3 accepted papers
To deploy safe and agile robots in cluttered environments, there is a need to develop fully decentralized controllers that guarantee safety, respect actuation limits, prevent deadlocks, and scale to thousands of agents. Current approaches fall short of meeting all these goals: optimization-based met
In this paper, we address the problem of reducing the computational burden of Model Predictive Control (MPC) for real-time robotic applications. We propose TransformerMPC, a method that enhances the computational efficiency of MPC algorithms by leveraging the attention mechanism in transformers for
We present an online motion planning algorithm (3D-OGSSE) for generating smooth, collision-free trajectories over multiple planning iterations for a 3-D agent operating in an unknown, obstacle-cluttered, 3-D environment. In each planning iteration, 3D-OGSSE constructs an obstacle-free region termed