Suning Shang

About

Suning Shang is a leading researcher in multi-robot systems, with a focus on scalable task allocation, collaborative perception, and cloud-enabled autonomy. Their seminal work, "Balanced connected task allocations for multi-robot systems" (2018, 62 citations), introduced an exact flow-based integer program and an approximate tree-based genetic algorithm, solving a critical challenge in distributing tasks among robots while maintaining connectivity—a breakthrough for real-world deployments. Shang also advanced multi-robot SLAM with their 2018 paper "Cloud-Based Framework for Scalable and Real-Time Multi-Robot SLAM" (40 citations), addressing the computational bottleneck of coordinating dozens of robots simultaneously. By leveraging cloud infrastructure, Shang enabled real-time, large-scale collaborative mapping, pushing the boundaries of what was previously thought feasible. Additionally, their 2017 work on "Cloud-Based Knowledge Sharing in Cooperative Robot Tracking of Multiple Targets with Deep Neural Network" explored how deep learning and cloud resources can enhance multi-target tracking. With over 100 citations across their top works, Shang’s contributions are foundational for researchers and engineers building resilient, scalable multi-robot teams for search-and-rescue, environmental monitoring, and industrial automation.

Research Focus

Key Achievements

Top Papers

1. 1
   Balanced connected task allocations for multi-robot systems: An exact flow-based integer program and an approximate tree-based genetic algorithm

   62 citations · 2018
2. 2
   Cloud-Based Framework for Scalable and Real-Time Multi-Robot SLAM

   40 citations · 2018
3. 3
   Cloud-Based Knowledge Sharing in Cooperative Robot Tracking of Multiple Targets with Deep Neural Network

   3 citations · 2017

Key Collaborators

Contact & Links