SUMMARYIn this paper, we propose a novel type of serial robot with minimal actuation. The robot is a serial rigid structure consisting of multiple links connected by passive joints and of movable actuators. The novelty of this robot is that the actuators travel over the links to a given joint and adjust the relative angle between the two adjacent links. The joints passively preserve their angles until one of the actuators moves them again. This actuation can be applied to any serial robot with two or more links. This unique configuration enables the robot to undergo the same wide range of motions typically associated with hyper-redundant robots but with much fewer actuators. The robot is modular and its size and geometry can be easily changed. We describe the robot's mechanical design and kinematics in detail and demonstrate its capabilities for obstacle avoidance with some simulated examples. In addition, we show how an experimental robot fitted with a single mobile actuator can maneuver through a confined space to reach its target.
This article presents a motion planning method for a novel hyper-redundant robot with minimal actuation based on the principles of fractals and self-organizing systems. The robot consists of multiple links connected by passive joints and a movable actuator. The actuator travels over the links to a given joint and adjusts the relative angle between the two adjacent links allowing the robot to undergo the same wide range of motions of hyper-redundant robot but with only one actuator. A suitable objective of the motion planner is to minimize the number of actuator traversals, which translates into minimizing the number of bends in the c-space trajectory. To this end, we propose a novel method for motion planning using fractals and self-organizing systems. A self-similar pattern for the path is implemented to map a path from start to finish. Each iteration of path segments is of smaller dimension than the previous one and is appended to it, just as in classical fractals. This process continues until a feasible trajectory is calculated. Self-organizing systems are then applied to this trajectory post-processing to optimize it by eliminating bends in the path. Examples of the robot maneuvering around obstacles and through confined spaces are shown to demonstrate the efficacy of the motion planner.
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