Search arXivSearch

arXiv · 2406.11219

A Flexible and Resilient Formation Approach based on Hierarchical Reorganization

Abstract

Conventional formation methods typically rely on fixed hierarchical structures, such as predetermined leaders or predefined formation shapes. These rigid hierarchies can render formations cumbersome and inflexible in complex environments, leading to potential failure if any leader loses connectivity. To address these limitations, this paper introduces a reconfigurable affine formation that enhances both flexibility and resilience through hierarchical reorganization. The paper first elucidates the critical role of hierarchical reorganization, conceptualizing this process as involving role reallocation and dynamic changes in topological structures. To further investigate the conditions necessary for hierarchical reorganization, a reconfigurable hierarchical formation is developed based on graph theory, with its feasibility rigorously demonstrated. In conjunction with role transitions, a power-centric topology switching mechanism grounded in formation consensus convergence is proposed, ensuring coordinated resilience within the formation. Finally, simulations and experiments validate the performance of the proposed method. The aerial formations successfully performed multiple hierarchical reorganizations in both three-dimensional and two-dimensional spaces. Even in the event of a single leader's failure, the formation maintained stable flight through hierarchical reorganization. This rapid adaptability enables the robotic formations to execute complex tasks, including sharp turns and navigating through forests at speeds up to 1.9 m/s.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yuzhu Li, Wei Dong. 2024-09-06. A Flexible and Resilient Formation Approach based on Hierarchical Reorganization. https://arxiv.org/abs/2406.11219

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

J-PARSE: Jacobian-based Projection Algorithm for Resolving Singularities Effectively in Inverse Kinematic Control of Serial Manipulators

J-PARSE is an algorithm for smooth first-order inverse kinematic control of a serial manipulator near kinematic singularities. The commanded end-effector velocity is interpreted component-wise, according to the available mobility in each dimension of the task space. First, a substitute ''Safety'' Jacobian matrix is created, keeping the aspect ratio of the manipulability ellipsoid above a threshold value. The desired velocity is then projected onto non-singular and singular directions, and the latter projection scaled down by a factor informed by the altered mobility. A right-inverse of the non-singular Safety Jacobian is applied to the modified command. In the absence of joint limits and collisions, this ensures safe transition into and out of low-mobility configurations, guaranteeing locally stable reaching behavior towards target poses within, on the boundary of, and outside the workspace. The behavior is further guaranteed to be locally asymptotically stable if the starting and target configurations are not exactly singular, even if they are nearly singular. Velocity control with J-PARSE is benchmarked against approaches from the literature, illustrating its use of a single tuning parameter to simultaneously achieve high reaching accuracy and stable behavior. Applications in teleoperation, servoing, and learning are demonstrated. Videos and code are available at https://jparse-manip.github.io/.

cs.RO

NeMo-map: Neural Implicit Flow Fields for Spatio-Temporal Motion Mapping

Safe and efficient robot operation in complex human environments can benefit from good models of site-specific motion patterns. Maps of Dynamics (MoDs) provide such models by encoding statistical motion patterns in a map, but existing representations use discrete spatial sampling and typically require costly offline construction. We propose a continuous spatio-temporal MoD representation based on implicit neural functions that directly map coordinates to the parameters of a Semi-Wrapped Gaussian Mixture Model. This removes the need for discretization and imputation for unevenly sampled regions, enabling smooth generalization across both space and time. Evaluated on two public datasets with real-world people tracking data, our method achieves better accuracy of motion representation and smoother velocity distributions in sparse regions while still being computationally efficient, compared to available baselines. The proposed approach demonstrates a powerful and efficient way of modeling complex human motion patterns and high performance in the trajectory prediction downstream task. The code is publicly available at https://github.com/test-bai-cpu/nemo-map.

cs.RO

Spatiotemporal Calibration of Doppler Velocity Logs for Underwater Robots

The calibration of extrinsic parameters and clock offsets between sensors for high-accuracy performance in underwater SLAM systems remains insufficiently explored. Existing methods for Doppler Velocity Log (DVL) calibration are either constrained to specific sensor configurations or rely on oversimplified assumptions, and none jointly estimate translational extrinsics and time offsets. We propose a Unified Iterative Calibration (UIC) framework for general DVL sensor setups, formulated as a Maximum A Posteriori (MAP) estimation with a Gaussian Process (GP) motion prior for high-fidelity motion interpolation. UIC alternates between efficient GP-based motion state updates and gradient-based calibration variable updates, supported by a provably statistically consistent sequential initialization scheme. The proposed UIC can be applied to IMU, cameras and other modalities as co-sensors. We release an open-source DVL-camera calibration toolbox. Beyond underwater applications, several aspects of UIC-such as the integration of GP priors for MAP-based calibration and the design of provably reliable initialization procedures-are broadly applicable to other multi-sensor calibration problems. Finally, simulations and real-world tests validate our approach.

cs.RO