Multi-Head Path Planning of SwarmItFIX Agents: A Markov Decision Process Approach

Veeramani, S.; Muthuswamy, Sreekumar; Sagar, Keerthi; Zoppi, Matteo

doi:10.1007/978-3-030-20131-9_221

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Finally, required angles ( sm θ k,w ) of all the manipulator joints for all the waypoints are determined with the help of an optimization-based inverse kinematics model given in Eqs. (2)(3)(4)(5). Where, Eq.…”

Section: Trajectory Planning Of Comau Ns16mentioning

confidence: 99%

“…The workspace of these SwarmItFIX platform can be modified by varying the size of the workbenches or by increasing the number of docking pins. The details of the SwarmItFIX setup and the agent coordination have been described in detail in the previous works of the authors [2][3][4]. A software framework called MRROC++ has been utilized for the path planning of robot systems including ASEA IRB 6 and SwarmItFIX [5].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid type multi-robot path planning of a serial manipulator and SwarmItFIX robots in sheet metal milling process

Veeramani

Muthuswamy

2021

Complex Intell. Syst.

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This work investigates on the coordinated locomotion between a ceiling-mounted serial manipulator and two SwarmItFIX robots. The former holds the machining tool as an end effector, and the other two robots act as swarm robotic fixtures in a sheet metal milling process. A novel offline coordination planner which follows the hierarchical based hybrid type decentralized planning strategy has been proposed. Motion of the serial manipulator and SwarmItFIX robots’ coordinated locomotion are divided into three sub-problems, viz, trajectory planning of serial manipulator, task planning of SwarmItFIX robots, and homogenous prioritized multi-robot path planning of SwarmItFIX robots. Mathematical formulation of all the three sub-problems is developed and presented in this paper. A hexagonal segment that fits inside the boundaries of the workspace is considered as the machining trajectory. The tool velocity is assumed to be constant as it improves the quality of machining. The results obtained from the proposed planner is found to be efficient as the task planning module has computed the precise support locations and support duration for the SwarmItFIX robots. The multi-robot path planning module of the planner computes the optimal collision-free paths of SwarmItFIX robots for all goal positions. Finally, trajectories of SwarmItFIX robots are found to be completely in-line with the trajectory of tool center point (TCP) of the serial manipulator.

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Section: Trajectory Planning Of Comau Ns16mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid type multi-robot path planning of a serial manipulator and SwarmItFIX robots in sheet metal milling process

Veeramani

Muthuswamy

2021

Complex Intell. Syst.

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“…The novel Swing and Dock (SaD) locomotion strategy of the mobile base has already been explained in the previous works. [15][16][17] Head is a vacuum type of end effector used to support the sheet metal on its bottom surface. For the head to reach the specific coordinate within the workspace, the cooperation of all the three agents become essential.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning based path planning of multiple agents of SwarmItFIX robot for fixturing operation in sheetmetal milling process

Veeramani

Muthuswamy

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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SwarmItFIX (self-reconfigurable intelligent swarm fixtures) is a multi-agent setup mainly used as a robotic fixture for large Sheet metal machining operations. A Constraint Satisfaction Problem (CSP) based planning model is utilized currently for computing the locomotion sequence of multiple agents of the SwarmItFIX. But the SwarmItFIX faces several challenges with the current planner as it fails on several occasions. Moreover, the current planner computes only the goal positions of the base agent, not the path. To overcome these issues, a novel hierarchical planner is proposed, which employs Monte Carlo and SARSA TD based model-free Reinforcement Learning (RL) algorithms for the computation of locomotion sequences of head and base agents, respectively. These methods hold two distinct features when compared with the existing methods (i) the transition model is not required for obtaining the locomotion sequence of the computational agent, and (ii) the state-space of the computational agent become scalable. The obtained results show that the proposed planner is capable of delivering optimal makespan for effective fixturing during the sheet metal milling process.

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Artificial Intelligence in Manufacturing Equipment, Automation, and Robots

Chryssolouris

Alexopoulos

Arkouli

2023

A Perspective on Artificial Intelligence in Manufacturing

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Multi-Head Path Planning of SwarmItFIX Agents: A Markov Decision Process Approach

Cited by 6 publications

References 5 publications

Hybrid type multi-robot path planning of a serial manipulator and SwarmItFIX robots in sheet metal milling process

Hybrid type multi-robot path planning of a serial manipulator and SwarmItFIX robots in sheet metal milling process

Reinforcement learning based path planning of multiple agents of SwarmItFIX robot for fixturing operation in sheetmetal milling process

Artificial Intelligence in Manufacturing Equipment, Automation, and Robots

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