A Novel Version of Sampling-based Motion Planner for Manipulation with Faster Initial Solution and Convergence

In the case that the total angular momentum conservation about the system center of mass, a torque-free spacecraft system with space manipulators becomes the nonholonomic control system. Due to the nonholonomic characteristic, the motion planning and control of a nonholonomic system is much more difficult than that of a conventional. In terms of the nonholonomic characteristic, the three-dimensional attitude control problem of the free floating space manipulators is investigated. The mathematical model of attitude control of rigid spacecraft with space manipulators is obtained. The control problem of the spacecraft attitude becomes a motion planning problem for a drift free control system. The control input rule is expressed by Fourier orthonormal basis and coefficient vector. Based on the optimal motion planning, the particle swarm optimization algorithm is proposed for free floating space manipulators. The results of numerical simulation indicate that this approach is effective for the motion planning problem of space manipulators.

Optimal visibility-based path and motion planning of mobile observers for 3-D objects

Description: The implementation of well testing models for dynamic characterization of Naturally Fractured Vuggy Reservoirs (NFVR) commonly involves inverse modeling. Thus, this work introduces new results about dynamic description of NFVR by using a triple porosity-dual permeability model. The values of the inter-porosity flow parameters, storativity ratios, permeability ratio between fractures and vugs, wellbore storage coefficient, skin and total permeability, are automatically identified in Laplace Space by using a gradient Newton-type global optimization method named The Tunneling. This method offers more reliable results in comparison to the local optimization methods commonly used in parameters estimations. Applications: The precision and computational efficiency of the Tunneling Method are demonstrated through the reliable results obtained from the automatic analysis of synthetic examples, where a very comprehensive and large set of synthetic noisy data was used. In all cases, the Tunneling Method obtained optimal solutions. Likewise, the analysis of several field well test data cases recorded in NFVR are shown to confirm the benefits above mentioned. Results, Observations, and Conclusions: The parameters determination in the problem here addressed is non-unique due to the ill-posedness problem forcing the local optimization methods to stop in non-optimal solutions. Likewise, local methods require many realizations until converge into an optimal solution. Thus, the superiority of the Tunneling Method for overcoming these challenges is demonstrated through the reliable analysis of several synthetic and field data cases. Performing the optimization in the Laplace space makes the procedure very efficient in computing time. To moderate or eliminate the non-uniqueness problem, information about storativity ratios obtained from core or well log analysis, can be used through the optimization process. Significance of Subject Matter: Currently, the well test data analysis is the only available procedure to estimate the dynamic parameters which characterize NFVRs. Likewise, the approach introduced in this work allows obtaining more reliable estimations of these parameters than local optimization methods.

Surface cracks exist in many civil infrastructures such as road and bridge deck surfaces, parking lots, and building surfaces, etc. To prevent the crack growth and further deterioration, it is necessary to fill these cracks with appropriate materials on time. We present a robotic crack filling system that can effectively and efficiently deliver fluidic repairing materials to fill surface cracks. Motion planning and cracking filling control are the two main tasks presented in this paper. We present a graph-based crack coverage (GCC) algorithm to solve motion planning problem. The filling motion control is captured by a model predictive control (MPC) scheme given the mobile platform trajectory. We also develop a crack-filling robotic prototype. Simulation and experimental results are presented to demonstrate the system design and performance evaluation.

Multivariate quartic polynomial optimization problems, as a special case of the general polynomial optimization, have a lot of practical applications in real world and are proved to be NP-hard. In this paper, some necessary local optimality conditions and some necessary global optimality conditions for quartic polynomial optimization problems with mixed variables are established. Then some local optimization methods, including weakly local optimization methods for general problems with mixed variables and strongly local optimization methods for quartic polynomial optimization problems with mixed variables, are proposed by exploiting these necessary local optimality conditions and necessary global optimality conditions. A global optimization method is proposed for quartic polynomial optimization problems by combining these local optimization methods together with some auxiliary functions. Some numerical examples are also given to illustrate that these approaches are very efficient.

