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Convergence Toward Ubiquitous RAS Product Deployment

– Ching Hu, Principal Systems Architect at SiMa.ai

RASThough the number of robotics and autonomous system (RAS) products continues to climb, there are still many areas needing further development before RAS deployments become pervasive. One such area is the transition from automated systems to autonomous systems. Here we will take a look at some of the critical facets of RAS products that need to be addressed to help their widespread adoption and commercialization. Also highlighted in this article, we will discuss the machine learning technology that will certainly aid in the effort.

  1. Development costs depend on availability and affordability of resources. This includes the availability and maturity of an affordable talent pool along with hardware, software, and system integration tools. Currently there is a shortage of machine learning experts capable of using these complex tools. To mitigate this talent shortage, the software and hardware tools need to be very easy to use and flexible enough to support a variety of ML networks, frameworks and operators.
  2. Production costs, driven primarily by supply chain costs, need to be low enough to support pricing that the market will bear. The production cost curve hits an inflection point as volumes increase, which drive down unit costs along with increasing alternative options available within the supply chain.
  3. Performance efficiency needs to meet a minimum efficiency threshold, which is highly dependent on algorithm maturity and hardware performance efficiency. Algorithm maturity is achieved when the algorithms have converged enough to transition out of the R&D phase into production with extended coverage in many domains. Hardware performance efficiency is achieved when there are more aggregated functions to support the autonomy stack with lower SWaP-C (size, weight, power, and cost) that supports the desired computation capacity. Performance efficiency is tightly correlated to the level of technology integration as efficient algorithms will have poor performance if the wrong hardware is used. To prevent this poor performance, hardware-software co-design is required for complex development in the machine learning domain.
  4. Operational efficiency has multiple subdimensions that affect performance. The subdimensions common to most autonomous systems are (1) Cloud backend service that supports the mission or operations; (2) Communication backbone or infrastructure that communicates with fielded autonomous systems; (3) Deployment infrastructure to interface between OEM production and deployment to provide system updates; (4) Service ecosystem ensuring a scalable and sustainable operation for autonomous operations and; (5) Safety for humans interacting with the RAS.
  5. Technology integration is tightly correlated to performance efficiency. The technology is defined to integrate both software and hardware, making it critical to have software-hardware co-design for more complex functional integration. Hardware integration, commonly referred to as SoC (System on Chip), integrates commonly used functions for ML, computer vision or perception, and image signal processing. Software integration should be tightly coupled to the SoC to provide additional design capabilities to enable further integration into the overall system software design. Most of the autonomous systems in development are primarily running on software.
  6. Public infrastructure integration to support autonomous systems lags behind due to economic incentive and necessity. Until there is a critical mass of demand that drives supply, the infrastructure piece will not be broadly available.
  7. Depth of regulation of autonomous systems early on is more to control high-level goals with very little technical regulation due to rapidly changing technology and lack of understanding or deep insights into social impacts.

Progress and innovation are often built upon existing technologies. The trend towards moving from automated operation to fully autonomous operation has driven the need for convergence of multi-domain expertise and integration into a RAS platform. Not only is it a convergence of technologies, but also engineering talents from multiple domains coming together to innovate. Some of the key subsystems to build an autonomous vehicle include high spectral range sensors with a wide field of view and long ranges; sensor data fusion subsystems; localization and mapping subsystems; perception and prediction; and path planning and intelligent decision-making. All of the above items contain elements from different industries which ultimately work together.

So, what is the vision of the future? If artificial intelligence is viewed as the “spirit” of an autonomous system, then machine learning is a key building block towards true general intelligence. Machine learning at the edge is where this transition will happen. SiMa.ai’s purpose-built MLSoC™ platform and associated SDK provide the foundational elements to enable innovative customers to address the capabilities required to accelerate the transition into the market. By helping enable this transition, SiMa.ai is paving the way to accelerate adoption of autonomous systems so the world can enjoy the great benefits of these ML-enabled RAS products much sooner. Visit SiMa.ai for more information.

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