A potential field method for robot motion planning in unknown environments

Lai, Li-Chun; Wu, Chia‐Ju; Shiue, Yeong‐Long

doi:10.1080/02533839.2007.9671265

Cited by 11 publications

(4 citation statements)

References 22 publications

(25 reference statements)

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“…So the robot cannot reach the goal position. This problem was mentioned in many papers, and some solutions are proposed (Shi et al, 2007;Lai et al, 2007).…”

Section: Calculation Of Attractive and Repulsive Factorsmentioning

confidence: 99%

A novel auto‐adapted path‐planning method for a shape‐shifting robot

et al. 2011

International Journal of Intelligent Computing and Cybernetics

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Purpose -The purpose of this paper is to describe a shape-shifting robot with diverse configurations, named "AMOEBA-I", which has been developed for search and rescue operations. The accessibility of this robot to unstructured environment is efficiently enhanced by changing its configuration. So the shape and reconfiguration of the robot should be considered in AMOEBA-I path planning to improve work ability of the robot in complex environment. The unique accessibility of AMOEBA-I is thus fully displayed. Design/methodology/approach -An auto-adapted path-planning method is presented for AMOEBA-I by introducing the reconfigurable ability of the robot into the modified potential field method. The modified potential field method solves the local minimum problem and goal-unreachable with nearby obstacles (GUWNO) effectively. A method of the shape-shifting robot's passing through the narrow space is studied by combining the corner detection with the modified potential field method. Findings -The ability of the robot to automatically change configuration to pass through a narrow space is proven through the experiment. Simulation results show that the robot can change its own configurations to perform auto-adapted path planning corresponding to the environmental variation. Therefore, the proposed method can improve the probability of completing the path planning. As a result, this method will shorten the path length and complete the rescue operation more effectively. Originality/value -The paper presents an effective auto-adapted path-planning method that integrates the reconfigurable ability of the robot into the modified potential field method in order to realize the auto-adapted path planning.

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“…So the robot cannot reach the goal position. This problem was mentioned in many papers, and some solutions are proposed (Shi et al, 2007;Lai et al, 2007).…”

Section: Calculation Of Attractive and Repulsive Factorsmentioning

confidence: 99%

A novel auto‐adapted path‐planning method for a shape‐shifting robot

et al. 2011

International Journal of Intelligent Computing and Cybernetics

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“…Afterwards, a path following method computes the controls to steer the vehicle along the computed path, taking the system limitations into account. Various techniques have been developed to solve the path planning, including graph based methods such as A* [2] and D*-lite [3], random sampling based methods like Rapidly exploring Random Trees (RRT) [4] and Probabilistic Road Maps (PRM) [5], methods based on artificial potential fields [6] and [7], and optimization based methods such as Trajopt [8] and ITOMP [9]. The path following phase often uses Model Predictive Control (MPC) [10] to steer the system along the computed path, as MPC can explicitly account for the system limitations.…”

Section: Introductionmentioning

confidence: 99%

Online motion planning for autonomous vehicles in vast environments

Mercy

Hostens

Pipeleers

2018

2018 IEEE 15th International Workshop on Advanced Motion Control (AMC)

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Nowadays, the potential of autonomous vehicles for order picking and material transport in vast environments with large amounts of obstacles is only exploited to a limited extent. In order to realize free, time-optimal motion of autonomous vehicles through such complex environments, this paper presents a novel motion planning approach. The approach combines a global path planner with a local trajectory generator. The global planner finds a path through the complete environment, taking only the stationary obstacles into account. The local trajectory generator computes a detailed trajectory in a local frame around the global path, accounting for both stationary and moving obstacles. This trajectory is parameterized as a spline, and is obtained by solving an optimal control problem. In order to always include the latest information about the environment, the optimal control problem is solved online with a receding horizon. The paper demonstrates the potential of the proposed method with extensive numerical simulations. In addition, it presents an experimental validation in which a KUKA youBot moves through an obstructed environment. To facilitate the numerical and experimental validation of the presented method, it is embodied in a user-friendly open-source software toolbox.

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“…In mobile robot path planning problems, various approaches such as; Visibility Graph (VG) [2], Voronoi Diagram (VD) [3,4], Artificial Potential Field (APF) [5][6][7][8][9], Virtual Force Field (VFF) [10], Virtual Force Histogram (VFH) [11][12][13][14], classical Wall-Following (WF) [15][16][17][18], Neural Networkbased approach (NN) [19,20], Fuzzy Logic (FL) [21,22] etc. are proposed in literature.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Potential Field Based Mobile Robot Navigation Method To Prevent From Deadlock

Weerakoon

Ishii

Nassiraei

2015

Journal of Artificial Intelligence and Soft Computing Research

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Artificial Potential Filed (APF) is the most well-known method that is used in mobile robot path planning, however, the shortcoming is that the local minima. To overcome this issue, we present a deadlock free APF based path planning algorithm for mobile robot navigation. The Proposed-APF (P-APF) algorithm searches the goal point in unknown 2D environments. This method is capable of escaping from deadlock and non-reachability problems of mobile robot navigation. In this method, the effective front-face obstacle information associated with the velocity direction is used to modify the Traditional APF (T-APF) algorithm. This modification solves the deadlock problem that the T-APF algorithm often converges to local minima. The proposed algorithm is explained in details and to show the effectiveness of the proposed approach, the simulation experiments were carried out in the MATLAB environment. Furthermore, the numerical analysis of the proposed approach is given to prove a deadlock free motion of the mobile robot.

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A potential field method for robot motion planning in unknown environments

Cited by 11 publications

References 22 publications

A novel auto‐adapted path‐planning method for a shape‐shifting robot

A novel auto‐adapted path‐planning method for a shape‐shifting robot

Online motion planning for autonomous vehicles in vast environments

An Artificial Potential Field Based Mobile Robot Navigation Method To Prevent From Deadlock

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