Mobile robot requirements (1) Must behave in a deliberate manner (2) Must react appropriately to it’s environment (3) Must anticipate uncertain information (4) Must be both robust and fault tolerant (5) Architecture must be incrementally flexible Winter 2016 © Ian Davis

Mobile robot functions Typical software functions Acquiring and interpreting input from sensors Controlling the motion of all moving parts Planning future activities Responding to current difficulties Winter 2016 © Ian Davis

Mobile robot complications Possible complications Obstacles may block chosen path Sensor input may be imperfect or fail Robot may run out of power Movement may diverge from plans Robot encounters hazardous materials (water) Unpredictable events demand panic response Winter 2016 © Ian Davis

Mobile robot architectures Closed loop control architecture Layered architecture Implicit invocation Blackboard architecture Incremental architecture Winter 2016 © Ian Davis

Closed loop architecture Simple interconnection between sensors and actuators. Appropriate for rigid requirements. Eg. follow black line. Doesn’t scale well. Inflexible, hard to change or refine. Conflicts between multiple feedback loops. (eg. getting dark, near water, low battery). Winter 2016 © Ian Davis

Closed loop evaluation (1) Behaves in very deliberate manner. (2) Difficult to respond to the unexpected. (3) Hard to structure logic into cooperating components, or layer overall software. (4) Simplicity improves robustness. (5) Loose coordination between hardware components simplifies replacement and/or duplication of sub units. Winter 2016 © Ian Davis

Closed loop overall Appropriate for simple robotic systems that must respond to only a small number of external events, and whose tasks do not require complex decomposition. Appropriate for simple components of a larger systems (eg. Directional steering) Might be improved by a learning module responsible for adjusting the closed loop parameters based on experience. Winter 2016 © Ian Davis

Layered architecture User input and supervisory functions Global planning Control logic Navigation Real-world modelling via data structures Collective sensor integration Individual sensor interpretation Robots hardware control Winter 2016 © Ian Davis

Layered architecture observations Seems like we are getting serious about building a sophisticated robot. Much more detailed appreciation of the software problem, if not its solution. We are creating a lot of work for ourselves. Winter 2016 © Ian Davis

Layered architecture pros Comfortable organization of development responsibilities. Layers can be tested in isolation. Layers of sophistication cleanly convert low level sensory uncertainty into appropriate high level decision making. Real world data model improves flexibility by separating role of data capture from data interpretation. Winter 2016 © Ian Davis

Layered evaluation cons No clear division between data hierarchy (real-world modelling) and control hierarchy (real-world behaviour). Poor tolerance for failure. Hard to continue if layers of software fail or register faults. Changes to low levels of software likely to impact on all layers of software Winter 2016 © Ian Davis

Layered architecture overall High level view of robotic control system provides a good point of departure. Layered evaluation useful in identifying layers of interface which may simplify design and testing of robot. Layered approach ties our hands very early on in the design process. Architecture may not map cleanly to the low level implementation. Winter 2016 © Ian Davis

Implicit invocation Task control architecture (TCA). Hierarchy of tasks called task trees. Task trees are directed at one or more tasks registered to handle them. Task trees may be revised following exceptions etc. Winter 2016 © Ian Davis

Implicit invocation evaluation Clear separation of action and reaction. Explicit support for concurrent agents. Uncertainty may be addressed by distributed software, diverse software solutions etc. Exception handling, wiretapping, and monitoring features improve fault tolerance. Good support for incremental development. Winter 2016 © Ian Davis

Implicit invocation overall TCA offers a comprehensive set of features for coordinating tasks of robot based on both expected and unexpected events. Appropriate for complex robotic projects. Focuses on independent separate tasks. Provides better support for autonomous behaviour of parts than layered architecture. Winter 2016 © Ian Davis

Blackboard architecture Central repository of data reflecting real-world view/knowledge base seen by robot. Operated on by: Captain Navigator Lookout Pilot The perception subsystem Winter 2016 © Ian Davis

Blackboard evaluation Data is passive - participants are active. Stronger division into roles encourages greater cohesion than implicit invocation. Task control architecture can be usefully layered as a subsystem of blackboard. May suffer same implementation problems as layered approach. Suggested roles are somewhat arbitrary human concepts. Winter 2016 © Ian Davis

Iterative improvement approach Build the robot in iterative stages. Carefully progress the software from being overly simple and restrictive, towards showing intelligent behaviour. Design the software based not on perceived needs but on observed behaviour. Limited knowledge about expected final behavior of robot. Winter 2016 © Ian Davis

Iterative improvement pros Gets something up and working fast, improving moral. Allows low level software to be tried and tested before most development begins. Allows more feedback earlier, resulting in more flexible responses to problems. Invites change and innovation throughout project life cycle. Winter 2016 © Ian Davis

Iterative improvement cons Hard/impossible to co-ordinate team. Hard to manage constant software changes. Software quality very vulnerable to change. Resulting deliverable very unclear. Huge problems with software maintenance. Winter 2016 © Ian Davis

Iterative improvement overall Reasonable strategy for a small project, or large project involving very few people. Useful approach when prototyping. Appropriate for small sections of code, having well defined behaviour. A large unmanaged project is unlikely to be a successful project. Iterative improvement invites disaster. Winter 2016 © Ian Davis

Architectural checklist Is the overall organization clear, include a good overview and justification? Are the major goals clearly stated? Are modules well defined, including their functionality and interfaces to other modules? Are all the requirements covered sensibly, using an appropriate number of modules? Will the architecture accommodate likely changes? Are necessary buy.v.build decisions included? Does the architecture describe how reuse code will be retrofitted? Winter 2016 © Ian Davis

Architectural checklist All all major data structures described and justified? Are data structures hidden behind access interfaces? Is database organization and content specified? Are key algorithms described and justified? Are major objects described and justified? Is strategy for handling user input described? Is strategy for handling I/O described and justified? Are key aspects of user interface defined? Is the user interface modularized easing later change? Winter 2016 © Ian Davis

Architectural checklist Are memory requirements estimated, and strategy for memory management described? Does the architecture impose space and speed budgets? Is the strategy for handling strings described? Is a coherent error handling strategy provided? Are error messages managed cleanly? Is a level of robustness specified? Are parts of the architecture over or under architected? Is the architecture independent of machine and language? Are the motivations for all major decisions provided? Winter 2016 © Ian Davis

Architectural checklist IS THE GROUP OF PROGRAMMERS WHO WILL IMPLEMENT THE SYSTEM, COMFORTABLE WITH THE ARCHITECTURE? (From Code Complete - McConnell) Winter 2016 © Ian Davis