scholarly journals Socially Compliant Path Planning for Robotic Autonomous Luggage Trolley Collection at Airports

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2759 ◽  
Author(s):  
Jiankun Wang ◽  
Max Q.-H. Meng
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Dimensional Space ◽  
Optimal Path ◽  
High Dimensional ◽  
Force Model ◽  
Planning Problem ◽  
Self Organizing Map ◽  
Social Force ◽  
The Social

This paper describes a socially compliant path planning scheme for robotic autonomous luggage trolley collection at airports. The robot is required to efficiently collect all assigned luggage trolleys in a designated area, while avoiding obstacles and not offending the pedestrians. This path planning problem is formulated in this paper as a Traveling Salesman Problem (TSP). Different from the conventional solutions to the TSP, in which the Euclidean distance between two sites is used as the metric, a high-dimensional metric including the factor of pedestrians’ feelings is applied in this work. To obtain the new metric, a novel potential function is firstly proposed to model the relationship between the robot, luggage trolleys, obstacles, and pedestrians. The Social Force Model (SFM) is utilized so that the pedestrians can bring extra influence on the potential field, different from ordinary obstacles. Directed by the attractive and repulsive force generated from the potential field, a number of paths connecting the robot and the luggage trolley, or two luggage trolleys, can be obtained. The length of the generated path is considered as the new metric. The Self-Organizing Map (SOM) satisfies the job of finding a final path to connect all luggage trolleys and the robot located in the potential field, as it can find the intrinsic connection in the high dimensional space. Therefore, while incorporating the new metric, the SOM is used to find the optimal path in which the robot can collect the assigned luggage trolleys in sequence. As a demonstration, the proposed path planning method is implemented in simulation experiments, showing an increase of efficiency and efficacy.

Search and Rescue Robot Path Planning in Unknown Environment

2012 ◽  
Vol 241-244 ◽  
pp. 1682-1687 ◽  
Author(s):  
Tao Pang ◽  
Xiao Gang Ruan ◽  
Er Shen Wang ◽  
Rui Yuan Fan
Keyword(s):  
Path Planning ◽  
Learning Algorithm ◽  
Optimal Path ◽  
Local Environment ◽  
Search And Rescue ◽  
Planning Problem ◽  
Self Organizing Map ◽  
Initial State ◽  
Rescue Robot ◽  
Unknown Environment

For the path planning problem of search and rescue robot in unknown environment, a bionic learning algorithm was proposed. The GSOM (Growing Self-organizing Map) algorithm was used to build the environment cognitive map. The heuristic search A* algorithm was used to find the global optimal path from initial state to target state. When the local environment was changed, reinforcement learning algorithm based on sensor information was used to guide the search and rescue robot behavior of local path planning. Simulation results show the method effectiveness.

scholarly journals A New Online Random Particles Optimization Algorithm for Mobile Robot Path Planning in Dynamic Environments

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Behrang Mohajer ◽  
Kourosh Kiani ◽  
Ehsan Samiei ◽  
Mostafa Sharifi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Potential Field ◽  
Optimal Path ◽  
Target Position ◽  
Path Selection ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Robot Path

A new algorithm named random particle optimization algorithm (RPOA) for local path planning problem of mobile robots in dynamic and unknown environments is proposed. The new algorithm inspired from bacterial foraging technique is based on particles which are randomly distributed around a robot. These particles search the optimal path toward the target position while avoiding the moving obstacles by getting help from the robot’s sensors. The criterion of optimal path selection relies on the particles distance to target and Gaussian cost function assign to detected obstacles. Then, a high level decision making strategy will decide to select best mobile robot path among the proceeded particles, and finally a low level decision control provides a control signal for control of considered holonomic mobile robot. This process is implemented without requirement to tuning algorithm or complex calculation, and furthermore, it is independent from gradient base methods such as heuristic (artificial potential field) methods. Therefore, in this paper, the problem of local mobile path planning is free from getting stuck in local minima and is easy computed. To evaluate the proposed algorithm, some simulations in three various scenarios are performed and results are compared by the artificial potential field.

scholarly journals A route navigation algorithm for pedestrian simulation based on grid potential field

