Scaling the PLBA framework to large-scale multi-robot systems presents several challenges, particularly in complex and unstructured environments:
Computational Complexity: As the number of robots increases, the computational demands for real-time preference learning and behavior adjustment also rise. The multi-output Gaussian Processes used in PLBA may become computationally expensive, leading to delays in decision-making. Efficient algorithms and approximations will be necessary to maintain real-time performance across a larger fleet.
Communication Overhead: In large-scale deployments, the communication between robots and between robots and human operators can become a bottleneck. Ensuring reliable and timely communication is crucial for effective coordination and preference sharing. Strategies to minimize communication overhead, such as local decision-making and selective information sharing, will be essential.
Heterogeneity of Robots: Large-scale MRS often consists of heterogeneous robots with varying capabilities and roles. Adapting the PLBA framework to accommodate different types of robots while ensuring cohesive behavior can be challenging. The framework must be flexible enough to account for these differences in capabilities and preferences.
Environmental Variability: Complex, unstructured environments can exhibit significant variability, making it difficult to generalize learned preferences across different scenarios. The PLBA framework must be robust enough to handle this variability and quickly adapt to new environmental conditions without extensive retraining.
Scalability of Human Interaction: As the number of robots increases, the need for human oversight and interaction may also grow. Balancing the level of human involvement with the autonomy of the robots is critical. The PLBA framework must find ways to efficiently incorporate human feedback without overwhelming operators, potentially through automated preference elicitation techniques.
Safety and Coordination: Ensuring the safety of multiple robots operating in close proximity is a significant challenge. The PLBA framework must incorporate safety constraints and coordination mechanisms to prevent collisions and ensure that robots can work together effectively in dynamic environments.