Tata Consultancy Services Limited

Marichi Agarwal

Chayan Sarkar

Sounak Dey

Himadri Sekhar Paul

Rajesh Kumar Sinha

Swagata Biswas

Aniruddha Singhal

Sayan Paul

Ankush Ojha

Supratim Ghosh

Online 3-dimensional bin packing problem (O3D-BPP) is getting renewed prominence due to the industrial automation brought by Industry 4.0. However, due to limited attention in the past and its challenging nature, a good approximate technique is in scarcity as compared to 1D or 2D problems. Present disclosure provides system and method that considers real-time O3D-BPP of cuboidal boxes with partial information (look-ahead) in an automated robotic sorting center. System presents two rolling-horizon mixed-integer linear programming (MILP) cum-heuristic based algorithms wherein a framework is provided that adapts and improves performance of BP heuristics by utilizing information in an online setting with look-ahead.

Automated placement of objects in bins using look ahead information by virtual sorting and packing

Any technical error with robotic arms that are used to automatically perform object packing can affect quality and efficiency with which the packing is being carried out, and this in turn affects space utilization when a large quantity of objects are to be accommodated in tight packing spaces. This disclosure relates generally to automated object packing and more specifically to an object packing mechanism in which corrections are made when placement of object is identified as violating one or more regulations. The system packs objects by calculating ICP-BCP pairs for each empty space in a packing space. After packing each object, the system checks whether placement of the object violates one or more regulations, and if any violation is found, then the system determines and executes one or more corrective action to correct placement of the object that violates the regulation.

Method and system for automated object packing

The systems and methods provide for optimizing the scheduling of non-preemptive tasks in the multi-robot environment. The embodiment provides for scheduling, by implementing an Online Minimum Performance Loss Scheduling (OMPLS) technique, initially, tasks with a higher performance loss value and then secondly, tasks that can be scheduled within their deadline and having a low performance loss value amongst the merged tasks; and finally minimizing, a performance loss value of a remaining subset of tasks that cannot be scheduled within a pre-defined deadline. Moreover, the embodiments herein further provides executing the priority update on each of the remaining subset of tasks for optimizing the scheduling of the non-pre-emptive tasks.

Systems and methods for optimizing scheduling of non-preemptive tasks in multi-robotic environment

Parameters specific to robot, environment, target objects and their inter-relations need to be considered by a robot to estimate cost of a task. As the existing task allocation methods assume a single utility value for a robot-task pair, combining heterogeneous parameters is a challenge. In applications like search and rescue, manual intervention may not be possible in real time. For such cases, utility calculation may be a hindrance towards automation. Also, manufacturers follow their own nomenclature and units for robotic specifications. Only domain experts can identify semantically similar terms and perform necessary conversions. Systems and methods of the present disclosure provide a structured semantic knowledge model to store and describe data in a uniform machine readable format such that semantics of those data can be interpreted by the robots and utility computation can be autonomous to make task allocation autonomous, semantic enabled and capable of self-decision without human intervention.

Systems and methods for semantic knowledge based dynamic utility calculation

System and method is provided for scheduling of a set of non-preemptive tasks by partitioning, the set of non-preemptive tasks either as a set of schedulable tasks or as a set of non-schedulable tasks; sorting, by a scheduling technique, the set of non-preemptive tasks partitioned; determining, by the scheduling technique, a possibility of execution of each of the set of schedulable tasks; and scheduling the set of schedulable tasks and the set of non-schedulable tasks upon determining the possibility of execution of each of the set of schedulable tasks.

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Kolkata, India

Marichi Agarwal