6/20/2023 0 Comments Webots fitness functionLeveraging a fitness function to ensure that new code has structured, sensible logging during the development process ensures the operability and debuggability required for a production application. For example, logging is sometimes added as an afterthought and may not contain consistent or useful debugging information. Regardless of the application architecture (monolith, microservices, or other), fitness function-driven development can introduce continuous feedback for architectural conformance and inform the development process as it happens, rather than after the fact. During test-driven development we write tests to verify that features conform to desired business outcomes with fitness function-driven development we can also write tests that measure a system’s alignment to architectural goals. Fitness functions describe how close an architecture is to achieving an architectural aim. How do we enable evolution? Architectural goals and constraints may all change independently of functional expectations. “An evolutionary architecture supports guided, incremental change as the first principle across multiple dimensions.” What are fitness functions? The idea that architecture can support change was described by Neal Ford, Rebecca Parsons, and Pat Kua in 2017 as evolutionary architecture. For example, compliance standards today will likely be different a year from now ongoing changes in those standards should be reflected in gates to production deployment. What about other, non-functional requirements such as scalability, reliability, observability, and other architectural “-ilities”? How do we ensure operability and resiliency of features when they go to production? How can we encourage teams to build in these architectural standards, just as test-driven development builds in code quality and test coverage?Īrchitecture standards evolve constantly. TDD is an established practice for feature development that can improve code quality and test coverage. Finally, the proposed controller is compared with the developed method by other researchers.Test-driven development, or TDD, involves writing tests first then developing the minimal code needed to pass the tests. Humanoid navigation is performed in both simulated and experimental environments, and a comparison is done between them. To avoid the intercollision among the humanoids, a Petri‐net controller has been designed and implemented along with the proposed hybridised method. Here, navigation is performed in both static and dynamic environments. These optimised parameters are subsequently used by the adaptive ant colony optimisation technique to get the final turning angle by which the humanoid navigates in a cluttered environment. Here, the governing parameters of the adaptive ant colony optimisation technique are optimised by using adaptive particle swarm optimisation method. The inputs to the navigational controller are the front obstacle distance, left obstacle distance, and right obstacle distance, and the output is the required final turning angle to reach the target position. This paper is aimed at designing a navigation strategy for humanoid robots using a hybridised technique consisting of adaptive particle swarm optimisation and adaptive ant colony optimisation.
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