A New Artificial Potential Field Based Global Path Planning Algorithm for Mobile Robot Navigation

Jabari, Hamzah Al; Alobahji, Abdulrahman; Baran, Eray A.

doi:10.1109/amc51637.2022.9729318

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…It should be mentioned that the proposed navigation algorithm does not have access to the global map of the search environment, the locations of obstacles, and destination odor source. Therefore, planning-based obstacle avoidance algorithms like Artificial Potential Field algorithm [52], A* algorithm [53], Dijkstra algorithm [54], etc. are not applicable in our problem.…”

Section: End If 22: End Ifmentioning

confidence: 99%

Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm

Hassan,

Wang,

Mahmud

2024

Sensors

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Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot’s search behavior among four strategies: crosswind maneuver, Obstacle-Avoid Navigation, Vision-Based Navigation, and Olfaction-Based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-Based Navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms vision-only and olfaction-only methods, reducing average search time by 54% and 30%, respectively.

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Section: End If 22: End Ifmentioning

confidence: 99%

Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm

Hassan,

Wang,

Mahmud

2024

Sensors

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“…Theorem 1: Consider the dynamic safe function h(•) in (20), the system (7) and the DOB given in (8) with d(0) = 0. Suppose that Assumption 1 and 2 hold, and…”

Section: B Rdcbf Design For Mobile Manipulatormentioning

confidence: 99%

“…We designed two scenarios to test the obstacle avoidance capabilities, trajectory qualities, and the system safety under disturbance and inaccurate obstacle information. Additionally, there are constraints regarding the environment boundaries and the avoidance of Commonly used methods in local planning of mobile manipulator, such as APF [8], MPC [12], and CBF-based methods proposed in this paper, will be evaluated in both scenarios. Notably, Algorithm 1 and Algorithm 2 are also employed in MPC-based methods, serving as the distance algorithms for obstacle constraints.…”

Section: A Problem Statementmentioning

confidence: 99%

“…These algorithms generate an initial path that serves as a reference for local planners within the planning framework. Local planners like Artificial Potential Fields (APF) [8] and Dynamic Window Approach (DWA) [9] handle dynamic obstacles but face theoretical limitations when naturally extending to high-dimensional state spaces. For example, the APF algorithm needs to map the potential map into the manipulator's configuration space while the size of velocity space in DWA grows exponentially with the number of states.…”

Section: Introductionmentioning

confidence: 99%

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Tangent barrier Lyapunov function‐based constrained control of flexible manipulator system with actuator failure

Tang

Liu

2021

Intl J Robust & Nonlinear

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This article puts forward an adaptive neural network fault-tolerant control scheme under the state constraints for the flexible manipulator system with uncertain terms. The dynamic model of the system is described by partial differential equations. The tangent barrier Lyapunov functions are chosen in the design process for the sake of ensuring that all states in the system satisfy the constrained conditions. The uncertainties resulted from load mass, hub inertia, and bending stiffness in the system are approximated by using the universal approximation property of neural networks. The adaptive method is used to counteract the influence of joint motor actuator failure. At the same time, combining with the backstepping design framework to design effective controllers to assure that all signals in the closed-loop system are bounded. Lyapunov stability analysis method is used to prove the stability of the system. Finally, the simulation results prove the availability of the raised control method.

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Synchronization of the Coupled Swarm Agents’ Movement Interacting by the Thermal Motion Equivalent Method

Heiss,

Kozyr,

Morozov

2024

2024 IEEE 25th International Conference of Young Professionals in Electron Devices and Materials (EDM)

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A New Artificial Potential Field Based Global Path Planning Algorithm for Mobile Robot Navigation

Cited by 5 publications

References 16 publications

Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm

Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm

Tangent barrier Lyapunov function‐based constrained control of flexible manipulator system with actuator failure

Synchronization of the Coupled Swarm Agents’ Movement Interacting by the Thermal Motion Equivalent Method

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