Motion Planning Using Dynamic Roadmaps (original) (raw)
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Full-Body Motion Planning for Humanoid Robots using Rapidly Exploring Random Trees
KI - Künstliche Intelligenz, 2016
Humanoid robots with many degrees of freedom have an enormous range of possible motions. To be able to move in complex environments and dexterously manipulate objects, humanoid robots must be capable of creating and executing complex sequences of motions to accomplish their tasks. For soccer playing robots (e.g., the participants of RoboCup), the highly dynamic environment require real-time motion planning in spite of the enormous search space of possible motions. In this research, we propose a practical solution to the general movers problem in the context of motion planning for robots. The proposed robot motion planner uses a sample-based tree planner combined with an incremental simulator that models not only collisions, but also the dynamics of the motion. Thus it can ensure that the robot will be dynamically stable while executing the motion. The effectiveness of the robot motion planner is demonstrated both in simulation and on a real robot, using a variation of the Rapidly Exploring Random Tree (RRT) type of motion planner. The results of our empirical evaluation show that CONNECT works better than EXTEND versions of the RRT algorithms in simple domains, but that this advantage disappears in more obstacle-filled environments. The evaluation also shows that our motion planning system is able to find and execute complex motion plans for a small humanoid robot.
Fast and Feasible Deliberative Motion Planner for Dynamic Environments
2009
We present an approach to the problem of differentially constrained mobile robot motion planning in arbitrary time-varying cost fields. We construct a special search space which is ideally suited to the requirements of dynamic environments including a) feasible motion plans that satisfy differential constraints, b) efficient plan repair at high update rates, and c) deliberative goal-directed behavior on scales well beyond the effective range of perception sensors. The search space contains edges which adapt to the state sampling resolution yet aquire states exactly in order to permit the use of the dynamic programming principle without introducing infeasibility. It is a symmetric lattice based on a repeating unit of controls which permits off-line computation of the planner heuristic, motion simulation, and the swept volumes associated with each motion. For added planning efficiency, the search space features fine resolution near the vehicle and reduced resolution far away. Furthermore, its topology is updated in real-time as the vehicle moves in such a way that the underlying motion planner processes changing topology as an equivalent change in the dynamic environment. The planner was originally developed to cope with the reduced computation available on the Mars rovers. Experimental results with research prototype rovers demonstrate that the planner allows us to exploit the entire envelope of vehicle maneuverability in rough terrain, while featuring real-time performance.
Rapidly-exploring roadmaps: Weighing exploration vs. refinement in optimal motion planning
2011
Computing globally optimal motion plans requires exploring the configuration space to identify reachable free space regions as well as refining understanding of already explored regions to find better paths. We present the rapidly-exploring roadmap (RRM), a new method for single-query optimal motion planning that allows the user to explicitly consider the trade-off between exploration and refinement. RRM initially explores the configuration space like a rapidly exploring random tree (RRT). Once a path is found, RRM uses a user-specified parameter to weigh whether to explore further or to refine the explored space by adding edges to the current roadmap to find higher quality paths in the explored space. Unlike prior methods, RRM does not focus solely on exploration or refine prematurely. We demonstrate the performance of RRM and the trade-off between exploration and refinement using two examples, a point robot moving in a plane and a concentric tube robot capable of following curved trajectories inside patient anatomy for minimally invasive medical procedures.
Trajectory planning for robots in dynamic human environments
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010
This paper presents a trajectory planning algorithm for a robot operating in dynamic human environments. Environments such as pedestrian streets, hospital corridors, train stations or airports. We formulate the problem as planning a minimal cost trajectory through a potential field, defined from the perceived position and motion of persons in the environment. A Rapidly-exploring Random Tree (RRT) algorithm is proposed as a solution to the planning problem, and a new method for selecting the best trajectory in the RRT, according to the cost of traversing a potential field, is presented. The RRT expansion is enhanced to account for the kinodynamic robot constraints by using a robot motion model and a controller to add a reachable vertex to the tree. Instead of executing a whole trajectory, when planned, the algorithm uses a Model Predictive Control (MPC) approach, where only a short segment of the trajectory is executed while a new iteration of the RRT is computed. The planning algorithm is demonstrated in a simulated pedestrian street environment.
