Dynamic approach to behavior-based robotics: design, specification, analysis, simulation and implementation
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2000 |
| Tipo de documento: | Livro |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/1822/2173 |
Resumo: | Tese de doutoramento em Electrónica Industrial, ramo de Automação e Controlo. Guimarães : Universidade do Minho, 1999. |
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Dynamic approach to behavior-based robotics: design, specification, analysis, simulation and implementationAutonomous roboticsBehavior based roboticsDynamical systemsMoving attractorsReppelersTarget acquisitionObstacle avoidanceWall followingTarget representationWall representationDynamic fieldsDynamic neural fieldsTese de doutoramento em Electrónica Industrial, ramo de Automação e Controlo. Guimarães : Universidade do Minho, 1999.The aim of this thesis is to investigate how behavior-based robots can be modeled by non-linear dynamical systems. Taking the example of navigation as a case study, dynamic control architectures are developed and implemented on low-level vehicles. These architectures combine a number of behaviors and lead to flexible and smooth overt behavior which is stably coupled in closed loop with sensory information. Moreover, these architectures also comprise dynamical representations of information which enable the vehicles to exhibit cognitive behaviors such as decision making, memory, forgetting and robustness against noisy sensory information. The design of the individual behaviors, of the representations of particular types of information, as well as of their coupling is based on the qualitative theory of dynamical systems and dynamic field theory. These provide a general theoretical language in which autonomous robot architectures can be built.Dynamical systems theory was used as a theoretical language and tool to design, specify, analyze, simulate and implement behavior-based control architectures. The architectures were fully formulated in terms of dynamics and implemented on computationally modest vehicle platforms based on very low-level sensory information. The main ideas and achievements are the following: 1. Attractor dynamics can be used to control motion based on low-level sensors (a) Robot action can be generated in the manner of control systems, by assigning values to planning (i.e. behavioral) variables continuously in time. A process that can be formalized through dynamical systems. It was shown how an intelligent choice of such planning variables makes it possible to generate flexible behavior from asymptotically stable states (attractors) of such dynamical systems. (b) Generation of behavior is an intrinsically non-linear problem. Behavioral situations exist, in which a minor change in the configuration or sensory situation must elicit a qualitative change in the behavior. This is a simple form of decision making. This non-linearity poses a problem for the design of dynamical systems, as no general theory exists for such systems. (c) Bifurcation theory is one branch of the theory of non-linear dynamical systems that is very structured by powerful theorems. Local bifurcation theory helps to describe how attractors and repellers of dynamical systems may annihilate by collision or emerge through splitting at critical parameter values. Because the approach in this thesis made use of attractor solutions only, local bifurcation theory may be employed to design the dynamical systems such that an appropriate bifurcation is obtained when a behavioral decision must be made. Additional benefits are the contribution of the behavioral dynamics to the overall control theoretic stability of the autonomous robot even when decision making takes place. (d) Sensory information enters into the dynamical system by defining attractive or repulsive values of the planning variables, determining the strength of attraction or repulsion as well as the range of values over which theses forces act. Although the contribution of each individual sensor to the dynamical system is not invariant under change of the values of the planning variables, and neither necessarily generates the right postulated functional form nor necessarily generates the right attractors and repellers, the superposition of all contributions of all sensors does exhibit that invariance and thus generates the designed dynamics. This is ultimately true because the environment is invariant, for instance, under rotation of the vehicles on the spot, and the summed contributions sample that environment. (e) We made a detailed presentation of how individual motion behaviors can be designed, how they can be integrated and moreover how they can be implemented on autonomous vehicles equipped with low-levels sensors like infra-red sensors, sonars, photo-resistors or microphones. (f) Vehicle motion toward targets while simultaneously avoiding obstacles and/or following walls was generated from attractors of dynamical systems of heading direction and path velocity. (g) Vehicle motion toward targets and avoidance of perceived obstructions also was generated from attractors of dynamical systems of angular and path velocities. 2. Dynamic fields can endow robots with sub-symbolic representations based on low-level sensory information (a) We showed how the ideas of attractor dynamics employed to control the motion of the robots can be extended to the level of representation by using dynamic fields to interpolate sensory information. (b) We have demonstrated how dynamic fields can provide robotic systems with sub-symbolic representations that rely on low-level sensory information. These representations enabled a robotic vehicle to exhibit the simplest forms of cognitive abilities. For instance: • A dynamic field model for target representation based on low-level sound sensors was built. This permitted the robot to exhibit skills such as detecting targets only if sensory information was consistent, estimating direction to a target through interpolation, deciding which target to track when multiple targets were presented and stabilization of such decision, maintaining targets in short-term memory during momentary absence of pertinent sensory information, and deleting a memory item after a characteristic delay to clear the memory from obsolete information. • A dynamic field model for wall representation based on low-level distance sensors (infra-red sensors and sonars) was also built that supported wall detection, robust wall orientation estimation and wall selection. 