976 research outputs found
Autonomous Locomotion Mode Transition Simulation of a Track-legged Quadruped Robot Step Negotiation
Multi-modal locomotion (e.g. terrestrial, aerial, and aquatic) is gaining
increasing interest in robotics research as it improves the robots
environmental adaptability, locomotion versatility, and operational
flexibility. Within the terrestrial multiple locomotion robots, the advantage
of hybrid robots stems from their multiple (two or more) locomotion modes,
among which robots can select from depending on the encountering terrain
conditions. However, there are many challenges in improving the autonomy of the
locomotion mode transition between their multiple locomotion modes. This work
proposed a method to realize an autonomous locomotion mode transition of a
track-legged quadruped robot steps negotiation. The autonomy of the
decision-making process was realized by the proposed criterion to comparing
energy performances of the rolling and walking locomotion modes. Two climbing
gaits were proposed to achieve smooth steps negotiation behaviours for energy
evaluation purposes. Simulations showed autonomous locomotion mode transitions
were realized for negotiations of steps with different height. The proposed
method is generic enough to be utilized to other hybrid robots after some
pre-studies of their locomotion energy performances
Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation
An originally chaotic system can be controlled into various periodic
dynamics. When it is implemented into a legged robot's locomotion control as a
central pattern generator (CPG), sophisticated gait patterns arise so that the
robot can perform various walking behaviors. However, such a single chaotic CPG
controller has difficulties dealing with leg malfunction. Specifically, in the
scenarios presented here, its movement permanently deviates from the desired
trajectory. To address this problem, we extend the single chaotic CPG to
multiple CPGs with learning. The learning mechanism is based on a simulated
annealing algorithm. In a normal situation, the CPGs synchronize and their
dynamics are identical. With leg malfunction or disability, the CPGs lose
synchronization leading to independent dynamics. In this case, the learning
mechanism is applied to automatically adjust the remaining legs' oscillation
frequencies so that the robot adapts its locomotion to deal with the
malfunction. As a consequence, the trajectory produced by the multiple chaotic
CPGs resembles the original trajectory far better than the one produced by only
a single CPG. The performance of the system is evaluated first in a physical
simulation of a quadruped as well as a hexapod robot and finally in a real
six-legged walking machine called AMOSII. The experimental results presented
here reveal that using multiple CPGs with learning is an effective approach for
adaptive locomotion generation where, for instance, different body parts have
to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201
Dynamic Gaits and Control in Flexible Body Quadruped Robot
Legged robots are highly attractive for
military purposes such as carrying heavy loads on uneven
terrain for long durations because of the higher mobility
they give on rough terrain compared to wheeled
vehicles/robots. Existing state-of-the-art quadruped robots
developed by Boston Dynamics such as LittleDog and
BigDog do not have flexible bodies. It can be easily seen that
the agility of quadruped animals such as dogs, cats, and deer
etc. depend to a large extent on their ability to flex their
bodies. However, simulation study on step climbing in 3D
terrain quadruped robot locomotion with flexible body has
not been reported in literature. This paper aims to study the
effect of body flexibility on stability and energy efficiency in
walking mode, trot mode and running (bounding) mode on
step climbing
A literature review on the optimization of legged robots
Over the last two decades the research and development of legged locomotion robots has grown steadily. Legged
systems present major advantages when compared with ‘traditional’ vehicles, because they allow locomotion in inaccessible
terrain to vehicles with wheels and tracks. However, the robustness of legged robots, and especially their energy
consumption, among other aspects, still lag behind mechanisms that use wheels and tracks. Therefore, in the present
state of development, there are several aspects that need to be improved and optimized. Keeping these ideas in mind,
this paper presents the review of the literature of different methods adopted for the optimization of the structure
and locomotion gaits of walking robots. Among the distinct possible strategies often used for these tasks are referred
approaches such as the mimicking of biological animals, the use of evolutionary schemes to find the optimal parameters
and structures, the adoption of sound mechanical design rules, and the optimization of power-based indexes
Development of a Quadruped Robot and Parameterized Stair-Climbing Behavior
Stair-climbing is a difficult task for mobile robots to accomplish, particularly for legged robots. While quadruped robots have previously demonstrated the ability to climb stairs, none have so far been capable of climbing stairs of variable height while carrying all required sensors, controllers, and power sources on-board. The goal of this thesis was the development of a self-contained quadruped robot capable of detecting, classifying, and climbing stairs of any height within a specified range. The design process for this robot is described, including the development of the joint, leg, and body configuration, the design and selection of components, and both dynamic and finite element analyses performed to verify the design. A parameterized stair-climbing gait is then developed, which is adaptable to any stair height of known width and height. This behavior is then implemented on the previously discussed quadruped robot, which then demonstrates the capability to climb three different stair variations with no configuration change
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