Task space motion planning using reactive control
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010
In this paper we present an approach to reduce the effort for planning robot motions by shifting the planning problem to a high-level representation. We combine classical sampling-based random tree planning with a reactive controller connecting sampling points with nontrivial trajectories, utilizing redundant DOFs to locally avoid obstacles. While the reactive planner operates locally on a short time scale, the complementary sampling-based method is able to find globally feasible solutions due to its larger preview horizon. Additionally, planning is done in a low-dimensional task space instead of the high-dimensional joint space. Comparing the average planning time and number of tree extensions for several scenarios and planning methods, we demonstrate that this hybrid planning approach is capable of solving a large fraction of planning queries while saving considerable planning time.
KidVO: A Kinodynamically Consistent Algorithm for Online Motion Planning in Dynamic Environments
Industrial Robot, 2016
In this research, an efficient method, called Kinodynamic Velocity Obstacle (KidVO), will be proposed for motion planning of omnimobile robots considering kinematic and dynamic constraints (KDCs). The suggested method improves Generalized Velocity Obstacle approach by a systematic selection of proper time horizon. Selection procedure of time horizon is based on kinematical and dynamical restrictions of the robot. Towards this aim, an omnimobile robot with a general geometry is taken into account and the admissible velocity and acceleration cones reflecting KDCs, are derived, respectively. To prove the advantages of the suggested planning method, its performance is compared with Generalized Velocity Obstacles (GVO), the so-called Hamilton-Jacobi-Bellman (HJB), and Rapidly-exploring Random Tree (RRT). The obtained results of the presented scenarios which contain both computer and real-world experiments for complicated crowded environments indicate the merits of the suggested methodology in terms of its near-optimal behavior, successful obstacle avoidance both in static and dynamic environments, and reaching to the goal pose.
2011
Existing sampling-based robot motion planning methods are often inefficient at finding trajectories for kinodynamic systems, especially in the presence of narrow passages between obstacles and uncertainty in control and sensing. To address this, we propose EG-RRT, an Environment-Guided variant of RRT designed for kinodynamic robot systems that combines elements from several prior approaches and may incorporate a cost model based on the LQG-MP framework to estimate the probability of collision under uncertainty in control and sensing. We compare the performance of EG-RRT with several prior approaches on challenging sample problems. Results suggest that EG-RRT offers significant improvements in performance.
Efficient and safe on-line motion planning in dynamic environments
2009 IEEE International Conference on Robotics and Automation, 2009
This paper presents a new on-line planner for dynamic environments that is based on the concept of Velocity Obstacles (VO). It addresses the issue of motion safety, i.e. avoiding states of inevitable collision, by selecting a proper time horizon for the velocity obstacle. The proper choice of the time horizon ensures that the boundary of the velocity obstacle coincides with the boundary of the set of inevitable collision states. This time horizon is determined by the minimum time it would take the robot to avoid collision, either by stopping or by passing the respective obstacle. The planner generates a near-time optimal trajectory to the goal by selecting at each time step the velocity that minimizes the time-to-go and is out of the velocity obstacle. The planner takes into account the shape, velocity, and path curvature of the obstacle's trajectory. It is demonstrated for on-line motion planning in very crowded static and dynamic environments.
RRT-connect: An efficient approach to single-query path planning
2000
A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementally building two Rapidly-exploring Random Trees (RRTs) rooted at the start and the goal configurations. The trees each explore space around them and also advance towards each other through the use of a simple greedy heuristic. Although originally designed to plan motions for a human arm (modeled as a 7-DOF kinematic chain) for the automatic graphic animation of collision-free grasping and manipulation tasks, the algorithm has been successfully applied to a variety of path planning problems. Computed examples include generating collision-free motions for rigid objects in 2D and 3D, and collision-free manipulation motions for a 6-DOF PUMA arm in a 3D workspace. Some basic theoretical analysis is also presented.
RDT+: A parameter-free algorithm for exact motion planning
2011 IEEE International Conference on Robotics and Automation, 2011
In this paper parameter-free concepts for exact motion planning are investigated. With the proposed RDT + approach the collision detection parameters of a Rapidlyexploring Dense Tree (RDT) are automatically adjusted until an exact solution can be found. For efficient planning discrete collision detection routines are used within the RDT planner and by verifying the results with exact collision detection methods, the RDT + concept allows to compute motions that are guaranteed collision-free. We show the probabilistic completeness of the proposed planner and present an extension for handling narrow passages.