A Visual Predictive Control Framework for Robust and Constrained Multi-Agent Formation Control

Fallah, Mostafa M. H.; Janabi–Sharifi, Farrokh; Sajjadi, Sina; Mehrandezh, Mehran

doi:10.1007/s10846-022-01674-5

Cited by 12 publications

(10 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the authors in [23][24][25][26] propose a vision based algorithm to solve the formation control problem thus introducing various constraints into the system's design. This means that each robot must keep its predecessor in its Field Of View (FOV).…”

Section: Related Literaturementioning

confidence: 99%

“…Particularly, in [23] the authors use a novel Lyapunov barrier function to deal with the system's constraints while using a recursive adaptive backstepping method and Neural Network approximation to solve the formation tracking problem. Similarly, the authors in [24] proposed a robust depth-based visual predictive controller, which optimizes the trajectory planned while taking into account the constraints presented by the visual feedback. On the other hand, in [25] a differential game is suggested where one agent stays within the FOV of the other in a set workspace.…”

Section: Related Literaturementioning

confidence: 99%

See 1 more Smart Citation

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

Anogiatis,

Trakas,

Bechlioulis

2024

Electronics

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…The problem of controlling a swarm of drones is considered in a large number of articles today. Thus, an algorithm for organizing a collective movement based on the movement of a swarm "behind the leader", which is identified quite randomly, is considered [3,4]. In several works, for example, the attention of researchers is focused on the development of collision avoidance models in a moving swarm [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Approach to conception and modeling for distributed hierarchical control for autonomous drone swarm

2024

Adv Mach Lear Art Inte

View full text Add to dashboard Cite

Control of a drone swarm as a unity requires decentralization and hierarchy. Decentralizing control of drone swarms is necessary to free the human operator from having to constantly control the behavior of the drones within the swarm. Hierarchical control of a drone swarm is necessary so that human operators can adjust the activity of the swarm as a unit. The following separate roles have been identified for the implementation of decentralized hierarchical control of swarm activity: the activity of a separate drone, the activity of a drone- coordinator, and the activity of a human operator. The control hierarchy consists of a human operator who controls the change in the behavior of the drone coordinator. The drone coordinator controls the changes in the programmed behavior of individual drones. This approach is an analog of the management of human workers who perform assigned work, which opens up several possibilities. First, it is possible to use formal models of performance people’s behavior in social teams. Second, formal models can be used for decision-making and optimization for controlling a drone-coordinator in a swarm. Thirdly, computer modeling can be applied to the behavior of a drone swarm, which will allow choosing the optimal behavior of the swarm for different conditions of its activity

show abstract

“…The problem of controlling a swarm of drones is considered in a large number of articles today. Thus, in [3,4] an algorithm for organizing a collective movement based on the movement of a swarm "behind the leader", which is identi ed quite randomly, is considered.…”

Section: Introductionmentioning

confidence: 99%

Approach to modeling of distributed hierarchical control for autonomous drone swarm

Shyian

2023

Preprint

View full text Add to dashboard Cite

Control of a drone swarm as a unit requires decentralization and hierarchy. Decentralizing control of the drone swarm is necessary to free the human-operator from having to constantly control the behavior of the drones within the swarm. Hierarchical control of a drone swarm is necessary so that a human-operator can adjust the activity of the swarm as a unit (as a whole). To implement this approach, the control model is proposed. The following separate roles have been identified for the implementation of decentralized hierarchical control of swarm activity: the activity of a separate drone, the activity of a drone- coordinator, and the activity of a human-operator. The control hierarchy consists of a human-operator who controls the change in the behavior of the drone-coordinator. The drone-coordinator controls the changes in the behavior of individual drones in the swarm. Drones in a swarm perform programmed behavior. This approach allows us to consider the control of a drone swarm as an analog of the management of human-workers who perform assigned work. This opens up several possibilities. First, it is possible to use methods of formalizing people’s behavior in social teams. For example, at the level of formal models of performance of their functional duties. Second, formal models can be used for decision-making and optimization for controlling a drone-coordinator in a swarm. Thirdly, computer modeling can be applied to the behavior of a drone swarm, which will allow choosing the optimal behavior of the swarm for different conditions of its activity.

show abstract

A Visual Predictive Control Framework for Robust and Constrained Multi-Agent Formation Control

Cited by 12 publications

References 36 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

Approach to conception and modeling for distributed hierarchical control for autonomous drone swarm

Approach to modeling of distributed hierarchical control for autonomous drone swarm

Contact Info

Product

Resources

About