Collision avoidance for mobile robots based on artificial potential field and obstacle envelope modelling

Wu, Zhenyu; Hu, Guang; Lin, Feng; Wu, Jiping; Liu, Shenglan

doi:10.1108/aa-01-2016-008

Cited by 33 publications

(11 citation statements)

References 30 publications

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“…In this paper, the obstacles are considered to be circular for simplicity; however, other complex shapes can be accommodated through diffeomorphic mappings. 40,41 These shapes can include overlapping obstacles and other obstacle structures. Let x g , y g be the position of the goal point.…”

Section: Control Architecturementioning

confidence: 99%

Obstacle avoidance control of a human-in-the-loop mobile robot system using harmonic potential fields

2017

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SUMMARYThis paper considers applications where a human agent is navigating a semi-autonomous mobile robot in an environment with obstacles. The human input to the robot can be based on a desired navigation objective, which may not be known to the robot. Additionally, the semi-autonomous robot can be programmed to ensure obstacle avoidance as it navigates the environment. A shared control architecture can be used to appropriately fuse the human and the autonomy inputs to obtain a net control input that drives the robot. In this paper, an adaptive, near-continuous control allocation function is included in the shared controller, which continuously varies the control effort exerted by the human and the autonomy based on the position of the robot relative to obstacles. The developed control allocation function facilitates the human to freely navigate the robot when away from obstacles, and it causes the autonomy control input to progressively dominate as the robot approaches obstacles. A harmonic potential field-based non-linear sliding mode controller is developed to obtain the autonomy control input for obstacle avoidance. In addition, a robust feed-forward term is included in the autonomy control input to maintain stability in the presence of adverse human inputs, which can be critical in applications such as to prevent collision or roll-over of smart wheelchairs due to erroneous human inputs. Lyapunov-based stability analysis is presented to guarantee finite-time stability of the developed shared controller, i.e., the autonomy guarantees obstacle avoidance as the human navigates the robot. Experimental results are provided to validate the performance of the developed shared controller.

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Section: Control Architecturementioning

confidence: 99%

Obstacle avoidance control of a human-in-the-loop mobile robot system using harmonic potential fields

2017

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“…17 Wu et al proposed an artificial potential field method based on geometric modeling of obstacles, which greatly reduces local minima and trajectory oscillations. 18 Some scholars try to combine the artificial potential field algorithm with the A* algorithm to achieve the effect of improving efficiency. Raheem and Abdulkareem constructed an enhanced roadmap through a new combination of probabilistic roadmaps and artificial potential fields and then found the optimal path in the enhanced roadmap through the A* algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

Chen

Tan

et al. 2021

International Journal of Advanced Robotic Systems

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Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

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“…A variety of algorithms exist addressing the problem which could be broadly classified into geometric methods, potential field based methods, optimization methods and others. Collision cone [14] and velocity obstacle [15] are algorithms belonging to the first while [16][17][18] gives avoidance using the second category of avoidance methods. Optimization based avoidance method is adopted in [19,20].…”

Section: Introductionmentioning

confidence: 99%

CONCORD: A UAV Conflict Resolution System using Correlated Equilibrium based Decision Making

Tony

Ghose

Chakravarthy

2020

AIAA Scitech 2020 Forum

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Unmanned Aerial Vehicles (UAVs) are in demand for a wide range of applications. A traffic management system similar to the Air Traffic Control (ATC) is necessary to handle these vehicles efficiently. The complexity associated with such a system will increase with the size of airspace and the number of vehicles using it at any given instant of time. The need for collision avoidance among UAVs is quite significant in this context. This paper presents the CONCORD, which is a UAV conflict resolution system that uses the notion of correlated equilibrium. Correlated equilibrium is a popular solution concept in game theory, which improves upon Nash equilibrium solution and gives better expected payoff to every player. In this work, an intelligent semi-autonomous conflict resolution subsystem is proposed which could be a part of UAV traffic management system that can handle multiple UAV conflicts effectively.

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Collision avoidance for mobile robots based on artificial potential field and obstacle envelope modelling

Cited by 33 publications

References 30 publications

Obstacle avoidance control of a human-in-the-loop mobile robot system using harmonic potential fields

Obstacle avoidance control of a human-in-the-loop mobile robot system using harmonic potential fields

Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

CONCORD: A UAV Conflict Resolution System using Correlated Equilibrium based Decision Making

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