Autonomous Planning
Autonomous Planning - Related Videos
11:40
Quantitative Autonomic Testing
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Cited by 176 •
2011
Standardized, comprehensive and fully quantitative testing of autonomic functions is described. The autonomic tests consist of evaluation of all three major autonomic domains including cardiovagal, adrenergic and sudomotor. The severity and distribution of dysautonomia is quantitated using Composite Autonomic Severity Scores.
01:22
Autonomic Nervous System
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2025
The autonomic nervous system (ANS) is a critical component of the peripheral nervous system, primarily responsible for regulating involuntary bodily functions and maintaining homeostasis. It functions in tandem with the central nervous system (CNS) to seamlessly coordinate various physiological processes without the need for conscious control. The ANS comprises two main divisions: the sympathetic and parasympathetic divisions. These divisions function antagonistically to maintain a dynamic...
03:18
Developing a Translaminar Pressure Model Using a Translaminar Autonomous System
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2025
Source: Sharma, T. P., et al., Translaminar Autonomous System Model for the Modulation of Intraocular and Intracranial Pressure in Human Donor Posterior Segments. J. Vis. Exp. (2020).This video demonstrates the procedure of preparing a human eye to study the effects of translaminar pressure on retinal ganglion cell signaling by simulating intraocular and intracranial pressures in a translaminar autonomous system.
09:45
Measuring Cardiac Autonomic Nervous System (ANS) Activity in Children
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Cited by 37 •
2013
Measurement of autonomic nervous system activity usually confines the researcher and participant to the laboratory, which may provide an intimidating environment to children. The VU University Ambulatory Monitoring System (VU-AMS) device can record cardiac autonomic control in any setting. The VU-AMS proved very amenable to testing in children.
01:23
Sampling Plans
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2024
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling. Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
FAQs
A planner represents the environment, available actions, constraints, and objectives, then searches or optimizes possible action sequences. Current observations guide which possibilities remain relevant, while specified goals and constraints determine how plans are evaluated. This representation lets an engineering system compare alternatives rather than select actions without regard to operating conditions.
Feedback gives the planner a way to compare current conditions with the assumptions behind an existing plan. When observations indicate that the environment or available options have changed, the system can revise its action sequence rather than continue following an unsuitable choice. This feedback-based behavior supports operation in settings where conditions are not fully predictable.
The selected plan depends on how the system represents its environment, which actions it treats as available, the constraints it must satisfy, and the objectives it prioritizes. Current observations also influence the search or optimization of possible sequences. Changing any of these inputs can alter the preferred plan and the resulting system behavior.
Compared with relying on continuous remote supervision, autonomous planning allows the system to generate, select, and revise actions using its own representation of conditions and current observations. This autonomy can reduce the need for constant human direction while retaining goal and constraint information in the planning process, which is important for systems operating beyond immediate supervision.
An engineering workflow begins by representing the environment, available actions, constraints, and objectives. The planner then uses current observations to search or optimize possible action sequences, selects an appropriate sequence, and applies feedback to update it when conditions change. Coordinating these stages links planning with perception and control, allowing actions to respond to operating conditions.
Robots, autonomous vehicles, spacecraft, and industrial systems can use this capability when they must act in uncertain or changing settings. In these applications, planning coordinates available actions with goals and constraints, while feedback supports adaptation to new observations. The intended engineering benefits are improved adaptability and efficiency with less continuous remote supervision.