Broadcast control of multi-robot systems with norm-limited update vector

Nor, Mohamad Hafizulazwan Mohamad; Ismail, Zool Hilmi; Ahmad, Mohd Ashraf

doi:10.1177/1729881420945958

Cited by 13 publications

(5 citation statements)

References 32 publications

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“…𝜗 𝑖 ⩽ 𝜗 𝑚𝑎𝑥 (10) Similarly, Equation ( 10) defines the constraints on the climb/dive angles, with 𝜗 𝑚𝑎𝑥 indicating the maximum permissible climb/dive angle, and 𝑖 = 1, 2, … , 𝑛. 𝐴 𝑖 < 𝐴 𝑚𝑎𝑥 (11) Furthermore, Equation ( 11), represents the limitation on altitude, where 𝐴 𝑚𝑎𝑥 denotes the maximum allowable altitude.…”

Section: Constraintsmentioning

confidence: 99%

“…This method provides robustness and flexibility in dynamic environments but faces challenges related to communication, computational complexity, and ensuring synchronization and stability among numerous agents. Similarly, the technique proposed in [11], broadcast control of multi-robot systems with norm-limited update vectors, ensures coordinated actions while maintaining system stability through constraints on the magnitude of updates. While simplifying communication and control in large-scale robotic systems, this method must overcome challenges related to communication reliability, scalability, and response time.…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Multiple Unmanned Aerial Vehicle Swarm Formation Control Using a Novel Fractional Swarming Strategy Approach

Wadood,

Yousaf,

Alatwi

2024

Fractal Fract

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This paper addresses the enhancement of multiple Unmanned Aerial Vehicle (UAV) swarm formation control in challenging terrains through the novel fractional memetic computing approach known as fractional-order velocity-pausing particle swarm optimization (FO-VPPSO). Existing particle swarm optimization (PSO) algorithms often suffer from premature convergence and an imbalanced exploration–exploitation trade-off, which limits their effectiveness in complex optimization problems such as UAV swarm control in rugged terrains. To overcome these limitations, FO-VPPSO introduces an adaptive fractional order β and a velocity pausing mechanism, which collectively enhance the algorithm’s adaptability and robustness. This study leverages the advantages of a meta-heuristic computing approach; specifically, fractional-order velocity-pausing particle swarm optimization is utilized to optimize the flying path length, mitigate the mountain terrain costs, and prevent collisions within the UAV swarm. Leveraging fractional-order dynamics, the proposed hybrid algorithm exhibits accelerated convergence rates and improved solution optimality compared to traditional PSO methods. The methodology involves integrating terrain considerations and diverse UAV control parameters. Simulations under varying conditions, including complex terrains and dynamic threats, substantiate the effectiveness of the approach, resulting in superior fitness functions for multi-UAV swarms. To validate the performance and efficiency of the proposed optimizer, it was also applied to 13 benchmark functions, including uni- and multimodal functions in terms of the mean average fitness value over 100 independent trials, and furthermore, an improvement at percentages of 29.05% and 2.26% is also obtained against PSO and VPPSO in the case of the minimum flight length, as well as 16.46% and 1.60% in mountain terrain costs and 55.88% and 31.63% in collision avoidance. This study contributes valuable insights to the optimization challenges in UAV swarm-formation control, particularly in demanding terrains. The FO-VPPSO algorithm showcases potential advancements in swarm intelligence for real-world applications.

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Section: Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Enhanced Multiple Unmanned Aerial Vehicle Swarm Formation Control Using a Novel Fractional Swarming Strategy Approach

Wadood,

Yousaf,

Alatwi

2024

Fractal Fract

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“…. (5) The entropy objective, 𝛼𝛼 , is defined based on Ziebart 37) . The temperature parameter, 𝛼𝛼, is the parameter that chooses the importance of relativity between the entropy and reward.…”

Section: Soft Actor-critic (Sac)mentioning

confidence: 99%

“…Robotics and control systems have become the most attractive research area for the researchers for decades 1,2,3,4), 5,6) . One of the challenging problems is controlling bipedal walking robots.…”

Section: Introductionmentioning

confidence: 99%

A Trajectory Control for Bipedal Walking Robot Using Stochastic-Based Continuous Deep Reinforcement Learning

Surriani,

Wahyunggoro,

Adha Imam Cahyadi

2023

Evergreen

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The bipedal walking robot is an advanced anthropomorphic robot that can mimic the human ability to walk. Controlling the bipedal walking robot is difficult due to its nonlinearity and complexity. To solve this problem, recent studies have applied various machine learning algorithms based on reinforcement learning approaches, however most of them rely on deterministic-policybased strategy. This research proposes Soft Actor Critic (SAC), which has stochastic policy strategy for controlling the bipedal walking robot. The option thought deterministic and stochastic policy affects the exploration of DRL algorithm. The SAC is a Deep Reinforcement Learning (DRL) based algorithm whose improvement obtained through the augmented entropy-based expected return allows the SAC algorithm to learn faster, gain exploration ability, and still ensure convergence. The SAC algorithm's performance is validated with a bipedal robot to walk towards the straight-line trajectory. The number of the reward and the cumulative reward during the training is used as the algorithm's performance evaluation. The SAC algorithm controls the bipedal walking robot well with a total reward of 384,752.8.

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“…Multi-robot hunting is one of the most important and complex challenges in the field of multi-robot cooperation due to the fact that it includes all multi-robot system subproblems such as task allocation, target localization, collaborative pursuing, obstacle and collisions avoidance. The research on multi-robot system cooperative hunting covers many disciplines and domain knowledge, such as optimization algorithms [8], real-time dynamic path planning [9]- [10], multi-robot coordination [11]- [13], planning and learning [14], and communication [15].…”

Section: Introductionmentioning

confidence: 99%

Hunting strategy for multi-robot based on wolf swarm algorithm and artificial potential field

Hamed¹,

Hamlich²,

Ennaji³

2022

IJEECS

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The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.

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Broadcast control of multi-robot systems with norm-limited update vector

Cited by 13 publications

References 32 publications

An Enhanced Multiple Unmanned Aerial Vehicle Swarm Formation Control Using a Novel Fractional Swarming Strategy Approach

An Enhanced Multiple Unmanned Aerial Vehicle Swarm Formation Control Using a Novel Fractional Swarming Strategy Approach

A Trajectory Control for Bipedal Walking Robot Using Stochastic-Based Continuous Deep Reinforcement Learning

Hunting strategy for multi-robot based on wolf swarm algorithm and artificial potential field

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