I am working with Dr. Aamir Ahmad on the problem of Multi-Robot Obstacle Avoidance for Target Tracking Scenarios using Model-Predictive Optimization.
We consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning
algorithm which is based on model predictive control (MPC).
Our motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking.
Multi-Robot Systems Multi-Robot Control Motion Planning
Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems