  The deployment flexibility and maneuverability of Unmanned Aerial Vehicles
(UAVs) increased their adoption in various applications, such as wildfire
tracking, border monitoring, etc. In many critical applications, UAVs capture
images and other sensory data and then send the captured data to remote servers
for inference and data processing tasks. However, this approach is not always
practical in real-time applications due to the connection instability, limited
bandwidth, and end-to-end latency. One promising solution is to divide the
inference requests into multiple parts (layers or segments), with each part
being executed in a different UAV based on the available resources.
  Furthermore, some applications require the UAVs to traverse certain areas and
capture incidents; thus, planning their paths becomes critical particularly, to
reduce the latency of making the collaborative inference process. Specifically,
planning the UAVs trajectory can reduce the data transmission latency by
communicating with devices in the same proximity while mitigating the
transmission interference.
  This work aims to design a model for distributed collaborative inference
requests and path planning in a UAV swarm while respecting the resource
constraints due to the computational load and memory usage of the inference
requests. The model is formulated as an optimization problem and aims to
minimize latency. The formulated problem is NP-hard so finding the optimal
solution is quite complex; thus, this paper introduces a real-time and dynamic
solution for online applications using deep reinforcement learning. We conduct
extensive simulations and compare our results to the-state-of-the-art studies
demonstrating that our model outperforms the competing models.
