Trajectory planning for multi-robot systems: Methods and applications

Madridano, Ángel; Al-Kaff, Abdulla; Martín, David; Escalera, Arturo de la

doi:10.1016/j.eswa.2021.114660

Cited by 120 publications

(46 citation statements)

References 77 publications

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“…As such, the system’s complexity tends to increase proportionally to many entities. Based on this, different methods in the state of the art address the trajectory planning problem for multiple UAVs based on GNNS navigation [ 5 , 6 ] and multi-UAV task allocation [ 7 , 8 ]. In this context, multi-agent coverage path planning (CPP) is a subfield of trajectory planning where the algorithms have to find the optimal paths of UAVs equipped with sensors of a limited footprint to cover the free workspace [ 9 ] and the optimal path allocation for each UAV.…”

Section: Introductionmentioning

confidence: 99%

Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications

Luna

Isaac

Ragab

et al. 2022

Sensors

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This paper deals with the problems and the solutions of fast coverage path planning (CPP) for multiple UAVs. Through this research, the problem is solved and analyzed with both a software framework and algorithm. The implemented algorithm generates a back-and-forth path based on the onboard sensor footprint. In addition, three methods are proposed for the individual path assignment: simple bin packing trajectory planner (SIMPLE-BINPAT); bin packing trajectory planner (BINPAT); and Powell optimized bin packing trajectory planner (POWELL-BINPAT). The three methods use heuristic algorithms, linear sum assignment, and minimization techniques to optimize the planning task. Furthermore, this approach is implemented with applicable software to be easily used by first responders such as police and firefighters. In addition, simulation and real-world experiments were performed using UAVs with RGB and thermal cameras. The results show that POWELL-BINPAT generates optimal UAV paths to complete the entire mission in minimum time. Furthermore, the computation time for the trajectory generation task decreases compared to other techniques in the literature. This research is part of a real project funded by the H2020 FASTER Project, with grant ID: 833507.

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Section: Introductionmentioning

confidence: 99%

Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications

Luna

Isaac

Ragab

et al. 2022

Sensors

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show abstract

“…4 Due to control properties and application fields, there is a tendency to replace traditional integrated single-vehicle systems with multivehicle cooperative systems. 5 Nowadays, trajectory tracking is an important exploratory topic for both multivehicle cooperative systems and single-vehicle systems. 6 In Reference 7, forward and backward tracking for a nonholonomic multivehicle system is implemented by synchronous distributed receding horizon control.…”

Section: Introductionmentioning

confidence: 99%

Adaptive longitudinal control for multivehicle cooperative systems with actuator saturation under road bumps

Yang

Wang

et al. 2022

Intl J Robust & Nonlinear

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In this article, an adaptive longitudinal control strategy is proposed for a multivehicle cooperative system with actuator saturation. The multivehicle cooperative system is modeled as a longitudinal system. Uncertain disturbances caused by road bumps during vehicle driving are estimated by an adaptive extended state observer based on Silverman canonical transformation and pole placement.An adaptive integral sliding mode control algorithm is designed to accomplish adaptive longitudinal control and handle actuator saturation. With Lyapunov criterion, stability is presented for the multivehicle cooperative system with the adaptive longitudinal control strategy. Simulation results are given to show effectiveness of the adaptive extended state observer and the adaptive integral sliding mode control algorithm.

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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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Trajectory planning for multi-robot systems: Methods and applications

Cited by 120 publications

References 77 publications

Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications

Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications

Adaptive longitudinal control for multivehicle cooperative systems with actuator saturation under road bumps

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

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