Jessica Leu | Motion Planning

Motion Planning Algorithm and Analysis

Motion planning is one of the most crucial factors to realize autonomy in real-world applications. These research projects focus on developing new motion planning algorithms and evaluating closed-loop/ open-loop motion planning performance.

Motion Planning for Industrial Mobile Robots with Closed-loop Stability Enhanced Prediction

We discuss necessary conditions for the closed-loop stability of a planning problem using the framework of model predictive control (MPC). It is concluded that the predictor needs to be able to detect the workers’ movement mode change within a time delay allowance and the MPC needs to have a sufficient prediction horizon and a proper cost function.

Efficient Robot Motion Planning via Sampling and Optimization

This work presents a benchmark which implements and compares existing planning algorithms on a variety of problems with extensive simulations. Based on that, we also propose a hybrid planning algorithm, RRT*-CFS, that combines the merits of sampling-based planning methods and optimization-based planning methods.

Motion Planning for Mobile Manipulators

Mobile manipulators, having both mobility from the platform and agility from the manipulator, have become a promising solution to bringing autonomy to the real-world. These research projects focus on motion planning for mobile manipulators in dynamic environment where obstacles are moving in the surroundings, or even uncertain environment where there are unknown factors (e.g., unseen obstacles) that will affect the robot performance.

Safe and Coordinated Hierarchical Receding Horizon Control for Mobile Manipulators

This paper presents a method, hierarchical receding horizon control algorithm (HRHC), to assure safety and a chieve higher time and space efficiency in robots surrounded by time-varying environments. HRHC contains an optimization based motion planning module that takes account of both the mobile platform and the manipulator to utilize the kinematic redunda ncy, and a low-level safety controller to deal with fast changes in the environment.

Motion Planning for Mobile Manipulators with Physical Contact in Uncertain Environment

We formulate the uncertainty-exploring motion planning as a partially observable Markov decision process (POMDP) with an additional reward term to encourage uncertainty exploration. We also present a hybrid optimization algorithm, namely, the Hamiltonian Monte Carlo sampling with convex feasible set algorithm (HMCCFS), to solve the POMDP efficiently.