– Sharp ideas for the software side of robotics

Sharp ideas for the software side of robotics

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National Robotics Initiative (NRI) for Project Funding

If you missed out on last year’s Robotics Technology Development and Deployment funding opportunity and can’t quite find a good funding match for your project from other funding sources, you may be interested to know about the National Robotics Initiative (NRI), which Obama announced this morning while touring CMU’s Robotics Institute. From the NRI program solicitation: […]

Using State Diagrams to Express Robot Behaviors & Transitions

In the last post, I wrote briefly on how the goal modeling task – described by the O-MaSE agent-oriented design methodology – is useful for laying out a birds eye view of what you want your (multi-agent) robot(s) to accomplish.  The simple act of decomposing high level goals into sub-goals goes a long way towards […]

Using O-MaSE Methodology for Goal Modeling

O-MaSE is an agent-oriented analysis & design (AOAD) methodology.  I have found it to be a particularly powerful tool, in the development of project work, due to its flexibility of use and ability to handle single and multi-agent domains equally well.  For example, unlike many other methodologies (which I’ll leave nameless to avoid stoking any fires), O-MaSE […]

Planning-Domain Research Guidance

In business software development, the more experienced a developer becomes, the less – he realizes – he knows.  The only developers I know, for the most part, who believe they’re truly gurus in their profession have around three years experience…after that comes a few years of realizing that they’ve only seen the tip of the […]

STRIPS for Classical Plan Representation and Planning

Planning concerns itself with determining future actions to achieve goals.  One, albeit naïve, approach to planning is to explicitly represent plans via ifs and for loops, laying out the sequential via low-level code.  But for better flexibility and scalability, if warranted for the task at hand, it’s preferred to represent plans less rigidly; plans that […]

InteRRaP Hybrid Architecture for Robotic & Multi-Agent Systems

Over the years, a number of well defined architectures have been proposed for a wide assortment of project domains; a few examples were described in Architectural Paradigms of Robotic Control.  As described, the architectural approaches can typically be categorized as follows: Reactive architectures which emphasize reacting to the immediate world environment without keeping an internal […]

Agent-Oriented Methodology Selection: O-MaSE

In selecting an appropriate agent-oriented analysis & design (AOAD) methodology, the project delivery team must carefully consider the ramifications of the methodology on the final solution and if the methodology’s presumed architectural paradigm, if there is one, is in line with the team’s goals for the project.  With traditional object-oriented analysis & design (OOAD) methodologies – […]

Methodologies for the Design of Robotic and Multi-Agent Systems

Object oriented analysis and design (OOAD) methodologies are well established, offering a plethora of methodologies depending on the needs of the project and the skills of the development team; e.g., eXtreme Programming, Domain Driven Design, Agile Modeling, and Crystal Clear.  (Scrum is omitted as it lacks adequate direction for OOAD; albeit, it’s a terrific project […]

Development of Multi-Agent Bell Pepper Picking Robot in Unstructured Horticulture

Automated Planning, Dominant Orientation Templates, Differential Evolution, Locally Adaptive Regression Kernels, Elastic Band Path Optimization, Open Motion Planning, Policy Learning, SLAM, , and other niche algorithms are interesting to learn in isolation, but at some point it’s time to put the puzzle pieces together and produce something useful.  For the past few months, I’ve been formulating a […]

Simulation as a Source of New Knowledge

An argument that interests me is the debate concerning what value simulations provide for researching new ideas and garnering new knowledge.  As Herbert Simon succinctly puts the argument in The Sciences of the Artificial, “How can a simulation ever tell us anything that we do not already know?”  The arguments backing this skeptic’s question are that […]

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