Minimally Actuated Robotic Arm
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  • Release Date: 2022-10-20
  • serial robot
  • minimal actuation
  • workspace analysis
  • heuristic search
Video Introduction

This video is adapted from 10.3390/app12199526

Kinematic redundancy refers to the difference between the number of degrees of freedom (DOF) of a robotic manipulator and the DOF needed to execute a given task [1]. In confined spaces where traditional robots are generally unable to operate, the most common solution is to use hyper-redundant robots (with more than 6 DOF and 3 DOF for 3D and 2D working spaces, respectively). Their large number of DOF enables them to easily maneuver around obstacles, avoid singularities and joint limits, and minimize the energy consumption [2][3]. However, this comes at the cost of a notoriously complex design for control and motion planning due to the high dimensionality of the problem [4]. Analytical methods usually require an extremely complicated characterization of the motion planning problem [5], which is impractical to apply, whereas numerical approaches struggle with expensive computational efforts stemming from the complexity level of the planning problem [6]. Although planning optimization for hyper redundant robots has been extensively researched in the last two decades, there have been no major breakthroughs in the field of motion planning for modular serial robots. Modular robotic arms are composed of a varying number of modules (links, joints, etc.) assembled together to achieve a given purpose [7][8]. The ability to change the assembly of the modules provides control over the level of redundancy of the robot so it can be adapted to a specific environment or task [8]. Although most algorithms have been developed to optimize the motion planning problem of hyper-redundant manipulators, the optimization of the redundancy itself remains an open challenge. Minimizing a manipulator’s redundancy level to fit a task enables easier and more efficient motion planning. Moreover, as the redundancy level increases, the motion plan is subjected to larger mechanical uncertainties, due to the accumulating backlash and the mechanical freedom associated with the joints. Thus, “minimal redundancy” clearly has both mechanical and computational advantages.

  1. Siciliano, B.; Khatib, O. Springer Handbook of Robotics; Springer: Berlin/Heidelberg, Germany, 2016.
  2. Burgner-Kahrs, J.; Rucker, D.C.; Choset, H. Continuum Robots for Medical Applications: A Survey. IEEE Trans. Robot. 2015, 31, 1261–1280.
  3. Shao, L.; Guo, B.; Wang, Y.; Chen, X. An overview on theory and implementation of snake-like robots. In Proceedings of the 2015 IEEE International Conference on Mechatronics and Automation (ICMA 2015), Beijing, China, 2–5 August 2015.
  4. Menon, M.S.; Ravi, V.C.; Ghosal, A. Trajectory planning and obstacle avoidance for hyper-redundant serial robots. J. Mech. Robot. 2017, 9, 041010.
  5. El-Sherbiny, A.; Elhosseini, M.A.; Haikal, A.Y. A comparative study of soft computing methods to solve inverse kinematics problem. Ain Shams Eng. J. 2018, 9, 2535–2548.
  6. Reiter, A. Path and Trajectory Planning. In Optimal Path and Trajectory Planning for Serial Robots; Springer Vieweg: Wiesbaden, Germany, 2020.
  7. Moubarak, P.; Ben-Tzvi, P. Modular and reconfigurable mobile robotics. Robot. Auton. Syst. 2012, 60, 1648–1663.
  8. Yim, M.; Shen, W.-M.; Salemi, B.; Rus, D.; Moll, M.; Lipson, H.; Klavins, E. Modular Self-reconfigurable Robot Systems: Challenges and Opportunities for the Future. IEEE Robot. Autom. Mag. 2007, 14, 43–52.
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Zarrouk, D.;  Gal, Y. Minimally Actuated Robotic Arm. Encyclopedia. Available online: (accessed on 20 June 2024).
Zarrouk D,  Gal Y. Minimally Actuated Robotic Arm. Encyclopedia. Available at: Accessed June 20, 2024.
Zarrouk, David, Yanai Gal. "Minimally Actuated Robotic Arm" Encyclopedia, (accessed June 20, 2024).
Zarrouk, D., & Gal, Y. (2022, October 20). Minimally Actuated Robotic Arm. In Encyclopedia.
Zarrouk, David and Yanai Gal. "Minimally Actuated Robotic Arm." Encyclopedia. Web. 20 October, 2022.
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