Landmark Guided Probabilistic Roadmap Queries

A landmark based heuristic is investigated for reducing query phase run-time of the probabilistic roadmap (\PRM) motion planning method. The heuristic is generated by storing minimum spanning trees from a small number of vertices within the \PRM graph and using these trees to approximate the cost of a shortest path between any two vertices of the graph. The intermediate step of preprocessing the graph increases the time and memory requirements of the classical motion planning technique in exchange for speeding up individual queries making the method advantageous in multi-query applications. This paper investigates these trade-offs on \PRM graphs constructed in randomized environments as well as a practical manipulator simulation.We conclude that the method is preferable to Dijkstra's algorithm or the ${\rm A}^*$ algorithm with conventional heuristics in multi-query applications.Comment: 7 Page

Cell Representations of the Configuration Space for Planning Optimal Paths

This paper proposes sampling techniques to approximate the configuration space for optimal motion planning. We sample valid configurations in the workspace and construct path subconvex cells in the free configuration space. The radius of each cell is calculated using lower bounds on the robot’s minimum time to collision. Using theorems about path convexity, the shortest paths found between any two points in the decomposed space are guaranteed to be safe. Experimental results are provided for a planar arm

Motion in the Field: A Study of Movement in Computer Science

In this project, I use a system that combines the use of a motion controller to obtain information about physical motion of a user with the transmission of outputs to a quadcopter that interacts with the physical environment. In the manual mode, the user sends commands to the quadcopter using hand controls. I utilized an existing system but increased usability to prevent accidental flight and created commands that are more intuitive for the user. In the automatic mode, the user gives a single command to the motion controller and it runs the A* path planning algorithm over a representation of a known environment to make the quadcopter find and execute the shortest path to its goal

End-to-end one-shot path-planning algorithm for an autonomous vehicle based on a convolutional neural network considering traversability cost

Path planning plays an important role in navigation and motion planning for robotics and automated driving applications. Most existing methods use iterative frameworks to calculate and plan the optimal path from the starting point to the endpoint. Iterative planning algorithms can be slow on large maps or long paths. This work introduces an end-to-end path-planning algorithm based on a fully convolutional neural network (FCNN) for grid maps with the concept of the traversability cost, and this trains a general path-planning model for 10 × 10 to 80 × 80 square and rectangular maps. The algorithm outputs the lowest-cost path while considering the cost and the shortest path without considering the cost. The FCNN model analyzes the grid map information and outputs two probability maps, which show the probability of each point in the lowest-cost path and the shortest path. Based on the probability maps, the actual optimal path is reconstructed by using the highest probability method. The proposed method has superior speed advantages over traditional algorithms. On test maps of different sizes and shapes, for the lowest-cost path and the shortest path, the average optimal rates were 72.7% and 78.2%, the average success rates were 95.1% and 92.5%, and the average length rates were 1.04 and 1.03, respectively

Mobile Robot Team Forming for Crystallization of Proteins

The process of protein crystallization is explained using the theory of robotics, particularly path planning of mobile robots. Path planning is a procedure which specifies motion trajectories of multiple mobile robots to form a robotic team with a desired pattern. Since protein crystals consist of a large number of protein molecules which come together to form a 3D lattice of uniform structure, it is hypothesized that each protein behaves like a mobile robot and takes adequate path to form a robotic team (crystal). Based on this hypothesis, it is shown that trajectories of the proteins should be simple and local, which generates three rules of motion for the protein robots, i.e., a. each protein searches for its nearest neighbor, b. each protein takes the shortest path to approach the nearest neighbor, and c. multiple proteins may form a sub-team of proteins. It is then proven mathematically that the planned path according to the three rules is stable and able to crystallize the proteins. Interaction forces at the molecular level are analyzed to show that the simple and local motion of the proteins is physically warranted. Computer simulation and experimental results are presented to validate the new theory. Keywords Mobile robots, robot team forming, protein crystallization, simplicity and locality. 3/5/2007 draft 1 1

Path Planning Algorithm using D* Heuristic Method Based on PSO in Dynamic Environment

This paper is devoted to find a short and safe path for robot in environment with moving obstacles such as different objects, humans, animals or other robots. A mixing approach of robot path planning using the heuristic method D star (D*) algorithm based on optimization technique is used. The heuristic D* method is chosen for finding the shortest path. Furthermore, to insure the path length optimality and for enhancing the final path, it has been utilized the Particle Swarm Optimization (PSO) technique. This paper focuses on computational part of motion planning in completely changing dynamic environment at every motion sample domains. Simulation results are given to show the effectiveness of the proposed method.                                                                                                                                           

A motion planner for nonholonomic mobile robots

This paper considers the problem of motion planning for a car-like robot (i.e., a mobile robot with a nonholonomic constraint whose turning radius is lower-bounded). We present a fast and exact planner for our mobile robot model, based upon recursive subdivision of a collision-free path generated by a lower-level geometric planner that ignores the motion constraints. The resultant trajectory is optimized to give a path that is of near-minimal length in its homotopy class. Our claims of high speed are supported by experimental results for implementations that assume a robot moving amid polygonal obstacles. The completeness and the complexity of the algorithm are proven using an appropriate metric in the configuration space R^2 x S^1 of the robot. This metric is defined by using the length of the shortest paths in the absence of obstacles as the distance between two configurations. We prove that the new induced topology and the classical one are the same. Although we concentrate upon the car-like robot, the generalization of these techniques leads to new theoretical issues involving sub-Riemannian geometry and to practical results for nonholonomic motion planning

Lazy Receding Horizon A* for Efficient Path Planning in Graphs with Expensive-to-Evaluate Edges

Motion-planning problems, such as manipulation in cluttered environments, often require a collision-free shortest path to be computed quickly given a roadmap graph. Typically, the computational cost of evaluating whether an edge of the roadmap graph is collision-free dominates the running time of search algorithms. Algorithms such as Lazy Weighted A* (LWA*) and LazySP have been proposed to reduce the number of edge evaluations by employing a lazy lookahead (one-step lookahead and infinite-step lookahead, respectively). However, this comes at the expense of additional graph operations: the larger the lookahead, the more the graph operations that are typically required. We propose Lazy Receding-Horizon A* (LRA*) to minimize the total planning time by balancing edge evaluations and graph operations. Endowed with a lazy lookahead, LRA* represents a family of lazy shortest-path graph-search algorithms that generalizes LWA* and LazySP. We analyze the theoretic properties of LRA* and demonstrate empirically that, in many cases, to minimize the total planning time, the algorithm requires an intermediate lazy lookahead. Namely, using an intermediate lazy lookahead, our algorithm outperforms both LWA* and LazySP. These experiments span simulated random worlds in $\mathbb{R}^2$ and $\mathbb{R}^4$, and manipulation problems using a 7-DOF manipulator.Comment: 16 pages; typos corrected; revised text; results unchange