Coordinating control of multiple rigid bodies based on motion primitives

This paper studies the problem of coordinated motion generation for a group of rigid bodies. Two classes of coordinated motion primitives, relative equilibria and ma- neuvers, are given as building blocks for generating coordi- nated motions. In a motion-primitive based planning frame- work, a control method is proposed for the robust execution of a coordinated motion plan in the presence of perturba- tions. The control method combines the relative equilibria stabilization with maneuver design, and results in a close- loop motion planning framework. The performance of the control method has been illustrated through a numerical sim- ulation.

Online Observability-Constrained Motion Suggestion via Efficient Motion Primitive-Based Observability Analysis

An active perception methodology is proposed to locally predict the observability condition in a reasonable horizon and suggest an observability-constrained motion direction for the next step to ensure an accurate and consistent state estimation performance of vision-based navigation systems. The methodology leverages an efficient EOG-based observability analysis and a motion primitive-based path sampling technique to realize the local observability prediction with a real-time performance. The observability conditions of potential motion trajectories are evaluated,and an informed motion direction is selected to ensure the observability efficiency for the state estimation system. The proposed approach is specialized to a representative optimizationbased monocular vision-based state estimation formulation and demonstrated through simulation and experiments to evaluate the ability of estimation degradation prediction and efficacy of motion direction suggestion.

Generation and Selection of Driver-Behavior-Based Transferable MotionPrimitives

To integrate driver experience and heterogeneous vehicle platform characteristics in a motion-planning algorithm,based on the driver-behavior-based transferable motion primitives(MPs), a general motion-planning framework for offline generation and online selection of MPs is proposed. Optimal control theory is applied to solve the boundary value problems in the process of generating MPs, where the driver behaviors and the vehicle motion characteristics are integrated into the optimization in the form of constraints. Moreover, a layered, unequal-weighted MP selection framework is proposed that utilizes a combination of environmental constraints, nonholonomic vehicle constraints,trajectory smoothness, and collision risk as the single-step extension evaluation index. The library of MPs generated offline demonstrates that the proposed generation method realizes the effective expansion of MP types and achieves diverse generation of MPs with various velocity attributes and platform types. We also present how the MP selection algorithm utilizes a unique MP library to achieve online extension of MP sequences. The results show that the proposed motion-planning framework can not only improve the efficiency and rationality of the algorithm based on driving experience but can also transfer between heterogeneous vehicle platforms and highlight the unique motion characteristics of the platform.

SOME LIMINF RESULTS ON INCREMENTS OF THE PRIMITIVES OF BROWNIAN MOTION

Let { W(t);t≥0 } be a standard Brownian motion.For a positive integer m ,define a Gaussian processX m(t)=1m!∫ t 0(t-s) m d W(s).In this paper the liminf behavior of the increments of this process is discussed by establishing some probability inequalities.Some previous results are extended and improved.

Extraction of Robot Primitive Control Rules from Natural Language Instructions

A support vector rule based method is investigated for the construction of motion controllers via natural language training. It is a two-phase process including motion control information collection from natural language instructions, and motion information condensation with the aid of support vector machine （SVM） theory. Self-organizing fuzzy neural networks are utilized for the collection of control rules, from which support vector rules are extracted to form a final controller to achieve any given control accuracy. In this way, the number of control rules is reduced, and the structure of the controller tidied, making a controller constructed using natural language training more appropriate in practice, and providing a fundamental rule base for high-level robot behavior control. Simulations and experiments on a wheeled robot are carried out to illustrate the effectiveness of the method.

Anticipating Degradation in State Estimation Accuracy via Online Nonlinear Observability Analysis

A methodology is proposed to enable real-time evaluation of the observability of local motions,and generate a local observability cost map to enable informed local motion planning in order to avoid potential degradation or degeneracy in state estimator performance.The proposed approach leverages efficient numerical techniques in nonlinear observability analysis and motion primitive-based planning technique to realize the local observability prediction with real-time performance.The degradation of the state estimation performance can be readily predicted with the local observability evaluation result.The proposed approach is specialized to a representative optimization-based monocular visual-inertial state estimation formulation and evaluated through simulation and experiments.The experimental results demonstrated the ability of the proposed methodology to correctly anticipate the potential state estimation degradation.

Decentralized MPC-Based Trajectory Generation for Multiple Quadrotors in Cluttered Environments

Challenges in motion planning for multiple quadrotors in complex environments lie in overall°ight e±ciency and the avoidance of obstacles,deadlock,and collisions among themselves.In this paper,we present a gradient-free trajectory generation method for multiple quadrotors in dynamic obstacle-dense environments with the consideration of time consumption.A model predictive control(MPC)-based approach for each quadrotor is proposed to achieve distributed and asynchronous cooperative motion planning.First,the motion primitives of each quadrotor are formulated as the boundary state constrained primitives(BSCPs)which are constructed with jerk limited trajectory(JLT)generation method,a boundary value problem(BVP)solver,to obtain time-optimal trajectories.They are then approximated with a neural network(NN),pre-trained using this solver to reduce the computational burden.The NN is used for fast evaluation with the guidance of a navigation function during optimization to guarantee°ight safety without deadlock.Finally,the reference trajectories are generated using the same BVP solver.Our simulation and experimental results demonstrate the superior performance of the proposed method.