481 research outputs found
Recent Advances in Path Integral Control for Trajectory Optimization: An Overview in Theoretical and Algorithmic Perspectives
This paper presents a tutorial overview of path integral (PI) control
approaches for stochastic optimal control and trajectory optimization. We
concisely summarize the theoretical development of path integral control to
compute a solution for stochastic optimal control and provide algorithmic
descriptions of the cross-entropy (CE) method, an open-loop controller using
the receding horizon scheme known as the model predictive path integral (MPPI),
and a parameterized state feedback controller based on the path integral
control theory. We discuss policy search methods based on path integral
control, efficient and stable sampling strategies, extensions to multi-agent
decision-making, and MPPI for the trajectory optimization on manifolds. For
tutorial demonstrations, some PI-based controllers are implemented in MATLAB
and ROS2/Gazebo simulations for trajectory optimization. The simulation
frameworks and source codes are publicly available at
https://github.com/INHA-Autonomous-Systems-Laboratory-ASL/An-Overview-on-Recent-Advances-in-Path-Integral-Control.Comment: 16 pages, 9 figure
Multi-Robot-Assisted Human Crowd Evacuation using Navigation Velocity Fields
This work studies a robot-assisted crowd evacuation problem where we control
a small group of robots to guide a large human crowd to safe locations. The
challenge lies in how to model human-robot interactions and design robot
controls to indirectly control a human population that significantly outnumbers
the robots. To address the challenge, we treat the crowd as a continuum and
formulate the evacuation objective as driving the crowd density to target
locations. We propose a novel mean-field model which consists of a family of
microscopic equations that explicitly model how human motions are locally
guided by the robots and an associated macroscopic equation that describes how
the crowd density is controlled by the navigation velocity fields generated by
all robots. Then, we design density feedback controllers for the robots to
dynamically adjust their states such that the generated navigation velocity
fields drive the crowd density to a target density. Stability guarantees of the
proposed controllers are proven. Agent-based simulations are included to
evaluate the proposed evacuation algorithms
Controlled Lagrangians and the stabilization of mechanical systems. II. Potential shaping
For pt.I, see ibid., vol.45, p.2253-70 (2000). We extend the method of controlled Lagrangians (CL) to include potential shaping, which achieves complete state-space asymptotic stabilization of mechanical systems. The CL method deals with mechanical systems with symmetry and provides symmetry-preserving kinetic shaping and feedback-controlled dissipation for state-space stabilization in all but the symmetry variables. Potential shaping complements the kinetic shaping by breaking symmetry and stabilizing the remaining state variables. The approach also extends the method of controlled Lagrangians to include a class of mechanical systems without symmetry such as the inverted pendulum on a cart that travels along an incline
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