A hyper-redundant robotic arm is a manipulator with many degrees of freedom, capable of executing tasks in cluttered environments where robotic arms with fewer degrees of freedom are unable to operate. This paper introduces a new method for modeling those manipulators in a completely dynamic way. The proposed method enables online changes of the kinematic structure with the use of a special function; termed “meta-controlling function”. This function can be used to develop policies to reduce drastically the computational cost for a single task, and to robustly control the robotic arm, even in the event of partial damage. The direct and inverse kinematics are solved for a generic three-dimensional articulated hyperredundant arm, that can be used as a proof of concept for more specific structures. To demonstrate the robustness of our method, experimental simulation results, for a basic “meta-controlling” function, are presented.
Hyper redundant robotic arms are useful, for operations in extremely cluttered environments with only a small free operational space as demonstrated by @cite_16 who developed a hyper redundant arm to make real time, precise operations inside nuclear reactors. @cite_33 created a snake fire-fighting hyper redundant robot, and @cite_22 used a 9-DoF arm to operate during surgeries in deep areas.
@cite_22 @cite_16 @cite_33
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The term "hyper-redundant" refers to robotic manipulators and mobile robots with a very large, possibly infinite, number of actuatable degrees of freedom. These robots are analogous in morphology and operation to snakes, worms, elephant trunks, and tentacles. This thesis presents a novel kinematic framework for hyper-redundant manipulator motion planning and task implementation. The basis of this formulation is the use of a "backbone reference set" which captures the essential macroscopic geometric features of hyper-redundant robots. In the analytical part of this work, the backbone representation is developed and used to solve problems in obstacle avoidance, locomotion, grasping, and "optimal" end effector placement. The latter part of this thesis deals with the design and implementation of a thirty-degree-of-freedom planar hyper-redundant manipulator which is used to demonstrate these novel kinematic and motion planning techniques. Design issues such as robustness with respect to mechanical failure, and design for easy assembly and repair are also addressed. The analytical and design concepts are combined to illustrate tasks for which hyper-redundant robotic mechanisms are well suited. A 30 degree-of-freedom (DOF) planar hyper-redundant manipulator is constructed in order to validate and demonstrate the authors' previous work on a framework for hyper-redundant robot kinematic analysis and algorithm development. The implementation of this robot is reviewed, and experimental work in hyper-redundant manipulation, obstacle avoidance, and locomotion is summarized. Potential benefits of high-DOF manipulators include superior obstacle avoidance ability, increased robustness with respect to mechanical failure, and the ability to perform new forms of robot locomotion and grasping. > To make production lines more flexible, dual-arm robots are good candidates to be deployed in autonomous assembly units. In this paper, we propose a sparse kinematic control strategy, that minimizes the number of joints actuated for a coordinated task between two arms. The control strategy is based on a hierarchical sparse QP architecture. We present experimental results that highlight the capability of this architecture to produce sparser motions (for an assembly task) than those obtained with standard controllers. "Hyper-redundant" robots have a very large or infinite degree of kinematic redundancy. This paper develops new methods for determining "optimal" hyper-redundant manipulator configurations based on a continuum formulation of kinematics. This formulation uses a backbone curve model to capture the robot's essential macroscopic geometric features. The calculus of variations is used to develop differential equations, whose solution is the optimal backbone curve shape. We show that this approach is computationally efficient on a single processor, and generates solutions in O(1) time for an N degree-of-freedom manipulator when implemented in parallel on O(N) processors. For this reason, it is better suited to hyper-redundant robots than other redundancy resolution methods. Furthermore, this approach is useful for many hyper-redundant mechanical morphologies which are not handled by known methods.