Dancing PRM*: Simultaneous Planning of Sampling and Optimization with Configuration Free Space Approximation

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.

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.

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.

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

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.

Scholars have proposed a cable-driven parallel robot (CDPR) with aerial and ground actuators, referred to as a cable-towed aerial platform (CTAP), to address the limited ability to withstand external forces of existing unmanned arrival vehicle (UAV)-based aerial platforms. To tackle practical problems (e.g., firefighting), a CTAP may need to perform motion planning in an environment with obstacles and communication failures. To this end, this article proposes an online decentralized planning approach based on multi-agent reinforcement learning (MARL) for a CTAP to achieve real-time motion planning in a communication-denied environment with obstacles. This article then defines the state and action spaces of a MARL-based planner. A reward function is designed for the MARL-based planning approach according to the widely used optimization-based planning approach. This study has successfully trained a MARL-based planner using this approach, and the important training techniques used in this study are reported. Statistical comparison of the MARL-based decentralized planner and an optimization-based centralized planner is conducted in simulation. The MARL-based and optimization-based planners are deployed to a CTAP prototype to address motion planning problems in the real world. Experimental results show that the MARL-based planner can achieve successful, decentralized, and online motion planning for a CTAP.

Lazy collision checking has been proposed to reduce the computational burden of collision checking which is considered as a major computational bottleneck in sampling-based motion planning. Unfortunately, in complex environments with many obstacles, lazy collision checking can cause an excessive amount of optimistic thrashing problems and significantly degrade the performance of the planner. In this paper, we present an adaptive lazy collision checking method to alleviate the optimistic thrashing, thus broaden the applicability. Our method delays collision checking on the regions predicted to be in the configuration free space, while checking early on the other regions to reduce the optimistic thrashing. To identify such regions, we adopt a configuration free space approximation represented by a set of hyperspheres which can be constructed without significant proximity computation. To demonstrate benefits of our approach we have compared against prior methods including RRT*, PRM* and lazy PRM* across benchmarks with varying dimensions. Overall, our method shows meaningful performance improvement up to three times higher in terms of convergence speed over the tested methods. We also discuss properties and theoretical analysis of the proposed algorithm.

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.

This paper is a basic study for the development of a simulator algorithm to generate optimal robot motion and path planning for a multi-D.O.F(Degree of Freedom) gantry-type welding robot system. The main idea of the simulator algorithm is to utilize vector equations to define the relationship between each frame derived by forward kinematics. Eight objective functions were defined using vector equations that define the relationship between frames, and each was formulated as an optimization problem. For the optimization problem, the optimal value was derived through optimizers such as SQP (Sequential Quadratic Problem), Active-set, and fgoalattain, and motion and error range were simulated by putting the derived control variables into a forward kinematics simulator. If a specific motion can be created through various formalized optimization problems, it is expected that it can be used as a policy for reinforcement learning in the future.

Optimal Motion Planning and Tracking Control for a...

For any mobile device, the ability to navigate smoothly in its environment is of paramount importance, which justifies researchers’ continuous work on designing new techniques to reach this goal. In this work, we briefly present a description of a hard work on designing a Same Fuzzy Logic Controller (S.F.L.C.) of the two reactive behaviors of the mobile robot, namely, “go to goal obstacle avoidance” and “wall following,” in order to solve its navigation problems. This new technique allows an optimal motion planning in terms of path length and travelling time; it is meant to avoid collisions with convex and concave obstacles and to achieve the shortest path followed by the mobile robot. The efficiency of employing the proposed navigational controller is validated when compared to the results from other intelligent approaches; its qualities make of it an efficient alternative method for solving the path planning problem of the mobile robot.

The maneuvering control of an aquatic vehicle using an oscillating foil as the propulsion system is considered. The control task is decomposed into the off-line step of motion planning and the online step of feedback tracking. Optimal control techniques are used for obtaining a repertoire of time scalable and concatenable primitive motions. The computed optimal motion plans are regulated by a controller that consists of a cascade of linear quadratic regulator, input-output feedback linearization and sliding mode control. Complete maneuvers are obtained by concatenating and executing time scaled copies of primitive motions. The motion planning and control scheme was experimentally tested for longitudinal maneuvers.

We present a number of breakthroughs for coordinated motion planning, in which the objective is to reconfigure a swarm of labeled convex objects by a combination of parallel, continuous, collision-free translations into a given target arrangement. Problems of this type can be traced back to the classic work of Schwartz and Sharir (1983), who gave a method for deciding the existence of a coordinated motion for a set of disks between obstacles; their approach is polynomial in the complexity of the obstacles, but exponential in the number of disks. Despite a broad range of other non-trivial results for multi-object motion planning, previous work has largely focused on sequential schedules, in which one robot moves at a time, with objectives such as the number of moves; attempts to minimize the overall makespan of a coordinated parallel motion schedule (with many robots moving simultaneously) have defied all attempts at establishing the complexity in the absence of obstacles, as well as the existence of efficient approximation methods. We resolve these open problems by developing a framework that provides constant-factor approximation algorithms for minimizing the execution time of a coordinated, parallel motion plan for a swarm of robots in the absence of obstacles, provided their arrangement entails some amount of separability. In fact, our algorithm achieves constant stretch factor: If all robots want to move at most d units from their respective starting positions, then the total duration of the overall schedule (and hence the distance traveled by each robot) is O(d). Various extensions include unlabeled robots and different classes of robots. We also resolve the complexity of finding a reconfiguration plan with minimal execution time by proving that this is NP-hard, even for a grid arrangement without any stationary obstacles. On the other hand, we show that for densely packed disks that cannot be well separated, a stretch factor Omega(N^{1/4}) may be required. On the positive side, we establish a stretch factor of O(N^{1/2}) even in this case. The intricate difficulties of computing precise optimal solutions are demonstrated by the seemingly simple case of just two disks, which is shown to be excruciatingly difficult to solve to optimality.

This paper presents, Anytime Fast-BIT* (AFBIT*), a sampling based, asymptotically optimal manipulation motion planner which quickly finds an initial feasible path and rapidly improves the path quality toward optimality. AFBIT* is guided by modified heuristics in task space for faster first solution. A local optimization method is adopted at the end of every round to optimize the current best path and generate the next vertex and edge for the global path while a new round begins to improve the quality of the remaining path. Simulation results suggest AFBIT* is more efficient and effective on manipulation problems than BIT* and Fast-BIT*.