Dynamic Tube MPC: Learning Tube Dynamics with Massively Parallel Simulation for Robust Safety in Practice
- URL: http://arxiv.org/abs/2411.15350v1
- Date: Fri, 22 Nov 2024 21:22:51 GMT
- Title: Dynamic Tube MPC: Learning Tube Dynamics with Massively Parallel Simulation for Robust Safety in Practice
- Authors: William D. Compton, Noel Csomay-Shanklin, Cole Johnson, Aaron D. Ames,
- Abstract summary: Inevitable tracking error necessitates robustification of the nominal plan to ensure safety.
In this work, we present a novel method leveraging massively parallel simulation to learn a dynamic tube representation.
The resulting Dynamic MPC Tube is applied to the 3D hopping robot ARCHER.
- Score: 28.37162791852146
- License:
- Abstract: Safe navigation of cluttered environments is a critical challenge in robotics. It is typically approached by separating the planning and tracking problems, with planning executed on a reduced order model to generate reference trajectories, and control techniques used to track these trajectories on the full order dynamics. Inevitable tracking error necessitates robustification of the nominal plan to ensure safety; in many cases, this is accomplished via worst-case bounding, which ignores the fact that some trajectories of the planning model may be easier to track than others. In this work, we present a novel method leveraging massively parallel simulation to learn a dynamic tube representation, which characterizes tracking performance as a function of actions taken by the planning model. Planning model trajectories are then optimized such that the dynamic tube lies in the free space, allowing a balance between performance and safety to be traded off in real time. The resulting Dynamic Tube MPC is applied to the 3D hopping robot ARCHER, enabling agile and performant navigation of cluttered environments, and safe collision-free traversal of narrow corridors.
Related papers
- Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models [57.45019514036948]
Simultaneous MRMP Diffusion (SMD) is a novel approach integrating constrained optimization into the diffusion sampling process to produce kinematically feasible trajectories.
The paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints.
arXiv Detail & Related papers (2025-02-05T20:51:28Z) - Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments [49.30744329170107]
We propose a novel approach for optimal online motion planning with minimal information about dynamic obstacles.
The proposed methodology combines Monte Carlo Tree Search (MCTS), for online optimal planning via model simulations, with Velocity Obstacles (VO), for obstacle avoidance.
We show the superiority of our methodology with respect to state-of-the-art planners, including Non-linear Model Predictive Control (NMPC), in terms of improved collision rate, computational and task performance.
arXiv Detail & Related papers (2025-01-16T16:45:08Z) - 3D Multi-Object Tracking with Semi-Supervised GRU-Kalman Filter [6.13623925528906]
3D Multi-Object Tracking (MOT) is essential for intelligent systems like autonomous driving and robotic sensing.
We propose a GRU-based MOT method, which introduces a learnable Kalman filter into the motion module.
This approach is able to learn object motion characteristics through data-driven learning, thereby avoiding the need for manual model design and model error.
arXiv Detail & Related papers (2024-11-13T08:34:07Z) - Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments [0.0]
This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100.
The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers.
arXiv Detail & Related papers (2024-10-03T17:50:19Z) - 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) - A Safer Vision-based Autonomous Planning System for Quadrotor UAVs with
Dynamic Obstacle Trajectory Prediction and Its Application with LLMs [6.747468447244154]
This paper proposes a vision-based planning system that combines tracking and trajectory prediction of dynamic obstacles to achieve efficient and reliable autonomous flight.
We conduct experiments in both simulation and real-world environments, and the results indicate that our approach can successfully detect and avoid obstacles in dynamic environments in real-time.
arXiv Detail & Related papers (2023-11-21T08:09:00Z) - Layout Sequence Prediction From Noisy Mobile Modality [53.49649231056857]
Trajectory prediction plays a vital role in understanding pedestrian movement for applications such as autonomous driving and robotics.
Current trajectory prediction models depend on long, complete, and accurately observed sequences from visual modalities.
We propose LTrajDiff, a novel approach that treats objects obstructed or out of sight as equally important as those with fully visible trajectories.
arXiv Detail & Related papers (2023-10-09T20:32:49Z) - Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform.
We produce a closed-loop controller to reactively push objects in a continuous action space.
We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
arXiv Detail & Related papers (2021-11-15T18:50:04Z) - 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) - Trajectory Planning for Autonomous Vehicles Using Hierarchical
Reinforcement Learning [21.500697097095408]
Planning safe trajectories under uncertain and dynamic conditions makes the autonomous driving problem significantly complex.
Current sampling-based methods such as Rapidly Exploring Random Trees (RRTs) are not ideal for this problem because of the high computational cost.
We propose a Hierarchical Reinforcement Learning structure combined with a Proportional-Integral-Derivative (PID) controller for trajectory planning.
arXiv Detail & Related papers (2020-11-09T20:49:54Z)
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.