Enhanced multi-agent systems formation and obstacle avoidance (EMAFOA) control algorithm

Aljassani, Alaa M.H.; Ghani, Suadad Noori; Al‐Hajjar, Ali M. H.

doi:10.1016/j.rineng.2023.101151

Cited by 4 publications

(2 citation statements)

References 72 publications

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“…It is of vital importance to venture further into the obstacle avoidance methods like in [31][32][33][34][35]. In [31], a controller is designed based on a fuzzy cascaded PID method, which employs artificial potential algorithm to avoid obstacles and ensure the fleet stays in for-mation.…”

Section: Related Literaturementioning

confidence: 99%

“…In [31], a controller is designed based on a fuzzy cascaded PID method, which employs artificial potential algorithm to avoid obstacles and ensure the fleet stays in for-mation. Similarly, in [32] the authors propose an artificial potential function, which does not stuck in local minima, and by utilizing the Lyapunov theorem and Riccati equations they ensure stability of the suggested formation. Another interesting approach is proposed in [33], where the robot follows a human while avoiding obstacles.…”

Section: Related Literaturementioning

confidence: 99%

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Motion Coordination of Multiple Autonomous Mobile Robots under Hard and Soft Constraints

Anogiatis,

Trakas,

Bechlioulis

2024

Electronics

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This paper presents a distributed approach to the motion control problem for a platoon of unicycle robots moving through an unknown environment filled with static obstacles under multiple hard and soft operational constraints. Each robot has an onboard camera to determine its relative position in relation to its predecessor and proximity sensors to detect and avoid nearby obstascles. Moreover, no robot apart from the leader can independently localize itself within the given workspace. To overcome this limitation, we propose a novel distributed control protocol for each robot of the fleet, utilizing the Adaptive Performance Control (APC) methodology. By utilizing the APC approach to address input constraints via the on-line modification of the error specifications, we ensure that each follower effectively tracks its predecessor without encountering collisions with obstacles, while simultaneously maintaining visual contact with its preceding robot, thus ensuring the inter-robot visual connectivity. Finally, extensive simulation results are presented to demonstrate the effectiveness of the presented control system along with a real-time experiment conducted on an actual robotic system to validate the feasibility of the proposed approach in real-world scenarios.

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Section: Related Literaturementioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Motion Coordination of Multiple Autonomous Mobile Robots under Hard and Soft Constraints

Anogiatis,

Trakas,

Bechlioulis

2024

Electronics

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Practical time‐varying formation cooperative control for high‐order nonlinear multi‐agent systems avoiding spatial resource conflict via safety constraints

Ma,

Chou

2024

Intl J Robust & Nonlinear

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The operation of multi‐agent systems (MAS) in space necessitates considerations for obstacle avoidance, collision prevention, and connectivity among agents. This coupling conflict, stemming from spatial resource utilization, poses a significant and non‐negligible threat to the safe operation of MAS. This article proposes a consensus control scheme for the time‐varying formation of MAS, incorporating functions for maintaining connectivity, collision avoidance, and obstacle dodging. This scheme effectively constrains the time‐varying formation tracking error within an arbitrarily small range. The considered system model takes a high‐order nonlinear form, with uncertainties and disturbances present in each order. This grants the control scheme with high generality and practicability. By employing barrier Lyapunov function to delineate the safe operational space of MAS, conflicts in spatial resource utilization are avoided. This approach simultaneously fulfills the requirements for connectivity maintenance, collision avoidance, and obstacle dodging in the safe operation of MAS. An additional rotation operator is integrated into the controller to smoothly address the “minima” problem, eliminating the need for external intervention. Gaussian radial basis function is used to estimate the nonlinear terms, uncertainties, and unknown perturbation online in the system. The stability of the MAS under the proposed control scheme is analyzed through Lyapunov function. Finally, numerical simulation results are demonstrated to explain the effectiveness of the control scheme.

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A path planning algorithm for three-dimensional collision avoidance based on potential field and B-spline boundary curve

Liu,

Zhang,

Yang

et al. 2024

Aerospace Science and Technology

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Enhanced multi-agent systems formation and obstacle avoidance (EMAFOA) control algorithm

Cited by 4 publications

References 72 publications

Motion Coordination of Multiple Autonomous Mobile Robots under Hard and Soft Constraints

Motion Coordination of Multiple Autonomous Mobile Robots under Hard and Soft Constraints

Practical time‐varying formation cooperative control for high‐order nonlinear multi‐agent systems avoiding spatial resource conflict via safety constraints

A path planning algorithm for three-dimensional collision avoidance based on potential field and B-spline boundary curve

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