In this paper, we discuss the field of sampling-based motion planning. In contrast to methods that construct boundary representations of configuration space obstacles, sampling-based methods use only information from a collision detector as they search the configuration space. The simplicity of this approach, along with increases in computation power and the development of efficient collision detection algorithms, has resulted in the introduction of a number of powerful motion planning algorithms, capable of solving challenging problems with many degrees of freedom. First, we trace how samplingbased motion planning has developed. We then discuss a variety of important issues for sampling-based motion planning, including uniform and regular sampling, topological issues, and search philosophies. Finally, we address important issues regarding the role of randomization in sampling-based motion planning.

This paper proposes an adaptive framework for single shot motion planning, i.e., planning without preprocessing. This framework can be used in any situation, and in particular, is suitable for crowded environments in which the robot's free C-space has narrow corridors such as maintainability studies in complex 3D CAD models. Our strategy is to adaptively select a planner whose strengths match the current situation, and then, on-line, switch to a different planner when circumstances change. This requires techniques to evaluate the characteristics of the current query, and a set of planners which are characterized so that we can match the query with the best planner for it. Our experimental results in complex 3D environments show that our strategy solves queries that none of the planners could solve on their own.

We present a resolution complete path planner based on an implicit grid in the configuration space. The planner can be described as a two-level process in which a global planner restricts a local planner to certain subsets of the grid. The global planner starts by letting the local planner search in a coarse subset of the grid, and successively refines the grid until a solution is found. The local planner applies a scheme for lazy evaluation on each subgrid in order to minimize collision checking and thereby increase performance. Experimental results in an industrial application show that lazy evaluation on a grid is very efficient in practice. The algorithm is particularly useful in high dimensional, relatively uncluttered configuration spaces, especially when collision checking is computationally expensive. Single queries are handled quickly since no preprocessing is required.

In this paper, we propose a motion planning method for cooperative transportation of a large object by multiple mobile robots in a 3 dimensional environment. This task has various kinds of problems, such as path planning, manipulation, and so on. All of these problems can’t be solved at once, since computational time is exploded. Accordingly, we divide a motion planner into a local manipulation planner and a global path (motion) planner, and design these two planners respectively. And we integrate two planners. Namely, we aim at integrating a gross motion planner and a fine motion planner. As to the local manipulation planner, we build a manipulation technique, which is suitable for mobile robots by position-control. We compute conditions, in which the object becomes unstable during manipulation, and generate the each robot’s motion considering the robots ’ motion errors and indefinite factors from the planning stage. As to the global path planner, we reduce the dimensions of the configuration space (C-space) using the feature of transportation by mobile robots. We can find a solution with searching in this smaller dimensional C-space using the potential field defined in the C-space. And constraints of the object manipulation are considered as the potential function. We verify the effectiveness of our proposed motion planning method through simulations and experiments.

Traditional approaches to the motion-planning problem can be classified into solutions for single-query and multiple-query problems with the tradeoffs on run-time computation cost and adaptability to environment changes. In this paper, we propose a novel approach to the problem that can learn incrementally on every planning query and effectively manage the learned road-map as the process goes on. This planner is based on previous work on probabilistic roadmaps and uses a data structure called Reconfigurable Random Forest (RRF), which extends the Rapidly-exploring Random Tree (RRT) structure proposed in the literature. The planner can account for environmental changes while keeping the size of the roadmap small. The planner removes invalid nodes in the roadmap as the obstacle configurations change. It also uses a tree-pruning algorithm to trim RRF into a more concise representation. Our experiments show that the resulting roadmap has good coverage of freespace as the original one. We have also successful incorporated the planner into the application of intelligent navigation control.

The efficient planning of contact tasks for intelligent robotic systems requires a thorough understanding of the kinematic constraints imposed on the system by the contacts. In this paper, we derive closed-form analytic solutions for the position and orientation of a passive polygon moving in sliding and rolling contact with two or three active polygons whose positions and orientations are independently controlled. This is accomplished by applying a simple elimination procedure to solve the appropriate system of contact constraint equations. The benefits of having analytic solutions are numerous. For example, they eliminate the need for iterative nonlinear equation solving algorithms to determine the position and orientation of the passive polygon given the positions and orientations of the active ones. Also, because they contain the configuration variables of the active polygons and the relevant geometric parameters, models of geometric and control uncertainty can be readily incorpora...

A two-level search algorithm for motion planning is presented in this paper. The algorithm combines a multiheuristic local search algorithm with a subgoal graph based global guidance. A novel feature of the planner is that it can adjust the balance between local and global planning. As the experimental data suggests that the optimal balance depends on the problem, a scheduling mechanism is added to the algorithm to adjust the balance during planning. The resulting motion planner is capable of solving very difficult motion planning problems. 1. Introduction In the basic variation of motion planning, the task is to generate a collision-free path for a movable object among known and static obstacles. A considerable amount of research has been conducted during the last two decades, but because of the hardness of the problem, no single overall method for motion planning is available. Therefore, research in robot motion planning remains as one of the important fields of study in the task o...

An efficient path planning algorithm for general 6 degrees of freedom robots is presented in the paper. The path planner is based on multiheuristic A * search algorithm with dynamic subgoal generation for rapid escaping from deep local-minimum wells. The algorithm has been implemented as an extension to a robot off-line programming and simulation system for testing. The presented test results demonstrate that the algorithm is practically applicable to path planning for devices of different kinematic structure. INTRODUCTION Computation of a collision-free path for an movable object among obstacles is an important problem in the fields of robotics, CIM and AI. Various automatic task level programming systems can be build for robot guidance, teleoperation, assembly and disassembly among others, if a suitable method for path planning is available. In this paper an algorithm for path planning is presented. It is an extension of the well known and widely applied A * search algorithm (H...

Abstract — In this paper, we propose a motion planning method of multiple mobile robots for cooperative transportation of a large object in a three-dimensional environment. This task has various kinds of problems, such as obstacle avoidance and stable manipulation. All of these problems cannot be solved at once, since it would result in a dramatic increase of the computational time. Accordingly, we divided the motion planner into a global path planner and a local manipulation planner, designed them, and integrated them. The aim was to integrate a gross motion planner and a fine motion planner. Concerning the global path planner, we reduced the dimensions of the configuration space (C-space) using the feature of transportation by mobile robots. We used the potential field to find the solution by searching in this smaller-dimension reconstructed C-space. In the global path planner, the constraints of the object manipulation are considered as the cost function and the heuristic function in the A ∗ search. For the local manipulation planner, we developed a manipulation technique, which is suitable for mobile robots by position control. We computed the conditions in which the object becomes unstable during manipulation and generated each robot’s motion considering the robots ’ motion errors and indefinite factors from the planning stage. We verified the effectiveness of our proposed motion planning method through simulations. Index Terms — Motion planning, multiple mobile robots, cooperative transportation, cooperative manipulation. I.

This article provides a brief tutorial-cum-overview of motion planning for "flexible" shapes. The article takes the point of view that motion planning for flexible shapes, in a broad sense, essentially amounts to motion planning for systems with many degrees of freedom (dofs), a well studied problem in robotics. The article starts with the basics of motion planning including introduction to some key concepts, surveys a number of recent approaches to solve the motion planning for systems with many dofs, and then discusses the application of some of these approaches to motion planning for flexible shapes and reports on some recent work in this area. I. Introduction Motion planning, in its broadest sense, refers to the ability of a system to automatically plan its motions. It is considered central to the development of autonomous robots and encompasses issues in fields as diverse as artificial intelligence, computational geometry, machine perception, mechanics and control theory. The pa...