3. Representations can be integrated with stable action planning and control (a) The pattern of activation in a dynamic field shapes continuously in time the vector field of the dynamics of the behavior to which it is dedicated: i. The value over which a peak of positive activation is centered de- fines an attractive value of the planning variable used to design the behavioral dynamics. ii. The amount of total positive activation in the field determines the attraction strength of that force. iii. Because the field dynamics is invariant under rotations of the vehicle on the spot so is the dynamics that brings about the motion of the vehicle. (b) The amount of positive activation in a dynamic field may also inhibit the contribution of other behaviors to the complete behavioral dynamics. (c) The time scale of the field dynamics is set much faster than that of the planning variables so that the field has typically relaxed to a stable pattern on the time scale on which the movement plans of the robot evolve. The resulting behavior is therefore stable. (d) Two examples are: • The dynamic field for target representation specifies the particular form of the target acquisition dynamics. • Analogously, the dynamic field for wall representation specifies the wall-following dynamics. • The dynamic field for wall representation inhibits the contribution of obstacle avoidance to the planning dynamics when a wall is detected. 4. Navigation in non-engineered environments (a) The smooth overt behavior generated when the systems are set to work in non-structured environments was documented. (b) The implementation of the dynamic field for target representation on a small autonomous vehicle enabled it to find sound sources while avoiding obstacles. Memory helped to keep it moving toward the target during the periods when sensory information was not available. Decision making enabled the vehicle to track only one sound source. Hysteresis in the field dynamics enabled the vehicle to continue moving toward the selected sound target even when it approach a second sound source of equal intensity due to obstacle avoidance. Detection of a sound target with an intensity near the sensor threshold is stabilized through the cooperative forces within the field. (c) The implementation of the dynamic field for wall representation on a small autonomous vehicle enabled it to follow walls with various shapes (e.g. planar, circular and concave corners). Decision making allowed to select a wall and hysteresis permitted to maintain the decision stable even when new walls were encountered during the motion course.Shaker VerlagUniversidade do MinhoBicho, E.20002000-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttp://hdl.handle.net/1822/2173eng3-8265-7462-1info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-11T07:31:06Zoai:repositorium.sdum.uminho.pt:1822/2173Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-11T07:31:06Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
Dynamic approach to behavior-based robotics: design, specification, analysis, simulation and implementation |
| title |
Dynamic approach to behavior-based robotics: design, specification, analysis, simulation and implementation |
| spellingShingle |
Dynamic approach to behavior-based robotics: design, specification, analysis, simulation and implementation Bicho, E. Autonomous robotics Behavior based robotics Dynamical systems Moving attractors Reppelers Target acquisition Obstacle avoidance Wall following Target representation Wall representation Dynamic fields Dynamic neural fields |
| title_short |
Dynamic approach to behavior-based robotics: design, specification, analysis, simulation and implementation |
| title_full |
Dynamic approach to behavior-based robotics: design, specification, analysis, simulation and implementation |
| title_fullStr |
Dynamic approach to behavior-based robotics: design, specification, analysis, simulation and implementation |
| title_full_unstemmed |
Dynamic approach to behavior-based robotics: design, specification, analysis, simulation and implementation |
| title_sort |
Dynamic approach to behavior-based robotics: design, specification, analysis, simulation and implementation |
| author |
Bicho, E. |
| author_facet |
Bicho, E. |
| author_role |
author |
| dc.contributor.none.fl_str_mv |
Universidade do Minho |
| dc.contributor.author.fl_str_mv |
Bicho, E. |
| dc.subject.por.fl_str_mv |
Autonomous robotics Behavior based robotics Dynamical systems Moving attractors Reppelers Target acquisition Obstacle avoidance Wall following Target representation Wall representation Dynamic fields Dynamic neural fields |
| topic |
Autonomous robotics Behavior based robotics Dynamical systems Moving attractors Reppelers Target acquisition Obstacle avoidance Wall following Target representation Wall representation Dynamic fields Dynamic neural fields |
| description |
Tese de doutoramento em Electrónica Industrial, ramo de Automação e Controlo. Guimarães : Universidade do Minho, 1999. |
| publishDate |
2000 |
| dc.date.none.fl_str_mv |
2000 2000-01-01T00:00:00Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/book |
| format |
book |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/2173 |
| url |
http://hdl.handle.net/1822/2173 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
3-8265-7462-1 |
| dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Shaker Verlag |
| publisher.none.fl_str_mv |
Shaker Verlag |
| dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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mluisa.alvim@gmail.com |
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1817545345114046464 |