Motion planning

About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.

Neural RRT*: Learning-Based Optimal Path Planning

Abstract: Rapidly random-exploring tree (RRT) and its variants are very popular due to their ability to quickly and efficiently explore the state space. However, they suffer sensitivity to the initial solution and slow convergence to the optimal solution, which means that they consume a lot of memory and time to find the optimal path. It is critical to quickly find a short path in many applications such as the autonomous vehicle with limited power/fuel. To overcome these limitations, we propose a novel optimal path planning algorithm based on the convolutional neural network (CNN), namely the neural RRT* (NRRT*). The NRRT* utilizes a nonuniform sampling distribution generated from a CNN model. The model is trained using quantities of successful path planning cases. In this article, we use the A* algorithm to generate the training data set consisting of the map information and the optimal path. For a given task, the proposed CNN model can predict the probability distribution of the optimal path on the map, which is used to guide the sampling process. The time cost and memory usage of the planned path are selected as the metric to demonstrate the effectiveness and efficiency of the NRRT*. The simulation results reveal that the NRRT* can achieve convincing performance compared with the state-of-the-art path planning algorithms. Note to Practitioners —The motivation of this article stems from the need to develop a fast and efficient path planning algorithm for practical applications such as autonomous driving, warehouse robot, and countless others. Sampling-based algorithms are widely used in these areas due to their good scalability and high efficiency. However, the quality of the initial path is not guaranteed and it takes much time to converge to the optimal path. To quickly obtain a high-quality initial path and accelerate the convergence speed, we propose the NRRT*. It utilizes a nonuniform sampling distribution and achieves better performance. The NRRT* can be also applied to other sampling-based algorithms for improved results in different applications.

Global Manipulation Planning in Robot Joint Space With Task Constraints

Abstract: We explore global randomized joint-space path planning for articulated robots that are subjected to task-space constraints. This paper describes a representation of constrained motion for joint-space planners and develops two simple and efficient methods for constrained sampling of joint configurations: tangent-space sampling (TS) and first-order retraction (FR). FR is formally proven to provide global sampling for linear task-space transformations. Constrained joint-space planning is important for many real-world problems, which involves redundant manipulators. On the one hand, tasks are designated in workspace coordinates: to rotate doors about fixed axes, to slide drawers along fixed trajectories, or to hold objects level during transport. On the other hand, joint-space planning gives alternative paths that use redundant degrees of freedom (DOFs) to avoid obstacles or satisfy additional goals while performing a task. We demonstrate that our methods are faster and more invariant to parameter choices than the techniques that exist.

Path planning and collision avoidance for movement of instruments in a radiation therapy environment

Abstract: A patient positioning system for use with a radiation therapy system that monitors the location of fixed and movable components and pre-plans movement of the movable components so as to inhibit movement if a collision would be indicated. The positioning system can also coordinate movement of multiple movable components for reduced overall latency in registering a patient. The positioning system includes external measurement devices which measure the location and orientation of objects, including components of the radiation therapy system, in space and can also monitor for intrusion into the active area of the therapy system by personnel or foreign objects to improve operational safety of the radiation therapy system.

Path planning for deformable linear objects

Abstract: We present a new approach to path planning for deformable linear (one-dimensional) objects such as flexible wires. We introduce a method for efficiently computing stable configurations of a wire subject to manipulation constraints. These configurations correspond to minimal-energy curves. By restricting the planner to minimal-energy curves, the execution of a path becomes easier. Our curve representation is adaptive in the sense that the number of parameters automatically varies with the complexity of the underlying curve. We introduce a planner that computes paths from one minimal-energy curve to another such that all intermediate curves are also minimal-energy curves. This planner can be used as a powerful local planner in a sampling-based roadmap method. This makes it possible to compute a roadmap of the entire "shape space," which is not possible with previous approaches. Using a simplified model for obstacles, we can find minimal-energy curves of fixed length that pass through specified tangents at given control points. Our work has applications in cable routing, and motion planning for surgical suturing and snake-like robots

Collision-Free Motion Planning of Two Robots

Abstract: An approach to collision-free motion planning of two moving robots in a common workspace is presented. Each robot is represented by a sphere containing the wrist and the manipulator hand. The results from a strictly straight line trajectory planning method are utilized for planning a path avoiding potential collisions. Due to the distinct nature of the potential collisions between the two moving robots, a new classification of path requirement situations is presented and utilized for planning a collision-free path. Notions of collision map and time scheduling are developed and applied for realizing a collision-free motion planning. A procedure is developed for the time scheduling of the straight line trajectory. An example is shown for the time scheduling of the trajectory, which shows the significance of the proposed approach in collision-free motion planning of the two moving robots.