A                                                 G3-Continuous Extend Procedure for Path Planning of Mobile Robots with Limited Motion Curvature and State Constraints

Gawron, Tomasz; Michałek, Maciej Marcin

doi:10.3390/app8112127

Cited by 14 publications

(8 citation statements)

References 23 publications

(41 reference statements)

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“…Xue [32] presented a multi-objective evolutionary algorithm for the path planning problem. Another approach, authored by Gawron and Michałek [33], is available for the path planning problem of mobile robots. They showed that their path planning, which is collision-free, satisfies curvature constraints, and preserves continuity of the curvature arc-length derivative.…”

Section: Advanced Mobile Roboticsmentioning

confidence: 99%

Special Feature on Advanced Mobile Robotics

Kim

2019

Applied Sciences

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Section: Advanced Mobile Roboticsmentioning

confidence: 99%

Special Feature on Advanced Mobile Robotics

Kim

2019

Applied Sciences

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“…According to the principle of geometry, the operating system regards the mobile robot as a point and realizes the path planning of the mobile robot from the start point to the target point [9]. The most direct method is visibility-based graphs, connect the vertices of obstacles to construct different polygons [10][11][12], and then use path planning algorithms to plan the path. The Dijkstra algorithm is a typical method and realizes the shortest path search by accumulating the path length.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Fusion Pathfinding Algorithm Using Delaunay Triangulation and Improved A-Star for Mobile Robots

Liu

et al. 2021

IEEE Access

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Although many studies exist on mobile robot path planning, the disadvantages of complex algorithms and many path nodes in logistics warehouses and manufacturing workshops are obvious, mainly due to the inconsistency of map environment construction and pathfinding strategies. In this study, to improve the efficiency of mobile robot path planning, the Delaunay triangulation algorithm was used to process complex obstacles and generate Voronoi points as pathfinding priority nodes. The concept of the grid was used to extract obstacle edges to provide obstacle avoidance strategies for robot pathfinding. Subsequently, the search for priority and regular path nodes used the improved A-star (A*) algorithm. The dynamic fusion pathfinding algorithm (DFPA), based on Delaunay triangulation and improved A*, was designed, which realizes the path planning of mobile robots. MATLAB 2016a was used as the simulation software, to firstly verify the correctness of the DFPA, and then to compare the algorithm with other methods. The results show that under the experimental environment with the same start point, goal point, and number of obstacles, the map construction method and pathfinding strategy proposed in this paper reduce the planned path length of the mobile robot, the number of path nodes, and the cost of overall turn consumption, and increase the success rate of obtaining a path. The new dynamic map construction method and pathfinding strategy have important reference significance for processing chaotic maps, promoting intelligent navigation, and site selection planning.INDEX TERMS Delaunay triangulation, A-star algorithm, mobile robot, map modeling, path planning.

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“…The mathematical optimization approaches, firstly formulating a cost function of the automatic parking problem and then minimizing the objective cost function [9][10][11][12][13]. Besides car-like vehicles, reference trajectory is feasible in the case of parking N-trailer vehicles when complicated kinematic constraints are taken into account in planning the trajectory [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…in the case of parking N-trailer vehicles when complicated kinematic constraints are taken into account in planning the trajectory [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An Inverse Vehicle Model for a Neural-Network-Based Integrated Lateral and Longitudinal Automatic Parking Controller

2019

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The majority of currently used automatic parking systems exploit the planning-and-tracking approach that involves planning the reference trajectory first and then tracking the desired reference trajectory. However, the response delay of longitudinal velocity prevents the parking controller from tracing the desired trajectory because the vehicle's velocity and other state parameters are not synchronized, while the controller maneuvers the vehicle according to the planned desired velocity and steering profiles. We propose an inverse vehicle model to provide a neural-network-based integrated lateral and longitudinal automatic parking controller. We approximated the relationship of the planned velocity to the vehicle's velocity using a second-order difference equation that involves the response characteristic of the vehicle's longitudinal delay. The adjusted desired velocity to track the origin-planned velocity is calculated using the inverse vehicle model. Furthermore, we proposed an integrated longitudinal and lateral parking controller using an artificial neural network (ANN) model trained on a dataset applying the inverse vehicle model. By learning the control laws between the vehicle's states and the corresponding actions, the proposed ANN-based controller could yield a steering angle and the adjusted desired velocity to complete automatic parking in a confined space.Fuzzy-based and knowledge-based approaches have been presented [17][18][19]. These methods are capable of providing solutions within a range of designed rules with an advantage in easy implements and practical usages.Recently, methods based on deep neural networks have been expected to solve the drawbacks of the mentioned step-by-step approaches by maneuvering vehicles without prior offline trajectory planning [20][21][22][23]. By training an artificial neural network (ANN) using a dataset generated by simulation or experiment, the ANN learns hyper-dimensional relationships between the current vehicle states and the appropriate vehicle maneuvering signals. Instead of calculating the parking trajectory offline, the ANN-based parking controller can yield a direct maneuvering signal of the steering angle and velocity online, while the vehicle is moving into a parking space. Liu et al. presented a method to enumerate all the possible parking trajectories and corresponding steering actions, and then have the parking controller learn the relationship between the given initial-and-final state pairs and the corresponding sequence of steering actions using an ANN [20]. Li et al. proposed an end-to-end neural-network-based automatic parking controller [21]. Rathour also proposed an encoder-decoder architecture for automatic parking [22]. Moon et al. developed an automatic parking controller with a twin ANN architectures [23].However, neither the planning-based methods nor ANN-based controllers have taken account of longitudinal control delay for a vehicle under automatic parking. Figure 1 shows a typical block diagram of a conventional automatic parking contr...

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A G3-Continuous Extend Procedure for Path Planning of Mobile Robots with Limited Motion Curvature and State Constraints

Cited by 14 publications

References 23 publications

Special Feature on Advanced Mobile Robotics

Special Feature on Advanced Mobile Robotics

A Dynamic Fusion Pathfinding Algorithm Using Delaunay Triangulation and Improved A-Star for Mobile Robots

An Inverse Vehicle Model for a Neural-Network-Based Integrated Lateral and Longitudinal Automatic Parking Controller

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