Energy-Efficient Collision-Free Machine/AGV Scheduling Using Vehicle Edge Intelligence DOI Creative Commons
Zhengying Cai, Juan Du, Tao Huang

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(24), P. 8044 - 8044

Published: Dec. 17, 2024

With the widespread use of autonomous guided vehicles (AGVs), avoiding collisions has become a challenging problem. Addressing issue is not straightforward since production efficiency, collision avoidance, and energy consumption are conflicting factors. This paper proposes novel edge computing method based on vehicle intelligence to solve energy-efficient collision-free machine/AGV scheduling First, architecture was built, corresponding state transition diagrams for were developed. Second, problem modeled as multi-objective function with electric capacity constraints, where prevention, conservation comprehensively considered. Third, an artificial plant community algorithm explored AGVs. The proposed utilizes heuristic search swarm multiple AGVs realize suitable deploying embedded platforms computing. Finally, benchmark dataset developed, some experiments conducted, results revealed that could effectively instruct automatic avoid high efficiency.

Language: Английский

Bio-Inspired Traffic Pattern Generation for Multi-AMR Systems DOI Creative Commons
Rok Vrabič, Andreja Malus,

Jure Dvoršak

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2849 - 2849

Published: March 6, 2025

In intralogistics, autonomous mobile robots (AMRs) operate without predefined paths, leading to complex traffic patterns and potential conflicts that impact system efficiency. This paper proposes a bio-inspired optimization method for autonomously generating spatial movement constraints (AMRs). Unlike traditional multi-agent pathfinding (MAPF) approaches, which focus on temporal coordination, our approach proactively reduces by adapting weighted directed grid graph improve flow. is achieved through four mechanisms inspired ant colony systems: (1) reward decreases the weight of traversed edges, similar pheromone deposition, (2) delay penalty increases edge weights along delayed (3) collision at conflict locations, (4) an evaporation mechanism prevents premature convergence suboptimal solutions. Compared existing proposed addresses entire intralogistic problem, including plant layout, task distribution, release dispatching algorithms, fleet size. Its rule generation low computational complexity make it well suited dynamic environments. Validated physics-based simulations in Gazebo across three scenarios, standard MAPF benchmark, two industrial environments, generated using improved throughput up 10% compared unconstrained navigation 4% expert-designed solutions while reducing need conflict-resolution interventions.

Language: Английский

Citations

0
Energy-Efficient Collision-Free Machine/AGV Scheduling Using Vehicle Edge Intelligence DOI Creative Commons
Zhengying Cai, Juan Du, Tao Huang

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(24), P. 8044 - 8044

Published: Dec. 17, 2024

With the widespread use of autonomous guided vehicles (AGVs), avoiding collisions has become a challenging problem. Addressing issue is not straightforward since production efficiency, collision avoidance, and energy consumption are conflicting factors. This paper proposes novel edge computing method based on vehicle intelligence to solve energy-efficient collision-free machine/AGV scheduling First, architecture was built, corresponding state transition diagrams for were developed. Second, problem modeled as multi-objective function with electric capacity constraints, where prevention, conservation comprehensively considered. Third, an artificial plant community algorithm explored AGVs. The proposed utilizes heuristic search swarm multiple AGVs realize suitable deploying embedded platforms computing. Finally, benchmark dataset developed, some experiments conducted, results revealed that could effectively instruct automatic avoid high efficiency.

Language: Английский

Citations

1