A Rescue Robot Path Planning Based on Ant Colony Optimization Algorithm

Zhang, Xiaoyong; Wu, Min; Peng, Jun; Jiang, Fu

doi:10.1109/itcs.2009.177

Cited by 4 publications

(4 citation statements)

References 4 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the sensors can detect the change of environmental information at any time, and the change has no influence on obstacle avoidance path planning. From Figure 2, the rolling window view is the dashed circle including the detected effective information of nine grids (g=13, 14,15,21,22,23,29,30,31).…”

Section: Problem Formulationmentioning

confidence: 99%

“…To solve the problem of path planning, some modern intelligent path planning algorithms, such as genetic algorithm (GA) [5][6][7], simulated annealing (SA) [8,9], neural network (NN) [10][11][12], particle swarm optimization (PSO) [13,14], and ant colony optimization (ACO) [15][16][17][18], are adopted. Among those approaches, the ACO is the only meta-heuristic approach inspired by the behaviour of the biological ants in real world.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Route Optimization for Autonomous Container Truck Based on Rolling Window

Huang

Zheng

2016

International Journal of Advanced Robotic Systems

View full text Add to dashboard Buy / Rent full text

Route optimization for autonomous container truck is one of the key problems to realize the automatic container port. An environment model for container truck is built by grid method. Considering the complex and unknown construction environment of the container port, an improved ant colony optimization (IACO) algorithm based on rolling window is proposed to achieve path planning for container truck. In the simulations, it is obvious that the IACO will not only achieve a safe collision avoidance path, the length of which is shorter than the truck's traditional route no matter how complex the environment is, but also show good analytical and disposing ability of dead ends in the path planning process. Compared with conventional ant colony optimization (ACO), the running time of IACO is shorter. The results of the simulation experiments demonstrate that the IACO is a good method which is applicable to route optimization for autonomous container truck.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Route Optimization for Autonomous Container Truck Based on Rolling Window

Huang

Zheng

2016

International Journal of Advanced Robotic Systems

View full text Add to dashboard Buy / Rent full text

show abstract

“…In [28], Zhang et al proposed an improved ACO algorithm. The key differences with the traditional ACO algorithm are (1) the definition of an objective attraction function in the transition rule probability which is based on the attraction of the goal position.…”

Section: Related Workmentioning

confidence: 99%

SmartPATH: An Efficient Hybrid ACO-GA Algorithm for Solving the Global Path Planning Problem of Mobile Robots

Châari

Koubaa

Trigui

et al. 2014

International Journal of Advanced Robotic Systems

View full text Add to dashboard Buy / Rent full text

Path planning is a fundamental optimization problem that is crucial for the navigation of a mobile robot. Among the vast array of optimization approaches, we focus in this paper on Ant Colony Optimization (ACO) and Genetic Algorithms (GA) for solving the global path planning problem in a static environment, considering their effectiveness in solving such a problem. Our objective is to design an efficient hybrid algorithm that takes profit of the advantages of both ACO and GA approaches for the sake of maximizing the chance to find the optimal path even under real-time constraints. In this paper, we present smartPATH, a new hybrid ACO-GA algorithm that relies on the combination of an improved ACO algorithm (IACO) for efficient and fast path selection, and a modified crossover operator to reduce the risk of falling into a local minimum. We demonstrate through extensive simulations that smartPATH outperforms classical ACO (CACO), GA algorithms. It also outperforms the Dijkstra exact method in solving the path planning problem for large graph environments. It improves the solution quality up to 57% in comparison with CACO and reduces the execution time up to 83% as compared to Dijkstra for large and dense graphs. In addition, the experimental results on a real robot shows that smartPATH finds the optimal path with a probability up to 80% with a small gap not exceeding 1m in 98%.

show abstract

“…Aiming at the rescue problem in mines, Tian et al developed a modified neural network to expand the Kalman filter and realized a method of robot path planning based on multisensor fusion [20]. Zhang et al employed a modified ant colony algorithm (ACO) to the problem of rescue path planning [21]. Basilico and Amigoni introduced a strategy based on a multicriterion decision to solve the problem of robot path planning [22].…”

Section: Related Workmentioning

confidence: 99%

PSO-Based Robot Path Planning for Multisurvivor Rescue in Limited Survival Time

Geng

Gong

Zhang

2014

Mathematical Problems in Engineering

View full text Add to dashboard Buy / Rent full text

Since the strength of a trapped person often declines with time in urgent and dangerous circumstances, adopting a robot to rescue as many survivors as possible in limited time is of considerable significance. However, as one key issue in robot navigation, how to plan an optimal rescue path of a robot has not yet been fully solved. This paper studies robot path planning for multisurvivor rescue in limited survival time using a representative heuristic, particle swarm optimization (PSO). First, the robot path planning problem including multiple survivors is formulated as a discrete optimization one with high constraint, where the number of rescued persons is taken as the unique objective function, and the strength of a trapped person is used to constrain the feasibility of a path. Then, a new integer PSO algorithm is presented to solve the mathematical model, and several new operations, such as the update of a particle, the insertion and inversion operators, and the rapidly local search method, are incorporated into the proposed algorithm to improve its effectiveness. Finally, the simulation results demonstrate the capacity of our method in generating optimal paths with high quality.

show abstract

A Rescue Robot Path Planning Based on Ant Colony Optimization Algorithm

Cited by 4 publications

References 4 publications

Route Optimization for Autonomous Container Truck Based on Rolling Window

Route Optimization for Autonomous Container Truck Based on Rolling Window

SmartPATH: An Efficient Hybrid ACO-GA Algorithm for Solving the Global Path Planning Problem of Mobile Robots

PSO-Based Robot Path Planning for Multisurvivor Rescue in Limited Survival Time

Contact Info

Product

Resources

About