Fault-Tolerant, Rigidity-Preserving Control of Inflatable Truss Robots

• URL: http://arxiv.org/abs/2605.20561v1
• Date: Tue, 19 May 2026 23:39:55 GMT
• Title: Fault-Tolerant, Rigidity-Preserving Control of Inflatable Truss Robots
• Abstract summary: Isoperimetric robotic trusses can adapt to different tasks and environments because they have a high strength-to-weight ratio.<n>Motor failures in operational environments can severely limit operational capabilities if not properly addressed.<n>This paper presents a fault-tolerant control framework for an inflatable robotic truss that maintains functionality despite motor failures.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Isoperimetric robotic trusses can adapt to different tasks and environments because they have a high strength-to-weight ratio, can change their own shape dramatically, and can be reconfigured into a variety of different shapes. However, motor failures in operational environments can severely limit operational capabilities if not properly addressed. This paper presents a fault-tolerant control framework for an inflatable robotic truss that maintains functionality despite motor failures, shown through three key contributions. First, we extend the kinematic optimization to handle arbitrary combinations of motor failures by imposing equality constraints to ensure failed actuators are not used. Second, we introduce discrete-time control barrier function (DTCBF) constraints that mathematically guarantee structural rigidity while maximizing workspace utilization, a critical requirement for reliable operation of truss robots under discrete-time control. Third, we implement closed-loop position control using onboard encoder feedback and a forward kinematics-based state estimator, improving positional accuracy in the presence of disturbances. We validate our approach through simulation and hardware experiments on a 2D isoperimetric truss testbed. For a 2D configuration with 6 actuators, we demonstrate >69% workspace preservation under single-motor failures and a >25% improvement in tracking accuracy with closed-loop control. These results establish a foundation for more robust and resilient isoperimetric truss robots operating under degraded actuation.

Related papers

• Effector-Centric NMPC of Tiltable-Multirotors for Offset-Free Omnidirectional Aerial Manipulation [9.325443556436436]
  This work presents a design analysis and a wrench-based control framework for tiltable-multirotors in aerial manipulation.<n>A four-rotor tiltable configuration provides a balance between interference-free propeller sizing and hovering efficiency.<n>We develop an effector-centric nonlinear model predictive control framework that integrates design choices, handling, and disturbance compensation.
  arXiv  Detail & Related papers  (2026-08-18T14:18:48Z)
• 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)
• Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations [122.22391796628408]
  We present Bench2Drive-Robust, to our knowledge the first device-centric robustness benchmark for closed-loop end-to-end autonomous driving.<n>We systematically evaluate deployment-oriented perturbations arising from three major sources: camera-stream failures, ego-state estimation errors, and compute-induced control delay.<n>Our results show that these deployment-related perturbations can substantially degrade closed-loop driving performance.
  arXiv  Detail & Related papers  (2026-05-18T08:45:24Z)
• Sliding Mode Control for Safe Trajectory Tracking with Moving Obstacles Avoidance: Experimental Validation on Planar Robots [1.7044368816068716]
  This paper presents a unified control framework for robust trajectory tracking and moving obstacle avoidance.<n>The proposed architecture guarantees tracking in the presence of external disturbances while strictly enforcing collision avoidance constraints.<n>The efficacy and versatility of the approach are validated through numerical simulations and extensive real-world experiments.
  arXiv  Detail & Related papers  (2026-04-27T14:20:10Z)
• MARS-Dragonfly: Agile and Robust Flight Control of Modular Aerial Robot Systems [21.471271822760276]
  Modular Aerial Robot Systems (MARS) comprise multiple drone units with reconfigurable connected formations.<n>MARS rely on simplified quasi-static models and rule-based allocation, which generate discontinuous and unbounded motor commands.<n>We develop a compact mechanical system that enables passive docking, detection-free passive locking, and magnetic-assisted separation.
  arXiv  Detail & Related papers  (2026-04-07T06:43:08Z)
• Shape Control of a Planar Hyper-Redundant Robot via Hybrid Kinematics-Informed and Learning-based Approach [42.55947704321053]
  Hyper-redundant robots offer high dexterity, making them good at operating in confined and unstructured environments.<n>To extend the reachable workspace, we built a multi-segment flexible rack actuated planar robot.<n>We propose a hybrid kinematics-informed and learning-based shape control method, named SpatioCoupledNet.
  arXiv  Detail & Related papers  (2026-03-11T04:35:06Z)
• Learning Agile and Robust Omnidirectional Aerial Motion on Overactuated Tiltable-Quadrotors [15.511018281264674]
  This work investigates reinforcement learning for omnidirectional aerial motion control on over-actuated tiltable quadrotors.<n>We present a learning-based control framework that enables efficient acquisition of coordinated rotor-joint behaviors for reaching target poses.
  arXiv  Detail & Related papers  (2026-02-25T05:13:25Z)
• Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation [88.83749146867665]
  Existing approaches learn a policy to predict a distant next-best end-effector pose.<n>They then compute the corresponding joint rotation angles for motion using inverse kinematics.<n>We propose Kinematics enhanced Spatial-TemporAl gRaph diffuser.
  arXiv  Detail & Related papers  (2025-03-13T17:48:35Z)
• FLOAT Drone: A Fully-actuated Coaxial Aerial Robot for Close-Proximity Operations [4.5231181198147485]
  We present FLOAT Drone (FuLly-actuated cO Aerial roboT), a novel fully-actuated UAV featuring two key structural innovations.<n>By integrating control surfaces into fully-actuated systems for the first time, we significantly suppress lateral airflow disturbances during operations.<n>Furthermore, a coaxial dual-rotor configuration enables a compact size while maintaining high hovering efficiency.
  arXiv  Detail & Related papers  (2025-03-02T08:30:30Z)
• 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)
• First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion [73.37945453998134]
  Traditional approaches to quadruped control employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic range is curtailed. In this work, these challenges are addressed by framing quadruped control as optimisation in a structured latent space. A deep generative model captures a statistical representation of feasible joint configurations, whilst complex dynamic and terminal constraints are expressed via high-level, semantic indicators. We validate the feasibility of locomotion trajectories optimised using our approach both in simulation and on a real-worldmal quadruped.
  arXiv  Detail & Related papers  (2020-07-03T07:04:18Z)
• Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning [85.13138591433635]
  The main drawbacks of input-output linearizing controllers are the need for precise dynamics models and not being able to account for input constraints. In this paper, we address both challenges for the specific case of bipedal robot control by the use of reinforcement learning techniques.
  arXiv  Detail & Related papers  (2020-04-15T18:15:49Z)

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.