Motion Planning Using Dynamic Roadmaps

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.

Proceedings of the International Symposium on Combinatorial Search, 2021

Robust robot motion planning in dynamic environments requires that actions be selected under real-time constraints. Existing heuristic search methods that can plan high-speed motions do not guarantee real-time performance in dynamic environments. Existing heuristic search methods for realtime planning in dynamic environments fail in the highdimensional state space required to plan high-speed actions. In this paper, we present extensions to a leading planner for high-dimensional spaces, R*, that allow it to guarantee realtime performance, and extensions to a leading real-time planner, LSS-LRTA*, that allow it to succeed in dynamic motion planning. In an extensive empirical comparison, we show that the new methods are superior to the originals, providing new state-of-the-art search performance on this challenging problem.

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.

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.

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.

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.

Journal of Automation, Mobile Robotics and Intelligent Systems

Path planning is an essential part of the control system of any mobile robot. In this article the path planner for a humanoid robot is presented. The short description of an universal control framework and the Motion Generation System is also presented. Described path planner utilizes a limited number of motions called the Motion Primitives. They are generated by Motion Generation System. Four different algorithms, namely the: Informed RRT, Informed RRT with random bias, and RRT with A* likeheuristics were tested. For the last one the version with biased random function was also considered. All mentioned algorithms were evaluated considering three dif ferent scenarios. Obtained results are described and discussed.

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008

Motion planning for robots with many degrees of freedom (DoF) is a generally unsolved problem in the robotics context. In this work an approach for trajectory planning is presented, which takes account of the different kinematic parts of a humanoid robot. Since not all joints of the robot are important for different planning phases, the RRT-based planner is able to adapt the number of DoF on the fly to improve the performance and the quality of the results. The runtime of the approach is evaluated in comparison to a standard RRT planner. Futhermore several extensions to the algorithm are investigated.

5th IEEE-RAS International Conference on Humanoid Robots, 2005., 2005

This paper addresses an integrated humanoid motion planning scheme including both advanced algorithmic motion planning technique and dynamic pattern generator so that the humanoid robot achieve tasks including dynamic motions. A two-stage approach is proposed for this goal. First, geometric and kinematic motion planner first computes collision-free paths for the humanoid robot. Then the dynamic pattern generator provides dynamically feasible humanoid motion including both locomotion and task execution such as object transportation or manipulation. If the generated dynamic motion causes collision due to dynamic movements, the planner go back to the planning stage to remove the collision by path reshaping. This iterative planning scheme enables robust planning against variation of task dynamics. Simulation results are provided to validate the proposed planning method.

2006

Tree-based path planners have been shown to be well suited to solve various high dimensional motion planning problems. Here we present a variant of the Rapidly-Exploring Random Tree (RRT) path planning algorithm that is able to explore narrow passages or difficult areas more effectively. We show that both workspace obstacle information and C-space information can be used when deciding which direction to grow. The method includes many ways to grow the tree, some taking into account the obstacles in the environment. This planner works best in difficult areas when planning for free flying rigid or articulated robots. Indeed, whereas the standard RRT can face difficulties planning in a narrow passage, the tree based planner presented here works best in these areas.