An agent-based approach for energy-efficient sensor networks in logistics

As part of the fourth industrial revolution, logistics processes are augmented with connected information systems to improve their reliability and sustainability. Above all, customers can analyse process data obtained from the networked logistics operations to reduce costs and increase margins. The logistics of managing liquid goods is particularly challenging due to the strict transport temperature requirements involving monitoring via sensors attached to containers. However, these sensors transmit much redundant information that, at times, does not provide additional value to the customer, while consuming the limited energy stored in the sensor batteries. This paper aims to explore and study alternative approaches for location tracking and state monitoring in the context of liquid goods logistics. This problem is addressed by using a combination of data-driven sensing and agent-based modelling techniques. The simulation results show that the longest life span of batteries is achieved when most sensors are put into sleep mode yielding an increase of ×21.7 and ×3.7 for two typical routing scenarios. However, to allow for situations in which high quality sensor data is required to make decisions, agents need to be made aware of the life cycle phase of individual containers. Key contributions include (1) an agent-based approach for modelling the dynamics of liquid goods logistics to enable monitoring and detect inefficiencies (2) the development and analysis of three sensor usage strategies for reducing the energy consumption, and (3) an evaluation of the trade-offs between energy consumption and location tracking precision for timely decision making in resource constrained monitoring systems

Design of a Deep Post Gripping Perception Framework for Industrial Robots

The use of flexible and autonomous robotic systems is a possible solution for automation in dynamic and unstructured industrial environments. Pick and place robotic applications are becoming common for the automation of manipulation tasks in an industrial context. This context requires the robot to be aware of its surroundings throughout the whole manipulation task, even after accomplishing the gripping action. This work introduces the deep post gripping perception framework, which includes post gripping perception abilities realized with the help of deep learning techniques, mainly unsupervised learning methods. These abilities help robots to execute a stable and precise placing of the gripped items while respecting the process quality requirements. The framework development is described based on the results of a literature review on post gripping perception functions and frameworks. This results in a modular design using three building components to realize planning, monitoring and verifying modules. Experimental evaluation of the framework shows its advantages in terms of process quality and stability in pick and place applications

An agent-based approach for energy-efficient sensor networks in logistics

As part of the fourth industrial revolution, logistics processes are augmented with connected information systems to improve their reliability and sustainability. Above all, customers can analyse process data obtained from the networked logistics operations to reduce costs and increase margins. The logistics of managing liquid goods is particularly challenging due to the strict transport temperature requirements involving monitoring via sensors attached to containers. However, these sensors transmit much redundant information that, at times, does not provide additional value to the customer, while consuming the limited energy stored in the sensor batteries. This paper aims to explore and study alternative approaches for location tracking and state monitoring in the context of liquid goods logistics. This problem is addressed by using a combination of data-driven sensing and agent-based modelling techniques. The simulation results show that the longest life span of batteries is achieved when most sensors are put into sleep mode yielding an increase of ×21.7 and ×3.7 for two typical routing scenarios. However, to allow for situations in which high quality sensor data is required to make decisions, agents need to be made aware of the life cycle phase of individual containers. Key contributions include (1) an agent-based approach for modelling the dynamics of liquid goods logistics to enable monitoring and detect inefficiencies, (2) the development and analysis of three sensor usage strategies for reducing the energy consumption, and (3) an evaluation of the trade-offs between energy consumption and location tracking precision for timely decision making in resource constrained monitoring systems