Utilization of surface wave dispersive properties may be roughly divided into three steps: field data acquisition, dispersion-curve picking, and inversion of phase velocities (

Recently, Permanent Magnet Generator has been widely used due to its ability of having simple structure yet high efficiency. To maximise the output performance of the generator, advances and trends in mathematical modelling and computer simulation with using optimization techniques have opened the way to a new approach for electrical machine design. In this paper, a Radial Flux Permanent Magnet Generator configuration was examined and optimised throughout sizes and weight using Local Optimization Method as an iterative process in order to achieves optimal output. By utilizing LOM technique as optimization process, the output voltage of RFPMG increase by 13.64% from the initial results. With a corresponding increase in output voltage performances, the configurations geometry adjustment additionally helps in expanding the generator operational area.

The successful integration of autonomous robots in real-world environments strongly depends on their ability to reason from context and take socially acceptable actions. Current autonomous navigation systems mainly rely on geometric information and hard-coded rules to induce safe and socially compliant behaviors. Yet, in unstructured urban scenarios these approaches can become costly and suboptimal. In this paper, we introduce a motion planning framework consisting of two components: a data-driven policy that uses visual inputs and human feedback to generate socially compliant driving behaviors (encoded by high-level decision variables), and a local trajectory optimization method that executes these behaviors (ensuring safety). In particular, we employ Interactive Imitation Learning to jointly train the policy with the local planner, a Model Predictive Controller (MPC), which results in safe and human-like driving behaviors. Our approach is validated in realistic simulated urban scenarios. Qualitative results show the similarity of the learned behaviors with human driving. Furthermore, navigation performance is substantially improved in terms of safety, i.e., number of collisions, as compared to prior trajectory optimization frameworks, and in terms of data-efficiency as compared to prior learning-based frameworks, broadening the operational domain of MPC to more realistic autonomous driving scenarios.

Features of several well-known global and local optimization methods were discussed and their robustness and efficiency were tested on several artificial test functions in Matlab environment. The tested local methods were the interior-point, the quasi-Newton method, Nelder–Mead simplex, the pattern search, the NEWUOA and the BOBYQA methods. The global methods were the genetic algorithm (GA), the simulated annealing (SA), the particle swarm optimization (PSO), and the covariance matrix adaptation evolutionary strategy (CMA-ES) methods (see subsections 2.2 and 2.3 for their details). Furthermore, a novel global optimization method, called FOCusing robusT Optimization with Uncertainty-based Sampling (FOCTOPUS), which proved to be very efficient in the optimization of constrained and highly correlated parameters of combustion kinetic models, was also tested. The test functions were selected in such a way that they had a variety of features: uni-modal and multi-modal, differentiable and non-differentiable, separable and non-separable, low dimensional and high dimensional. The following test functions were used: the 20D Alpine function, the 4D Ackley function, the Cross-in-tray 2D function, the Hartmann-6D function, the Holder table 2D function, the 5D Rastrigin function, the 5D Rosenbrock function, the 4D modified Rosenbrock function, the 20D Zakharov function and a typical 2D multi-modal function. The general conclusion here is that, the global methods performed well on the multi-modal and high-dimensional test functions while the local methods were superior in the case of low-dimensional and uni-modal test functions. For the highly multi-modal test functions, the GA was better than all the other methods. The FOCTOPUS method proved to be inferior to GA for most of the test functions, thus its application cannot be generally recommended.

Motion planning for complex dynamic systems such as helicopters is a challenging problem due to non-holonomic and nonlinear differential constraints. Approaches for optimal kinodynamics motion planning have only been demonstrated for simple dynamic systems such as Dubins car or linear systems. In this paper we present Closed-loop FMT* (CL-FMT*) which is an extension of FMT* [7] that uses closed-loop prediction. With closed-loop prediction the computationally intense two-point-boundary-value steering procedure can be avoided. We describe the effectiveness of CL-FMT* compared to CL-RRT*. We then demonstrate the use of CL-FMT* for helicopter landing trajectory planning with realistic dynamics constrains.

