Tata Consultancy Services Limited

Arindam Pal

Subhra Mazumdar

Arpan Pal

Avik Ghose

Arijit Mukherjee

Tanushyam Chattopadhyay

Chayan Sarkar

Himadri Sekhar Paul

Venkatramanan Siva Subrahmanian

Santa Maiti

Francesco Parisi

Gopinath Mishra

Systems and methods of the present disclosure address the capacity constrained vehicle routing (CVRP) problem that may be applied to a warehouse scenario wherein multi-robot task allocation is required. Conventional methods can solve CVRP instances up to 100 nodes. In the present disclosure, a nearest-neighbor based Clustering And Routing (nCAR) approach is provided that makes the systems and methods of the present disclosure scalable wherein the number of nodes can be in the range of several hundreds to several thousands within an order wave.

Systems and methods for scalable multi-vehicle task allocation

A multi-dimensional sensor data analysis system and method is provided. The multi-dimensional sensor data analysis system receives indoor and outdoor location, online and physical activity, online and physical proximity and additional a plurality of inputs (specific to a user), for example, surrounding of the subject, physiological parameters of the subject and recent social status of the subject, both online and offline. The multi-dimensional sensor data analysis system processes these inputs along with the knowledge of past behavior and traditional parameters of location, proximity and activity by performing a multi-dimensional sensor data analysis fusion technique, producing one or more outputs, for example, predicting or determining a human behaviour to a given stimuli.

Multi-dimensional sensor data based human behaviour determination system and method

Systems and methods for planning location-sensitive probabilistic behavior based evacuation paths, in a building, from source nodes to sink nodes in a network of routes including a plurality of vertices and edges are disclosed. For example, input parameters including layouts, number of evacuees at each source node, transit time, predetermined time period and maximum capacity associated with each edge and vertex are received. Further, weak evacuation schedules at a state of the evacuees are defined based on the layouts of the building, number of evacuees, transit time and predetermined time period. Furthermore, a strong evacuation schedule is defined based on the weak evacuation schedules and maximum capacity associated with each edge and vertex. Moreover, a mapping is defined from the strong evacuation schedule to the weak evacuation schedules to obtain a probabilistic behavior model. Also, an evacuation path is planned, in real-time, based on the probabilistic behavior model.

Systems and methods for planning location-sensitive probabilistic behavior based evacuation paths

Methods and systems for planning evacuation paths are provided to identify an optimum path for evacuation of every evacuee from a region of interest. Methods of the instant disclosure ensure that for each suggested evacuation path, the capacity of any edge on the path is not exceeded and the evacuation time is minimum. A path once identified is maintained. A randomized behavior model is employed to re-distribute evacuees in emergency situations. This provides optimum evacuation time that employs an improved technique with optimized run time and evacuation time after taking into consideration herd behavior.

Methods and systems for planning evacuation paths

Growing community of inventors

Kolkata, India

Arindam Pal