The static properties of tensegrity structures have been widely appreciated in civil engineering as the basis of extremely lightweight yet strong mechanical structures. However, the dynamic properties and their potential utility in the design of robots have been relatively unexplored. This paper introduces robots based on tensegrity structures, which demonstrate that the dynamics of such structures can be utilized for locomotion. Two tensegrity robots are presented: TR3, based on a triangular tensegrity prism with three struts, and TR4, based on a quadrilateral tensegrity prism with four struts. For each of these robots, simulation models are designed, and automatic design of controllers for forward locomotion are performed in simulation using evolutionary algorithms. The evolved controllers are shown to be able to produce static and dynamic gaits in both robots. A real-world tensegrity robot is then developed based on one of the simulation models as a proof of concept. The results demonstrate that tensegrity structures can provide the basis for lightweight, strong, and fault-tolerant robots with a potential for a variety of locomotor gaits.
No legged walking robot yet approaches the high reliability and the low power usage of a walking person, even on flat ground. Here we describe a simple robot which makes small progress towards that goal. Ranger is a knee-less four-legged 'bipedal' robot which is energetically and computationally autonomous, except for radio controlled steering. Ranger walked 65.2 km in 186,076 steps in about 31 h without being touched by a human with a total cost of transport [TCOT ≡ P/mgv] of 0.28, similar to human's TCOT of ≈ 0.3. The high reliability and low energy use were achieved by: (a) development of an accurate bench-test-based simulation; (b) development of an intuitively tuned nominal trajectory based on simple locomotion models; and (c) offline design of a simple reflex-based (that is, event-driven discrete feed-forward)stabilizing controller. Further, once we replaced the intuitively tuned nominal trajectory with a trajectory found from numerical optimization, but still using event-based control, we could further reduce the TCOT to 0.19. At TCOT = 0.19, the robot's total power of 11.5 W is used by sensors, processors and communications (45%), motor dissipation (≈34%) and positive mechanical work (≈21%). Ranger's reliability and low energy use suggests that simplified implementation of offline trajectory optimization, stabilized by a low-bandwidth reflex-based controller, might lead to the energy-effective reliable walking of more complex robots.
Current thinking attributes information processing for neuromuscular control exclusively to the nervous system. Our cadaveric experiments and computer simulations show, however, that the tendon network of the fingers performs logic computation to preferentially change torque production capabilities. How this tendon network propagates tension to enable manipulation has been debated since the time of Vesalius and DaVinci and remains an unanswered question. We systematically changed the proportion of tension to the tendons of the extensor digitorum versus the two dorsal interosseous muscles of two cadaver fingers and measured the tension delivered to the proximal and distal interphalangeal joints. We find that the distribution of input tensions in the tendon network itself regulates how tensions propagate to the finger joints, acting like the switching function of a logic gate that nonlinearly enables different torque production capabilities. Computer modeling reveals that the deformable structure of the tendon networks is responsible for this phenomenon; and that this switching behavior is an effective evolutionary solution permitting a rich repertoire of finger joint actuation not possible with simpler tendon paths. We conclude that the structural complexity of this tendon network, traditionally oversimplified or ignored, may in fact be critical to understanding brain-body coevolution and neuromuscular control. Moreover, this form of information processing at the macroscopic scale is a new instance of the emerging principle of nonneural "somatic logic" found to perform logic computation such as in cellular networks.
This paper describes a neuro-musculo-skeletal model of the human lower body which has been developed with the aim of studying the effects of spinal cord injury on locomotor abilities. The model represents spinal neural control modules corresponding to central pattern generators, muscle spindle based reflex pathways, golgi tendon organ based pathways and cutaneous reflex pathways, which are coupled to the lower body musculo-skeletal dynamics. As compared to other neuro-musculo-skeletal models which aim to provide a description of the possible mechanisms involved in the production of locomotion, the goal of the model here is to understand the role of the known spinal pathways in locomotion. Thus, while other models focus primarily on functionality at the overall system level, the model here emphasizes functional and topological correspondance with the biological system at the level of the subcomponents representing spinal pathways. Such a model is more suitable for the detailed investigation of clinical questions related to spinal control of locomotion. The model is used here to perform preliminary experiments addressing the following issues: (1) the significance of spinal reflex modalities for walking and (2) the relative criticality of the various reflex modalities. The results of these experiments shed new light on the possible role of the reflex modalities in the regulation of stance and walking speed. The results also demonstrate the use of the model for the generation of hypothesis which could guide clinical experimentation. In the future, such a model may have applications in clinical diagnosis, as it can be used to identify the internal state of the system which provides the closest behavioral fit to a patient's pathological condition.
In this paper, stable bipedal locomotion has been achieved using coupled evolution of morphology and control on a 5-link biped robot in a physicsbased simulation environment. The robot was controlled by a closed loop recurrent neural network controller. The goal was to study the effect of macroscopic, midrange and microscopic changes in mass distribution along the biped skeleton to ascertain whether optimal morphology and control pairs could be discovered. The sensor-motor coupling determined that small changes in morphology manifest themselves as large changes in the performance of the biped, which were exploited by the optimization process. In this way, mechanical design and controller optimization were reduced to a single process, and more mutually optimized designs resulted. This work points to alternative routes for efficient automated and manual biped optimization.
Tensegrity structures are stable 3-dimensional mechanical structures which maintain their form due to an intricate balance of forces between disjoint rigid elements and continuous tensile elements. Tensegrity structures can give rise to lightweight structures with high strength-to-weight ratios and their utility has been appreciated in architecture, engineering and recently robotics. However, the determination of connectivity patterns of the rigid and tensile elements which lead to stable tensegrity is challenging. Available methods are limited to the use of heuristic guidelines, hierarchical design based on known components, or mathematical methods which can explore only a subset of the space. This paper investigates the use of evolutionary algorithms in the form-finding of tensegrity structures. It is shown that an evolutionary algorithm can be used to explore the space of arbitrary tensegrity structures which are difficult to design using other methods, and determine new, non-regular forms. It suggests that evolutionary algorithms can be used as the basis for a general design methodology for tensegrity structures.
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