| Due to escalation in complexity, capability and application, robot design is increasingly difficult. A design environment can automate many design tasks, relieving the designer's burden. Prior to robot development, designers compose a robot from existing or custom developed components, simulate performance, optimize configuration and parameters, and write software for the robot. Robot designers customize these facets to the robot using a variety of software ranging from spreadsheets to C code to CAD tools. Valuable resources are expended, and very little of this expertise and development is reusable. This research begins with the premise that a language to comprehensively represent robots is lacking and that the aforementioned design tasks can be automated once such a language exists. This research proposes and demonstrates the following thesis: "A language to represent robots, along with a framework to generate simulations, optimize designs and generate control software, increases the effectiveness of design."; Synergy is the software developed in this research to reflect this philosophy. Synergy was prototyped and demonstrated in the context of lunar rover design, a challenging real-world problem with multiple requirements and a broad design space. Synergy was used to automatically optimize robot parameters and select parts to generate effective designs, while meeting constraints of the embedded components and sub-systems. The generated designs are superior in performance and consistency when compared to designs by teams of designers using the same knowledge. Using a single representation, multiple designs are generated for four distinct lunar exploration objectives. Synergy uses the same representation to auto-generate landing simulations and simultaneously generate control software for the landing.; Synergy consists of four software agents. A database and spreadsheet agent compiles the design and component information, generating component interconnections and ensuring consistency of types, physical units and constraints. A simulation agent generates comprehensive dynamic simulations fusing several uni-dimensional agents. An optimization agent executes rules embedded in the design, finds roots for the implicitly interconnected design and searches the parameter and component space using a genetic algorithm. A control software generator learns a generalized "neural-net" controller using the simulation for feedback and a genetic algorithm to guide the search. |
