An adaptive multi-objective motion distribution framework for wheeled mobile manipulators via null-space exploration

Xing, Hongjun; Gong, Zhaopei; Ding, Liang; Torabi, Ali; Chen, Jinbao; Gao, Haibo; Tavakoli, Mahdi

doi:10.1016/j.mechatronics.2023.102949

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…The BAORNN algorithm by Khan et al [62] takes 4.9, 4.05, and 3.26 s for the same three cases of circular, rectangular, and Rhodonea end-effector trajectories, respectively, while moving on flat ground. [27] used AMoMDi-framework for motion accuracy of flat terrain mobile manipulator and took 10.8 s to complete a circular end-effector trajectory.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“… Bidirectional PSO optimization [58] took average simulation time of 657, 538, and 3648 s in circular, Bezier, and rectangular end-effector trajectories, respectively, while the base moves on flat terrain. Conventional PSO optimization for 7 DOF mobile manipulator mounted on a two-wheeled differential mobile platform [62] takes 375, 488, and 468 s for circular, rectangular, and Rhodonea end-effector trajectories, respectively, while moving on flat ground. The BAORNN algorithm by Khan et al [62] takes 4.9, 4.05, and 3.26 s for the same three cases of circular, rectangular, and Rhodonea end-effector trajectories, respectively, while moving on flat ground. Xing et al [27] used AMoMDi-framework for motion accuracy of flat terrain mobile manipulator and took 10.8 s to complete a circular end-effector trajectory. …”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Xing et al [27] used AMoMDi-framework for motion accuracy of flat terrain mobile manipulator and took 10.8 s to complete a circular end-effector trajectory.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Past research has focused on resolving redundancy in mobile manipulator systems on 2D flat terrain with maximum manipulability ellipsoid [30][31][32], but required optimization and faced convergence issues, as well as locomotion inaccuracy [27] in a wheeled mobile base. Sandakalum and Ang, Jr. [33] have systematically reviewed planning algorithms for mobile manipulators and found that coordinating the mobile base and manipulator during optimal planning is challenging due to computational load.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hacot [25] and Seraji [26] have extended the control schemes to mobile manipulators by incorporating redundancy and analyzing wheel-terrain interactions. Various approaches have been proposed to improve the motion accuracy of redundant mobile manipulators, including adaptive multi-objective motion distribution (AMoMDiF by Xing et al [27]) and differential IK control [28] on industrial manipulators (6DOF U 10 ) in a structured flat environment. Tchoń and Jakubiak [29] developed a band-limited repeatable extended Jacobian formulation to solve the IK of a four-wheeled mobile manipulator.…”

Section: Literature Reviewmentioning

confidence: 99%

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End-effector path tracking of a 14 DOF Rover Manipulator System in CG-Space framework

Katiyar,

Dutta

2023

Robotica

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In order to resolve redundancy and path planning of a high DOF mobile manipulator using conventional approaches like Jacobian and a pseudoinverse method, researchers face the limitation of computational load and delay in response. If such kind of mobile manipulator is traversing the rough terrain, then conventional methods become too costly to implement due to the handling of redundant joints, obstacles, and wheel-terrain interaction. A few optimization-based redundancy resolution approaches try incorporating wheel-terrain interaction but fail in real-time response. This paper describes a 14 DOF Rover Manipulator System’s end-effector path-tracking approach using CG-Space framework to incorporate wheel-terrain interaction. CG-Space means the center of gravity (CG) locus of the Rover. The Rover’s CG is calculated while traversing over 3D terrain using a multivariable optimization method and a 3D point cloud image of the actual terrain and stored as CG-Space over the given terrain. First of all, we decide which part of the system moves to track the path, that is, arm or Rover, depending upon the manipulator’s work volume and manipulability measure restrictions. The next task is to obtain the Rover pose according to the end-effector path using a simple arm’s inverse kinematic solution between the CG-Space and end-effector task space without resolving redundancy. Meanwhile, obstacles and non-traversable regions are avoided in CG-Space. On diverse 3D terrains, simulation and experimental results support the suggested CG-Space framework for end-effector tracking.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Xing et al [27] used AMoMDi-framework for motion accuracy of flat terrain mobile manipulator and took 10.8 s to complete a circular end-effector trajectory.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%