Automated vehicles are becoming more prevalent in the modern world. Using platoons of automated vehicles can have numerous benefits including increasing the safety of drivers as well as streamlining roadway operations. How individual automated vehicles within a platoon react to each other is essential to creating an efficient method of travel. This paper looks at two individual vehicles forming a platoon and tracks the time headway between the two. Several speed profiles are explored for the following vehicle including a triangular and trapezoidal speed profile. It is discovered that a safety violation occurs during platoon formation where the desired time headway between the vehicles is violated. The aim of this research is to explore if this violation can be eliminated or reduced through utilization of different speed profiles.
To achieve this goal, a model of a swarm performing a collective transport task in a bounded domain featuring convex obstacles was simulated in MATLAB/ Simulink®. The closed-loop dynamic equations of this model were linearized about an equilibrium state with angular acceleration and linear acceleration set to zero. The simulation was run over 30 times to confirm system ability to successfully transport the payload to a goal point without colliding with obstacles and determine ideal operating conditions by testing various orientations of objects in the bounded domain. An additional purely MATLAB simulation was run to identify local minima of the Hessian of the navigation-like potential function. By calculating this Hessian periodically throughout the system’s progress and determining the signs of its eigenvalues, a system could check whether it is trapped in a local minimum, and potentially dislodge itself through implementation of a stochastic term in the robot controllers. The eigenvalues of the Hessian calculated in this research suggested the model local minima were degenerate, indicating an error in the mathematical model for this system, which likely incurred during linearization of this highly nonlinear system.
muscular hydrostat, which allows for nearly infinite degrees of freedom of movement without
the structure of a skeletal system. This study employed Magnetic Resonance Imaging with a
Gadoteridol-based contrast agent to image the octopus arm and view the internal tissues. Muscle
layering was mapped and area was measured using AMIRA image processing and the trends in
these layers at the proximal, middle, and distal portions of the arms were analyzed. A total of 39
arms from 6 specimens were scanned to give 112 total imaged sections (38 proximal, 37 middle,
37 distal), from which to ascertain and study the possible differences in musculature. The
images revealed significant increases in the internal longitudinal muscle layer percentages
between the proximal and middle, proximal and distal, and middle and distal sections of the
arms. These structural differences are hypothesized to be used for rapid retraction of the distal
segment when encountering predators or noxious stimuli. In contrast, a significant decrease in
the transverse muscle layer was found when comparing the same sections. These structural
differences are hypothesized to be a result of bending behaviors during retraction. Additionally,
the internal longitudinal layer was separately studied orally, toward the sucker, and aborally,
away from the sucker. The significant differences in oral and aboral internal longitudinal
musculature in proximal, middle, and distal sections is hypothesized to support the pseudo-joint
functionality displayed in octopus fetching behaviors. The results indicate that individual
octopus arm morphology is more unique than previously thought and supports that internal
structural differences exist to support behavioral functionality.
To achieve this goal, a model of swarm robots transportation should be designed, which is cruise control for this scenario. Secondly, based on free body diagram, force equilibrium equation can be deduced. Then, the function of plant can be deduced based on cruise control and force equilibrium equations. Thirdly, list potential controllers, which may implement desired controls of swarm robots, and test their performance. Modify value of gains and do simulations of these controller. After analyzing results of simulation, the best controller can be selected.
In the last section, there is conclusion of entire thesis project and pointing out future work. The section of future work will mention potential difficulties of building entire control system, which allow swarm robots transport over inclines in real environment.