A5018413983- Robotic Locomotion and Control
- Robotic Mechanisms and Dynamics
- Robotic Path Planning Algorithms
- Control and Dynamics of Mobile Robots
- Hydraulic and Pneumatic Systems
- Soil Mechanics and Vehicle Dynamics
- Biomimetic flight and propulsion mechanisms
- Vehicle Dynamics and Control Systems
- Dynamics and Control of Mechanical Systems
- Real-time simulation and control systems
University of Illinois Urbana-Champaign
2018-2021
This paper presents a novel Representation-Free Model Predictive Control (RF-MPC) framework for controlling various dynamic motions of quadrupedal robot in three dimensional (3D) space. Our formulation directly represents the rotational dynamics using rotation matrix, which liberates us from issues associated with use Euler angles and quaternion as orientation representations. With variation-based linearization scheme carefully constructed cost function, MPC control law is transcribed to...
Model predictive control (MPC) is a popular strategy for controlling robots but difficult systems with contact due to the complex nature of hybrid dynamics. To implement MPC contact, dynamic models are often simplified or sequences fixed in time order plan trajectories efficiently. In this work, we propose iterative linear quadratic regulator (iLQR) (HiLQR), which extends iLQR class piecewisesmooth dynamical state jumps. This accomplished by, first, allowing changing modes forward pass,...
This paper proposes a kinodynamic motion plan-ning framework for multi-legged robot jumping based on the mixed-integer convex program (MICP), which simultaneously reasons about centroidal motion, contact points, wrench, and gait sequences. method uniquely combines configuration space discretization construction of feasible wrench polytope (FWP) to encode kinematic constraints, actuator limit, friction cone constraint, sequencing into single MICP. The MICP could be efficiently solved global...
This paper proposes a mixed-integer convex programming formulation for dynamic motion planning. Many constraints such as the actuator torque constraint are nonlinear and non-convex due to trigonometrical terms from Jacobian matrix. often causes optimization problem converge local optima or even infeasible set. In this paper, we convexify by formulating quadratically-constrained program (MIQCP). More specifically, workspace is discretized into union of disjoint polytopes enforced upon outer...
This paper proposes a hybrid planning framework that generates complex dynamic motion plans for jumping legged robots to traverse challenging terrains. By employing primitive, the original problem is decoupled as path followed by trajectory optimization (TO) module handles dynamics. A variant of kinodynamic Rapidly-exploring Random Trees (RRT) planner finds parabola sequence between stance phases. To make this fast, reachability informed control sampling scheme leverages precomputed velocity...
Model Predictive Control (MPC) is a popular strategy for controlling robots but difficult systems with contact due to the complex nature of hybrid dynamics. To implement MPC contact, dynamic models are often simplified or sequences fixed in time order plan trajectories efficiently. In this work, we extend Hybrid iterative Linear Quadratic Regulator work fashion (HiLQR MPC) by 1) modifying how cost function computed when modes do not align, 2) utilizing parallelizations simulating rigid body...