Fasih Munir Malik
Topic
Novel Multimodal Human-Machine Interface for Enabling Shared
Control in Machine fleet operation
Currently working on:
Conducting User studies to validate the concept of one man multiple machines
The future of industrial automation depends on the ability of human operators to effectively manage fleets of increasingly autonomous machines. Traditionally, fleet operations have required a large workforce, but advancements in machine autonomy are enabling a shift toward single-operator control of multi-machine systems. However, this transition introduces new challenges related to cognitive workload, situational awareness, and safety of the operator.
This research validates the concept of fleet operation through user studies, focusing on productivity changes and human factors such as cognitive workload and situational awareness. A multimodal Human-Machine Interface (HMI) will be developed that can support safe and efficient supervision of multiple mobile machines. The study emphasizes adaptive autonomy and shared control, ensuring smooth collaboration between humans and machines under varying operational conditions.
The proposed approach is expected to significantly enhance operator performance, safety, and overall system productivity. By improving situational awareness and reducing cognitive strain, the research aims to establish guidelines for effective human-machine collaboration in complex multi-machine systems. The resulting prototype HMI framework will advance the digital transformation of industries such as construction, mining, and logistics.
Current approaches in work machines each have major limitations. Manual operation exposes operators to physical risks and faces growing workforce shortages. Remote operation, while safer, often results in a significant drop in productivity. Fully autonomous systems remain expensive and are not always suitable for dynamic environments. Semi-autonomous solutions, on the other hand, frequently leave operators idle for extended periods, reducing engagement and efficiency. The proposed approach counters these problems by enabling true human-machine teaming—balancing autonomy with human supervision to achieve safe, efficient, and adaptable fleet operation.