Synergy: A Language and Framework for Robot Design

Abstract

Due to escalation in complexity, capability and application, robot design is increasingly difficult. A design environment can automate many design tasks and relieve the designer's burden. Prior to robot development. designers usually 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 tools ranging from spreadsheets to C code to CAD tools. Valuable resoiirces 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 effecliveness 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 dcsign. 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 designs so generated 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 siniulations 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 pararneter 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.