Earlier work has shown that reusing experience from prior motion planning\nproblems can improve the efficiency of similar, future motion planning queries.\nHowever, for robots with many degrees-of-freedom, these methods exhibit poor\ngeneralization across different environments and often require large datasets\nthat are impractical to gather. We present SPARK and FLAME , two\nexperience-based frameworks for sampling-based planning applicable to complex\nmanipulators in 3 D environments. Both combine samplers associated with\nfeatures from a workspace decomposition into a global biased sampling\ndistribution. SPARK decomposes the environment based on exact geometry while\nFLAME is more general, and uses an octree-based decomposition obtained from\nsensor data. We demonstrate the effectiveness of SPARK and FLAME on a Fetch\nrobot tasked with challenging pick-and-place manipulation problems. Our\napproaches can be trained incrementally and significantly improve performance\nwith only a handful of examples, generalizing better over diverse tasks and\nenvironments as compared to prior approaches.\n

This study focuses on optimal motion planning for nonholonomic constraints mobile robot. We formulate the dynamics model of a differential-drive mobile robot by using Lagrangian mechanics, where the nonholonomic constraints are accurately described through differential equations. The optimal motion planning of the system is constructed as an optimal control problem which is then converted to a nonlinear programming problem by introducing trapezoidal collocation method, and the formulated nonlinear programming is solved by interior-point method. Compared with the prevailing methods in the field of motion planning, our proposed method can handle different kinds of path constraints, terminal conditions and collision-avoidance requirements. Simulation results indicate that the proposed approach can efficiently deal with various user-specified requirements with advantage of high computing efficiency.

This paper presents the optimal motion planning problem for connected and automated vehicles (CAVs) to cross a conflict area at an intersection with state and control constraints. First, we formulate the scheduled merging (or crossing) time for all CAVs as a mixed integer linear programming (MILP) problem where the merging time is solved frequently. Second, we formulate the optimal motion planning problem so that the CAVs can achieve their scheduled merging time as well as minimizing the energy consumption. Since we solve the motion planning problem analytically, not all the solutions are feasible to comply with the frequently updated merging time. To solve this problem, we propose a feasibility enforcement period (FEP). Then, we validate the proposed solution through simulation, and the results show that even the merging time is frequently updated, the CAVs can still achieve the merging time with a minimal deviation. Besides, our proposed framework also shows a significant improvement in terms of travel time as compared to the conventional one.

A recent trend in optimal motion planning has broadened the research area toward the hybridization of sampling, optimization and grid-based approaches. We can expect that synergy from such integrations leads to overall performance improvement, but seamless integration and generalization is still an open problem. In this paper, we suggest a hybrid motion planning algorithm utilizing a sampling-based and optimization-based planner while simultaneously approximating a configuration free space. Unlike conventional optimization-based approaches, the proposed algorithm does not depend on a priori information or resolution-complete factors, e.g., a distance field. Ours instead learns spatial information on the fly by exploiting empirical information during the execution, and decentralizes the information over the constructed graph for efficient access. With the help of the learned information, our optimization-based local planner exploits the local area to identify the connectivity of configuration free space without depending on the precomputed domain knowledge. To show the novelty of proposed algorithm, we evaluate it against other asymptotic optimal planners in both synthetic and complex benchmarks with varying degrees of freedom. We also discuss the performance improvement, properties and limitations we have observed.

This paper addresses a problem of optimal motion planning of mobile platforms amidst obstacles, considering the mobile platform dynamics. Due to nonholonomic constraints, actuator constraints, and state constraints by obstacle avoidance, the planning problem of mobile platform with two independently driven wheels is a complicated one. In this study, a dynamical model for the mobile platform is presented, including nonholonomic kinematic constraints. The idea of a path parameter is introduced to simplify the planning problem by considering the dynamics and nonholonomic constraints. Using the path parameter, the optimal motion planning problem is divided into two sub-problems: 1) time-optimization of trajectory along specified path, and 2) search for optimal path. Then two methods are proposed the solve the problems using the path parameter and parametrization by B-spline function. Finally, quasi-time-optimal solution for the original problem are planned by combining the two methods. Numerical examples show effectiveness of the motion planner.