

The optimal path is therefore presented in

Therefore, leading to more precise sensory measurements for the responders’ team.

This paper set out with the aim of providing a fast and reliable optimal path planning algorithm for a remote-sensing unmanned ground vehicle in an indoor hazardous environment. A proposed path planning algorithm consisting of PRM and SDA optimization algorithm has been presented. The previous section has

illustrated the feasibility of the proposed algorithm and has demonstrated promising results in a simulation scenario of a hazardous indoor environment.

These simulation results provide further support to be tested experimentally on an unmanned ground vehicle with an onboard processing unit and remote sensing equipment to get an insight how the complexity of a given map would affect the online processing time as well as for further verification of the algorithm. Further experimental studies of the algorithm are therefore recommended.

The authors declare no conflicts of interest regarding the publication of this paper.

Alenezi, M.R. and Almeshal, A.M. (2018) Optimal Path Planning for a Remote Sensing Unmanned Ground Vehicle in a Hazardous Indoor Environment. Intelligent Control and Automation, 9, 147-157. https://doi.org/10.4236/ica.2018.94011