CSymPlan is presented, a certified symbolic planning and control framework for high-DOF manipulators with two complementary implementations: an offline implementation that precomputes certified reach-avoid feedback policies for known workspaces; and an online implementation that synthesizes or updates symbolic policies at runtime from changing task and perception information using parallelization.
Abstract
Robot manipulators are commonly engineered around a decoupled motion-generation stack: a planner computes a collision-free path and a lower-level controller tracks the resulting reference. This separation is computationally convenient, but it can produce references that are difficult to execute under actuator limits, tracking error, model mismatch, and small obstacle clearances. We present CSymPlan, a certified symbolic planning and control framework for high-DOF manipulators with two complementary implementations: an offline implementation that precomputes certified reach-avoid feedback policies for known workspaces; and an online implementation that synthesizes or updates symbolic policies at runtime from changing task and perception information using parallelization. The offline implementation reduces the manipulator dynamics to a sampled perturbed double-integrator model in operational space through feedback linearization, treats torque-realization errors, modeling inaccuracies, and measurement uncertainty as bounded disturbances, and refines the synthesized symbolic policy to the Franka FR3 through a quantization--lookup--torque realization pipeline. The online implementation uses the same abstraction and refinement interface, but replaces the precomputed policy table with a runtime pFaces request--synthesis--execution loop. In randomized simulated benchmarks and perception-driven Franka FR3 experiments, both implementations complete reach-avoid tasks with zero safety violations; whenever no certified action exists, the robot holds, replans, or stops safely instead of executing an uncertified command.
To address the inherent nondeterminism and lack of verifiability of learning- or sampling-based path planning methods in safety-critical dual-robot cooperative manufacturing, this paper aims to develop a deterministic and decoupled kinematic framework that ensures submillimeter accuracy, repeatability and native compatibility with industrial robot controllers for tasks such as precision drilling and riveting of large aerospace components.
The proposed framework is founded on first principles of geometry. For cooperative following, an extended tool frame method is introduced to transform complex coupled motions into independent single-robot trajectories executable by standard robot commands. For symmetric tasks, an analytical mirroring algorithm based on an arbitrary spatial plane is developed to offline generate accurate follower trajectories. The approach relies on high-precision laser tracker calibration and is validated on a dual-KUKA KR500-3 industrial robot platform.
Experimental validation demonstrates that the framework achieves a total cooperative positioning error of less than 0.53 mm under realistic industrial conditions. Both cooperative following and cooperative mirroring tasks satisfy the stringent tolerance requirements of aerospace manufacturing, confirming submillimeter accuracy, high repeatability and practical feasibility on heavy-duty industrial robots.
This work presents a novel deterministic kinematic framework that decouples dual-robot cooperative constraints into single-robot tracking problems without modifying low-level controller code or relying on real-time external computation. The extended tool frame and analytical mirroring methods significantly reduce programming effort while guaranteeing path determinism and verifiability. The submillimeter accuracy achieved meets aerospace-grade standards, offering a practical, efficient and certifiable solution for high-precision dual-robot cooperative manufacturing.
Jia-Mei Lin, Bo Li, Wei Tian· Industrial robot· 0 citations
This paper presents a mechatronic framework for generating predictable trajectories and safely executing hardware-in-the-loop operations of a low-cost, rotary-actuated six-degree-of-freedom (6-DOF) parallel manipulator. Unlike traditional prismatic Stewart platforms, this system explicitly accounts for servo-horn geometry and angular deadbands in its kinematic structure. To safely process chaotic, human-generated inputs, a deterministic software pipeline was developed. It uses generalized arc-length parameterization to maintain a constant task-space velocity for any spatial path and 7th-order minimum-jerk polynomials for optimal navigation through multiple waypoints. A vectorized digital twin checks all trajectories before execution, ensuring real-valued inverse kinematics and hardware compliance. The architecture was validated with a thorough analysis of the volumetric workspace. This analysis shows significant kinematic coupling issues and proves that the deterministic time-allocation engine effectively avoids actuator velocity saturation during complex movements.
