An Iterative LQR Controller for Off-Road and On-Road Vehicles using a Neural Network Dynamics Model

• URL: http://arxiv.org/abs/2007.14492v1
• Date: Tue, 28 Jul 2020 21:20:01 GMT
• Title: An Iterative LQR Controller for Off-Road and On-Road Vehicles using a Neural Network Dynamics Model
• Abstract summary: Iterative Linear Quadratic Regulator(ILQR) for trajectory tracking of two different kinds of wheeled mobile robots namely Warthog and Polaris GEM. We use multilayer neural network to learn the discrete dynamic model of these robots which is used in ILQR controller to compute the control law.
• Score: 13.363673186237284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work we evaluate Iterative Linear Quadratic Regulator(ILQR) for trajectory tracking of two different kinds of wheeled mobile robots namely Warthog (Fig. 1), an off-road holonomic robot with skid-steering and Polaris GEM e6 [1], a non-holonomic six seater vehicle (Fig. 2). We use multilayer neural network to learn the discrete dynamic model of these robots which is used in ILQR controller to compute the control law. We use model predictive control (MPC) to deal with model imperfections and perform extensive experiments to evaluate the performance of the controller on human driven reference trajectories with vehicle speeds of 3m/s- 4m/s for warthog and 7m/s-10m/s for the Polaris GEM

Related papers

• A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
  Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
  arXiv  Detail & Related papers  (2026-08-11T01:51:08Z)
• How to Model Your Crazyflie Brushless [50.54424040768484]
  In early 2025, the Crazyflie Brushless was introduced, featuring brushless motors that provide around 50% more thrust.<n>This work presents a dynamics model of the Crazyflie Brushless and identifies its key parameters.<n>We demonstrate its suitability for reinforcement learning applications by training an end-to-end neural network position controller and learning a backflip controller.
  arXiv  Detail & Related papers  (2026-03-06T06:21:39Z)
• Adaptive Model-Predictive Control of a Soft Continuum Robot Using a Physics-Informed Neural Network Based on Cosserat Rod Theory [2.7114475063296455]
  This work introduces a real-time-capable nonlinear model-predictive control (MPC) framework for SCRs based on a domain-decoupled physics-informed neural network (DD-PINN)<n>In simulation, it demonstrates accurate tracking of dynamic trajectories and setpoint control with end-effector position errors below 3 mm.<n>In real-world experiments, the controller achieves similar accuracy and accelerations up to 3.55 m/s2.
  arXiv  Detail & Related papers  (2025-08-18T07:24:36Z)
• Planning with Adaptive World Models for Autonomous Driving [50.4439896514353]
  Motion planners (MPs) are crucial for safe navigation in complex urban environments. nuPlan, a recently released MP benchmark, addresses this limitation by augmenting real-world driving logs with closed-loop simulation logic. We present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on BehaviorNet's predictions.
  arXiv  Detail & Related papers  (2024-06-15T18:53:45Z)
• TEDNet: Twin Encoder Decoder Neural Network for 2D Camera and LiDAR Road Detection [2.8038082486377114]
  A novel Convolutional Neural Network model is proposed for the accurate estimation of the roadway surface. Our model is based on the use of a Twin-Decoder Neural Network (TEDNet) for independent camera and LiDAR feature extraction. Bird's Eye View projections of the camera and LiDAR data are used in this model to perform semantic segmentation on whether each pixel belongs to the road surface.
  arXiv  Detail & Related papers  (2024-05-14T08:45:34Z)
• A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential [71.53284767149685]
  We present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar. We include a micro-steps discretization approach to accurately linearize the dynamics and produce a prediction suitable for real-time implementation.
  arXiv  Detail & Related papers  (2023-12-22T16:29:55Z)
• RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality [51.244807332133696]
  This paper introduces RACER, a cutting-edge deep learning car-following model to predict Adaptive Cruise Control (ACC) driving behavior. Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving. RACER excels across key metrics, such as acceleration, velocity, and spacing, registering zero violations.
  arXiv  Detail & Related papers  (2023-12-12T06:21:30Z)
• Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC [0.0]
  We present an approach for agile and computationally efficient trajectory tracking on the MIT SoftFly, a sub-gram MAV (0.7 grams) Our strategy employs a cascaded control scheme, where an adaptive attitude controller is combined with a neural network policy trained to imitate a trajectory tracking robust tube model predictive controller (RTMPC) We experimentally evaluate our approach, achieving position Root Mean Square Errors lower than 1.8 cm even in the more challenging maneuvers, obtaining a 60% reduction in maximum position error compared to our previous work, and robustness demonstrating to large external disturbances.
  arXiv  Detail & Related papers  (2022-09-20T21:30:16Z)
• Learning a Single Near-hover Position Controller for Vastly Different Quadcopters [56.37274861303324]
  This paper proposes an adaptive near-hover position controller for quadcopters. It can be deployed to quadcopters of very different mass, size and motor constants. It also shows rapid adaptation to unknown disturbances during runtime.
  arXiv  Detail & Related papers  (2022-09-19T17:55:05Z)
• Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds [100.61456258283245]
  This paper presents a multi-layer motion planning and control architecture for autonomous racing. The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 $m/s2$.
  arXiv  Detail & Related papers  (2022-07-22T15:16:54Z)
• Adapting Rapid Motor Adaptation for Bipedal Robots [73.5914982741483]
  We leverage recent advances in rapid adaptation for locomotion control, and extend them to work on bipedal robots. A-RMA adapts the base policy for the imperfect extrinsics estimator by finetuning it using model-free RL. We demonstrate that A-RMA outperforms a number of RL-based baseline controllers and model-based controllers in simulation.
  arXiv  Detail & Related papers  (2022-05-30T17:59:09Z)
• Visual-Inertial Odometry with Online Calibration of Velocity-Control Based Kinematic Motion Models [3.42658286826597]
  Visual-inertial odometry (VIO) is an important technology for autonomous robots with power and payload constraints. We propose a novel approach for VIO with stereo cameras which integrates and calibrates the velocity-control based kinematic motion model of wheeled mobile robots online.
  arXiv  Detail & Related papers  (2022-04-14T06:21:12Z)
• Adaptive Model Predictive Control of Wheeled Mobile Robots [0.0]
  This paper presents a control algorithm for guiding a two wheeled mobile robot with unknown inertia to a desired point and orientation. The two wheeled mobile robot is modeled as a knife edge or a skate with nonholonomic kinematic constraints. The efficacy of the algorithm is demonstrated through numerical simulations at the end of the paper.
  arXiv  Detail & Related papers  (2022-01-03T20:07:44Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)
• Populations of Spiking Neurons for Reservoir Computing: Closed Loop Control of a Compliant Quadruped [64.64924554743982]
  We present a framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.
  arXiv  Detail & Related papers  (2020-04-09T14:32:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.