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989783
Author(s):  
Minghua Li ◽  
Yun Wei ◽  
Yan Xu
Keyword(s):  
Path Planning ◽  
Simulation Model ◽  
Potential Field ◽  
Force Model ◽  
Social Force ◽  
Complex Scene ◽  
Simulation Scenario ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Pedestrian Simulation

Pedestrian simulation modeling has become an important means to study the dynamic characters of dense populations. In the continuous pedestrian simulation model for complex simulation scenario with obstacles, the pedestrian path planning algorithm is an indispensable component, which is used for the calculation of pedestrian macro path and microscopic movement desired direction. However, there is less efficiency and poor robustness in the existing pedestrian path planning algorithm. To address this issue, we propose a new pedestrian path planning algorithm to solve these problems in this article. In our algorithm, we have two steps to determine pedestrian movement path, that is, the discrete potential fields are first generated by the flood fill algorithm and then the pedestrian desired speeds are determined along the negative gradient direction in the discrete potential field. Combined with the social force model, the proposed algorithm is applied in a corridor, a simple scene, and a complex scene, respectively, to verify its effectiveness and efficiency. The results demonstrate that the proposed pedestrian path planning algorithm in this article can greatly improve the computational efficiency of the continuous pedestrian simulation model, strengthen the robustness of application in complex scenes.

scholarly journals Modeling the Pedestrian Ability of Detecting Lanes and Lane Changing Behavior

2016 ◽  
Vol 10 (7) ◽  
pp. 1
Author(s):  
Mohammed Mahmod Shuaib
Keyword(s):  
Quantitative Measurement ◽  
Force Model ◽  
Social Force ◽  
Lane Changing ◽  
Crowd Dynamics ◽  
Proposed Model ◽  
The Social ◽  
Lane Formation ◽  
Dynamics Models ◽  
The Impact

Incorporating decision-making capability as an intelligence aspect into crowd dynamics models is crucial factor for reproducing realistic pedestrian flow. Crowd dynamics models are still suffering from poor representation of essential behaviors such as lane changing behavior. In this article, we provide the simulated pedestrians in the social force model more intelligence as an extension to the pedestrian’s investigation capability in bidirectional walkways, to let the model appear more representative of what actually happens in reality. In the proposed model, the lane’s structure is modeled as social network. Thereby, the simulated pedestrians with inconvenient walking can detect the available lanes inside his environment, investigate their attractions, and then make decisions to join the most attractive one. Simulations are performed to validate the work qualitatively by tracing the behavior of the simulated pedestrians and studying the impact of this behavior on lane formation. Finally, a quantitative measurement is used to study the effect of our contribution on the pedestrians’ efficiency of motion.

Optimal Collision-Free Path Planning for an Autonomous Multi-Wheeled Combat Vehicle

2017 ◽  
Author(s):  
Amr Mohamed ◽  
Moustafa El-Gindy ◽  
Jing Ren ◽  
Haoxiang Lang
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Vehicle Dynamics ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Vehicle Model ◽  
Optimal Path Planning ◽  
Combat Vehicle ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This paper presents an optimal collision-free path planning algorithm of an autonomous multi-wheeled combat vehicle using optimal control theory and artificial potential field function (APF). The optimal path of the autonomous vehicle between a given starting and goal points is generated by an optimal path planning algorithm. The cost function of the path planning is solved together with vehicle dynamics equations to satisfy the vehicle dynamics constraints and the boundary conditions. For this purpose, a simplified four-axle bicycle model of the actual vehicle considering the vehicle body lateral and yaw dynamics while neglecting roll dynamics is used. The obstacle avoidance technique is mathematically modeled based on the proposed sigmoid function as the artificial potential field method. This potential function is assigned to each obstacle as a repulsive potential field. The inclusion of these potential fields results in a new APF which controls the steering angle of the autonomous vehicle to reach the goal point. A full nonlinear multi-wheeled combat vehicle model in TruckSim software is used for validation. This is done by importing the generated optimal path data from the introduced optimal path planning MATLAB algorithm and comparing lateral acceleration, yaw rate and curvature at different speeds (9 km/h, 28 km/h) for both simplified and TruckSim vehicle model. The simulation results show that the obtained optimal path for the autonomous multi-wheeled combat vehicle satisfies all vehicle dynamics constraints and successfully validated with TruckSim vehicle model.

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