TCS’ IP-backed robotics solution

TCS has created an industry-first disruptive robotics solutions suite to automate several operations in the logistics industry.

The suite comprises AMRs, pickers and packers, and robotic orchestration platforms, backed by TCS intellectual property. This is the outcome of about a decade of rigor and efforts by TCS Research and TCS Pace™ teams.

With the robotics solutions, TCS reimagines and offers as-a-service the next generation of fully automated distribution, sorting, and material handling systems, which can be repurposed for multiple segments, sizes, and commodities. 

CEP companies, e-tailers, fast-moving consumer goods companies, manufacturers, professional manpower supply companies, and third-party logistics providers have for years been grappling with hard-to-automate problems due to the inherent nature of work in semi-structured environments. Labor scarcity, cost pressures, scale and growth of volumes, and health and safety requirements are some of the challenges faced by the industry in over a decade.

The solution designed by TCS not only optimizes sorting, distribution, and the performance of fulfilment centers by automating tasks, but also helps companies realize their growth strategy via integration, orchestration, enhancing capacity and throughput, managing knowledge, and enablement of wall-to-wall processes with robotics and all-pervasive intelligence.

What is TCS AMR?

TCS AMR is an industrial-grade and multi-functional fork-over mobile robot, capable of handling different payloads for intra-logistics operations. The compact design of TCS AMR makes it easy to navigate safely through narrow paths in an autonomous manner. It comes bundled with a vendor-neutral fleet management system (FMS) to effectively manage and control a fleet of robots. TCS FMS provides seamless integration with enterprise systems such as a warehouse management system for material handling orders.

Technology driving AMR

The robots come equipped with TCS proprietary payload cage detection modules, adaptive obstacle avoidance, navigation zone definition, and special navigation behavior models for queuing or single entry and charging. The robots can also communicate with other robots in a fleet, and with humans. Digital twin technology and robot sensor data are used to plan collision-free multi-robot paths and handle dynamic obstacles in an intelligent manner.

Material handling robots, along with other robotic interventions such as singulators, 3D bin packers, sorting robots, multi-AMR collaborative transport systems for uglies (non-machinable goods), and autonomous truck loose-loading and unloading systems, create the promise of a huge industry transformation within logistics. The wall-to-wall processes within such a lights-out-sorting or last-mile center usually require integration, orchestration, and next-generation optimization and control algorithms to leverage robotics and automation effectively.

Systems with AMRs could ensure the ability to do time-definite deliveries in various segments with right prioritization across the production chain and flexible sorting to accommodate varying peak-to-average ratios of demand.

Industry transformation through AMRs

Touchless, cost-efficient, always-on warehouses or sorting centers that are energy and cost optimized—ranging from transient and shipper-adjacent ones to augmented existing centers and those with greenfield operations—could transform the cost and business models for logistics companies.

Systems with AMRs could ensure new or incremental capacity additions to logistics networks without adding more workers. It can bolster contactless operations, perform time-definite deliveries across the production chain, and carry out flexible sorting to accommodate varying peak-to-average ratios of demand. In addition, automated data collection about robot movements helps assess real-time costs and energy expenditures, and ensure accurate pricing of products and services.

Most significantly, AMRs help manage physical manpower better by placing them in tasks that call for human decision-making, leaving the heavy lifting to a robot.