Papers

1

Total Citations

3

H-Index

1

About

M. Aditya Sharma is a rising leader in multi-robot systems and aerial swarm trajectory optimization. His research focuses on enabling real-time, computationally efficient coordination for large teams of autonomous robots, particularly in cluttered or dynamic environments. Sharma’s most impactful contribution is his pioneering work on GPU-accelerated batch solutions for distributed multi-robot trajectory optimization. His 2022 paper, which has already garnered significant early attention, demonstrates a method that computes collision-free trajectories for tens of aerial robots in under a fraction of a second—a dramatic speedup over traditional centralized approaches. By breaking the joint optimization into smaller, decoupled sub-problems solved in parallel on a GPU, his work directly addresses the scalability bottleneck that has long limited practical deployment of aerial swarms. This innovation holds transformative potential for applications ranging from drone light shows to search-and-rescue missions. Sharma’s research sits at the intersection of optimization theory, parallel computing, and robotics, and his early citation impact signals a rapidly growing influence in the field. He is a researcher to watch for anyone interested in the future of fast, scalable multi-agent autonomy.

Research Focus

Key Achievements

1
H-Index
1
Papers
3
Total Citations
3
Avg Citations/Paper
🏆 Most Cited Paper
Fast Joint Multi-Robot Trajectory Optimization by GPU Accelerated Batch Solution of Distributed Sub-Problems
3 citations · 2022
📈 Most Prolific Year: 2022 (1 Papers)
🤝 Key Collaborators: 4
🏛 Institutions: International Institute of Information Technology, Hyderabad

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 11 days ago