Motion planning

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

Time-optimal UAV trajectory planning for 3D urban structure coverage

Abstract: In this paper, we study the time-optimal trajectory planning of a sensor attached to an unmanned aerial vehicle (UAV) to provide complete 3-dimensional coverage with applications to urban environments with 2.5-dimensional features. The basic approach is to approximate the features of interest with a set of non planar coverage surfaces and to design a motion plan that guarantees the coverage surface is swept completely with a conical-field-of-view sensor. We establish a lower bound on time for a UAV to achieve complete coverage and derive the analytical coverage plan whose duration is a constant times this lower bound. Our hardware-in-the-loop simulation results verify the effectiveness of the proposed algorithm.

Motion Planning for Multiple Mobile R b ts using Dynamic Networks

Abstract: Abslrucl - A new motion planning framework is presented that enables multiple mobile robots with limited ranges of sensing and communication to maneuver and achieve goals safely in dynamic environments. To combine the respective advantages of centralized and de-centralized planning, this framework is based on the concept of centralized planning within dynamic robot networks. As the robots move in their environment, localized robot groups form networks, within which world models and robot goals can be shared. Whenever a network is formed, new information then becomes available to all robots in this network. With this new information, each robot uses a fast, centralized planner to compute new coordinated trajectories on the fly. Planning over several robot networks is decentralized and distributed. Both simulated and real-robot experiments have validated the approach.

Mobile robots at your fingertip: Bezier curve on-line trajectory generation for supervisory control

Abstract: A new interfacing method is presented to control mobile robot(s) in a supervised manner. Mobile robots often provide global position information to an operator. This research describes a method whereby the operator controls a mobile robot(s) using his finger or stylus via a touchpad or touch screen interface. Using a mapping between the robot's operational site and the input device, a human user can provide routing information for the mobile robot. Two algorithms have been developed to create the robot trajectory from the operator's input. Information regarding numerous path points is generated when the operator moves his finger/stylus. To prune away meaningless point information, a simple but powerful significant points extracting algorithm is developed. The resulting significant points are used as waypoints. An on-line piecewise cubic Bezier curves (PCBC) trajectory generation algorithm is presented to create a smooth trajectory for these significant points. As the method is based on distance and not on time, the velocity of mobile robot can be controlled easily within its allowable dynamic range. The PCBC trajectory can also be modified on the fly. Simulation results are presented to verify these newly developed methods.

Planning constrained motion

Abstract: We consider the motion planning problem for a point constrained to move along a path with radius of curvature at least one. The point moves in a two-dimensional universe with polygonal obstacles. We show the decidability of the reachability question: “Given a source placement (position and direction pair) and a target placement, is there a curvature-constrained path from source to target avoiding obstacles?” The decision procedure has time and space complexity 2o(poly(n, m)) wheren is the number of corners andm is the number of bits required to specify the position of corners.

Search-Based Path Planning with Homotopy Class Constraints

Abstract: Goal-directed path planning is one of the basic and widely studied problems in the field of mobile robotics. Homotopy classes of trajectories, arising due to the presence of obstacles, are defined as sets of trajectories that can be transformed into each other by gradual bending and stretching without colliding with obstacles. The problem of finding least-cost paths restricted to a specific homotopy class or finding least-cost paths that do not belong to certain homotopy classes arises frequently in such applications as predicting paths for dynamic entities and computing heuristics for path planning with dynamic constraints. In the present work, we develop a compact way of representing homotopy classes and propose an efficient method of graph search-based optimal path planning with constraints on homotopy classes. The method is based on representing the environment of the robot as a complex plane and making use of the Cauchy Integral Theorem. We prove optimality of the method and show its efficiency experimentally.