This paper presents an efficient motion planning framework for a perturbed linear system
using a minimax objective function while ensuring the safety of the system. Specifically, the …

We present a method for enhancing autonomous system capabilities by optimizing both
planning and control layers over a moving horizon. Traditional reliance on simplified models …

This paper proposes an adaptive framework for single shot motion planning (ie, planning
without preprocessing). This framework can be used in any situation, and in particular, is …

This paper presents a motion planning scheme we call Model Predictive Planning (MPP),
designed to optimize trajectories through obstacle-laden environments. The approach …

There is growing interest in applying machine learning to motion planning. Potential
applications are predicting an initial seed for trajectory optimization, predicting an effective …

High-dimensional configuration space is usually searched using sampling-based motion
planning methods. The well-known issue of sampling-based planners is the narrow passage …

This work focuses on spatial time-optimal motion planning, a generalization of the exact time-
optimal path following problem that allows a system to plan within a predefined space. In …

Motion planning as trajectory optimization requires generating trajectories that minimize a
desired objective function or performance metric. Finding a globally optimal solution is often …

We present the exploring/exploiting tree (EET) algorithm for motion planning. The EET
planner deliberately trades probabilistic completeness for computational efficiency. This …

The sampling process is very inefficient for sampling-based motion planning algorithms that
excess random samples are generated in the planning space. In this paper, we propose an …