Kassab Fakhoury, Ahmad T. Ramadan, Obada Alhoora et al.· IEEE Jordan Conference on Ap...· 0 citations
Planning trajectories for robot manipulators under kinematic equality constraints restricts feasible motions to a measure-zero submanifold of the configuration space, requiring special algorithmic treatment. A promising strategy is parametrizing the set of feasible configurations using analytic inverse kinematics (IK). Bespoke analytic IK functions can be written to be differentiable, a necessary property for gradient-based trajectory optimization. But the vast majority of IK functions are computed by automated meta-solvers like IKFast, and are difficult to modify for differentiability. We present a new approach for computing gradients of analytic IK parameterizations: we leverage the inverse function theorem to recover the desired gradients from the ordinary forward kinematic Jacobian. Furthermore, we present a least-squares domain extension and an optimization-amenable description of the reachability constraint, which preserves gradient signal outside the reachable workspace. We demonstrate the efficacy of our approach through numerical experiments and downstream tasks, including a hardware demonstration of an RB-Y1 picking up a box and placing it on a table. Project website: https://cohnt.github.io/inverse-function-theorem-parameterization/
Thomas Cohn, Seiji Shaw, Harel Biggie et al.· 0 citations
Recent advances in teleoperation have enabled robotic manipulators to perform dexterous, human-arm-like motions. However, human operators may fail to avoid suddenly appearing obstacles promptly and effectively, particularly under network latency or limited attention, thereby creating safety risks. To address this issue, we propose a lightweight and modular framework for proactive collision avoidance, operating directly at the end-effector velocity-command level. After preprocessing the point cloud, the framework first predicts potential collisions based on time-to-collision (TTC) with integrated overshoot protection, and subsequently rotates the relative-velocity vector using Rodrigues'rotation formula. The deflection changes only the direction of the relative velocity while preserving its magnitude, thereby mitigating the deadlock problem commonly encountered by conventional artificial potential field (APF) methods. The prediction module compensates for point-cloud processing latency introduced by complex teleoperation pipelines, while the lightweight design enables the high-frequency control required for teleoperation. Simulations across diverse scenarios show that the proposed method achieves a higher end-effector collision avoidance rate than the baseline methods. Experiments on a physical robotic system further validate its collision-avoidance effectiveness.
Changhao Hu, Zeyi Liu, Song Hu et al.· 0 citations
This thesis investigates advanced modeling and control strategies for robotic manipulators, focusing on the DLR-HIT II robotic hand and the KUKA LBR iiwa. It presents three core contributions that integrate simulation, model-based control, and data-driven methods to improve torque and position control under uncertainties and disturbances. First, a dual-platform simulation framework is developed using MATLAB Simscape Multibody and CoppeliaSim. The system accurately models the DLR-HIT II hand’s kinematics and dynamics, enabling both control validation and realistic interaction with virtual environments. The use of Unified Robot Description Format (URDF)-based modeling sup-ports reusability and modular analysis. Second, a physics-informed neural network (PINN) is proposed for direct torque and position control. This method uses only time and joint position inputs, internally computes derivatives, and generalizes well across various trajectory types. It eliminates the need for separate feedback controllers and shows strong robustness under disturbances, while maintaining low computational cost. Third, a Spike-Aware Hybrid Torque Control (SA-HTC) architecture is enhanced with a feedforward neural network (NN) trained offline. The network learns to improve Computed Torque Control (CTC) for unmodeled effects, friction, and external forces by refining the CTC torque output in real time. Simulation results across diverse trajectories and noise levels demonstrate that the SA-HTC method significantly improves tracking accuracy and robustness compared to classical CTC. To support deployment on position-controlled hardware, a physics-informed torque-to-position interface is introduced and compared with virtual stiffness and admittance wrappers, yielding lower steady-state bias, reduced phase lag, lower tracking error, and robust cross-trajectory generalization. Thus, these contributions advance the integration of learning-based and model-based control strategies in robotics. The results highlight scalable and efficient methods for accurate trajectory tracking and torque control, offering practical potential for robotic manipulation and automation applications.
In urban search and rescue, articulated tracked robots (ATRs) must traverse structured but contact-rich environments such as stairwells and cluttered building interiors. Reliable autonomy remains challenging because robot-terrain interaction (RTI) is hybrid and discontinuous, and effective flipper-track coordination is difficult to model analytically. We present ASTRIL-MPC, a language-guided neural kinematics model predictive control (MPC) framework for autonomous traversal. A learned kinematics model predicts short-horizon task-state increments from a height sequence and recent trajectories; NMPC plans with multi-objective costs and strict feasibility constraints; and a large language model (LLM) proposes bounded updates to selected weights and bounds through a safety-checked interface with range clipping, rate limiting, and consistency checks. The compiled predictor enables a full control cycle within 100 ms. Across three traversal tasks and a multi-height generalization setting, ASTRIL-MPC improves an aggregate traversal-quality score by up to 71% over a non-adaptive NMPC and by 67% over a PPO baseline, while eliminating measurable collision impacts during descent. These results indicate that combining learned kinematics, optimization-based planning, and language-guided retuning yields data-efficient and robust autonomy for articulated tracked robots.
Zhe Gan, Yan-Bo Chen, Li-Rong Che et al.· 0 citations
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