A Short Review on Biped Robots Motion Planning and Trajectory Design

Reddy, Kunchala Balakrishana; Suresh, Gamini; Mandava, Ravi Kumar; Kumar, T. Ch. Anil

doi:10.1007/978-981-16-8341-1_39

Cited by 5 publications

(3 citation statements)

References 37 publications

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“…In biped robots, Balakrishana et al [69] delve into their increasing relevance, attributed to their uncanny resemblance to human structure and joint mechanisms. Despite their intricate designs and multiple degrees of freedom, these robots rely heavily on meticulous gait planning to guarantee stable locomotion.…”

Section: Human-inspired Approachesmentioning

confidence: 99%

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Humanoid Robot Motion Planning Approaches: a Survey

de Lima,

Khan,

Tufail

et al. 2024

J Intell Robot Syst

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Humanoid robots are complex, dynamic systems. Any humanoid robotic application starts with determining a sequence of optimal paths to perform a given task in a known or unknown environment. This paper critically reviews and rates available literature on the three key areas of multi-level motion and task planning for humanoid robots. First is efficiency while navigating and manipulating objects in environments designed for humans. Here, the research has broadly been summarized as behavior cloning approaches. Second is robustness to perturbations and collisions caused by operation in dynamic and unpredictable environments. Here, the modeling approaches integrated into motion planning algorithms have been the focus of many researchers studying humanoid motion’s balance and dynamic stability aspects. Last is real-time performance, wherein the robot must adjust its motion based on the most recent sensory data to achieve the required degree of interaction and responsiveness. Here, the focus has been on the kinematic constraints imposed by the robot’s mechanical structure and joint movements. The iterative nature of solving constrained optimization problems, the computational complexity of forward and inverse kinematics, and the requirement to adjust to a rapidly changing environment all pose challenges to real-time performance. The study has identified current trends and, more importantly, research gaps while pointing to areas needing further investigation.

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Section: Human-inspired Approachesmentioning

confidence: 99%

“…Using theories from human motor control, the algorithm can adjust to different constraints based on the task. Another work, [69], offers another perspective on path planning, which provides an overview of biped robots and their challenges in gait planning.…”

Section: Human-inspired Approachesmentioning

confidence: 99%

Humanoid Robot Motion Planning Approaches: a Survey

de Lima,

Khan,

Tufail

et al. 2024

J Intell Robot Syst

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“…With the use of weighted graph theory and hybrid TLBO, process parameters and path planning strategies are optimized. 28 In building complex structures height and surface roughness of deposited beads are crucial for later bead deposition. Hence the influence of process parameters is studied and shown in Figure 13.…”

Section: Predictive Modelmentioning

confidence: 99%

Inclusion of IoT technology in additive manufacturing: Machine learning-based adaptive bead modeling and path planning for sustainable wire arc additive manufacturing and process optimization

Reddy

Suresh

Anilkumar

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Industrial civilization transforms current cutting edge technologies and the evolution of Industry 5.0 is more aggressive with the use of IoT-enabled smart machines and robots in the manufacturing sector today. IoT technology deals with digital data as in additive manufacturing (AM). The potential and progressive aspects of AM embarks for functional part development instead of initial prototyping. AM is one of large-scale production with less buy-to-fly (BTF) ratio. In the present work, a novel framework has been proposed and utilized to attain adaptive bead modeling and an appropriate path plan for enhanced deposition and surface quality of weld beads. Further, the influence of input process parameters toward sustainable wire arc additive manufacturing (WAAM) is also focused. Machine learning-based hybrid-TLBO (h-TLBO) and support vector machine (SVM) is deployed for the optimization process. With the aid of graph theory, weights are estimated for h-TLBO. The overall process parameters and entire data module is handled with IoT technology and can be accessed for processing. The simulated post-processing results are validated experimental test results and found to be in good concurrence.

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