Cellular-enabled collaborative robots are becoming paramount in Search-and-Rescue (SAR) and emergency response. Crucially dependent on resilient mobile network connectivity, they serve as invaluable assets for tasks like rapid victim localization and the exploration of hazardous, otherwise unreachable areas. However, their reliance on battery power and the need for persistent, low-latency communication limit operational time and mobility. To address this, and considering the evolving capabilities of 5G/6G networks, we propose a novel SAR framework that includes Mission Planning and Mission Execution phases and that optimizes robot deployment. By considering parameters such as the exploration area size, terrain elevation, robot fleet size, communication-influenced energy profiles, desired exploration rate, and target response time, our framework determines the minimum number of robots required and their optimal paths to ensure effective coverage and timely data backhaul over mobile networks. Our results demonstrate the trade-offs between number of robots, explored area, and response time for wheeled and quadruped robots. Further, we quantify the impact of terrain elevation data on mission time and energy consumption, showing the benefits of incorporating real-world environmental factors that might also affect mobile signal propagation and connectivity into SAR planning. This framework provides critical insights for leveraging next-generation mobile networks to enhance autonomous SAR operations.
Set of time instants; index t refers to time instant tt
R={r1,…,r∣R∣}
Set of robots rr ; index r refers to robot rr
A×B
Geometric dimensions of the area of interest
S={sa,b,∀(a,b)∈(A,B)}
Surface grid representing the area to be explored
lr,t,a,b
Binary decision variable indicating whether robot r is in position ga,b at time instant t
et,a,b
Binary variable indicating whether the unit of area ga,b has been explored at time t
Table 1: Model parameters
Figure 2: Enhancing mission planning through GNSS data pipeline.
Figure 3: Robots evaluated in the SAR Framework.
Category
Consumption Element
Avg. Consumption (W)
Components
Cellular Peripheral
15.77
Cameras and Nano Proc.
19.25
Human Recognition
29.38
3D LiDAR and SLAM
56.84
Mobility
Idle Down
21.62
Flexing Down
75.79
Table 2: Power consumption breakdown of the GO1 robot.
Figure 4: GO1 robot power consumption over different slopes scenarios until battery discharge
Slope Scenario
Power Consumption (W)
-11º
91.01
-5.3º
122.87
0º
142.95
5.3º
160.35
11º
203.38
Table 3: GO1 robot average Power Consumption by slope scenario at 1 m/s
Avg. Consumption (W)
Consumption Rate (%)
Quadruped
Wheeled
Quadruped
Wheeled
Cellular Reception
15.77
4.00
4.75%
13.97%
Cellular Transmission
16.72
4.95
5.04%
17.28%
Camera, LiDAR, Processor
76.09
12.00
22.93%
41.90%
Idle Up / Idle
80.33
0.29
24.20%
1.01%
Motion (1 m/s)
142.95
7.40
43.08%
25.84%
Table 4: Power consumption comparison.
Figure 5: Visualization of the two considered uneven scenarios. Subfigures (a) and (b) illustrate the geographical location and the corresponding derived 3D terrain models for each scenario.
Figure 6: Percentage of Explored Area per Time Epoch for a 50x50 \mboxm2 scenario. Wheeled versus Quadruped Robots.
Figure 7: Percentage of Explored Area per Time Epoch for a 500x500 \mboxm2 scenario. Wheeled versus Quadruped Robots.
Figure 8: Percentage of Explored Area per Time Epoch for a 50x50 m 2 scenario using a wheeled robot fleet.
Figure 9: Terrain results using one wheeled robot with Gazebo simulations.
Figure 10: Aggregated motion energy consumption and elevation results using a quadruped GO1 robot with Gazebo simulations.
Figure 11: Motion energy consumption over percentage of explored area of quadruped and wheeled robots in three different scenarios.
Department of Mechanical and Industrial Engineering, University of Illinois Chicago, IL, 60607 USA · DEVCOM Army Research Laboratory, Minneapolis, Minnesota, USA · DEVCOM Army Research Laboratory, Aberdeen Proving Grounds, Aberdeen, MD 21005 USA
Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639956