Motion planning for multiple non-holonomic robots: a geometric approach

Robotica ◽  
2008 ◽  
Vol 26 (4) ◽  
pp. 525-536 ◽  
Author(s):  
Elias K. Xidias ◽  
Nikos A. Aspragathos
Keyword(s):  
Genetic Algorithm ◽  
Euclidean Space ◽  
Motion Planning ◽  
Optimization Problem ◽  
Geometric Approach ◽  
Dynamic Environments ◽  
Planning Problem ◽  
Geometrical Approach ◽  
Moving Obstacles ◽  
Geometric Entity

SUMMARYIn this paper, a geometrical approach is developed to generate simultaneously optimal (or near-optimal) smooth paths for a set of non-holonomic robots, moving only forward in a 2D environment cluttered with static and moving obstacles. The robots environment is represented by a 3D geometric entity called Bump-Surface, which is embedded in a 4D Euclidean space. The multi-motion planning problem (MMPP) is resolved by simultaneously finding the paths for the set of robots represented by monoparametric smooth C2 curves onto the Bump-Surface, such that their inverse images onto the initial 2D workspace satisfy the optimization motion-planning criteria and constraints. The MMPP is expressed as an optimization problem, which is solved on the Bump-Surface using a genetic algorithm. The performance of the proposed approach is tested through a considerable number of simulated 2D dynamic environments with car-like robots.

Motion Planning of a Nonholonomic Multibody System Using Genetic Algorithm

2003 ◽  
Author(s):  
Xin-Sheng Ge ◽  
Li-Qun Chen
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Nonholonomic System ◽  
Multibody System ◽  
Planning Problem ◽  
Control Approach ◽  
Velocity Constraints ◽  
Nonholonomic Motion Planning ◽  
Optimal Control Approach ◽  
Motion Planning Problem

The motion planning problem of a nonholonomic multibody system is investigated. Nonholonomicity arises in many mechanical systems subject to nonintegrable velocity constraints or nonintegrable conservation laws. When the total angular momentum is zero, the control problem of system can be converted to the motion planning problem for a driftless control system. In this paper, we propose an optimal control approach for nonholonomic motion planning. The genetic algorithm is used to optimize the performance of motion planning to connect the initial and final configurations and to generate a feasible trajectory for a nonholonomic system. The feasible trajectory and its control inputs are searched through a genetic algorithm. The effectiveness of the genetic algorithm is demonstrated by numerical simulation.

Motion Planning of Mobile Robots for Occluded Obstacles

2018 ◽  
Vol 30 (3) ◽  
pp. 485-492
Author(s):  
Satoshi Hoshino ◽  
◽  
Tomoki Yoshikawa
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Dynamic Environments ◽  
Simulation Experiments ◽  
Moving Obstacles ◽  
Moving Direction ◽  
Velocity Obstacle

Motion planning of mobile robots for occluded obstacles is a challenge in dynamic environments. The occlusion problem states that if an obstacle suddenly appears from the occluded area, the robot might collide with the obstacle. To overcome this, we propose a novel motion planner, the Velocity Obstacle for occlusion (VOO). The VOO is based on a previous motion planner, the Velocity Obstacle (VO), which is effective for moving obstacles. In the proposed motion planner, information uncertainties about occluded obstacles, such as position, velocity, and moving direction, are quantitatively addressed. Thus, the robot based on the VOO is able to move not only among observed obstacles, but also among the occluded ones. Through simulation experiments, the effectiveness of the VOO for the occlusion problem is demonstrated by comparison with the VO.

scholarly journals A geometrical approach to the motion planning problem for a submerged rigid body

2009 ◽  
Vol 82 (9) ◽  
pp. 1641-1656 ◽  
Author(s):  
Ryan N. Smith ◽  
Monique Chyba ◽  
George R. Wilkens ◽  
Christopher J. Catone
Keyword(s):  
Rigid Body ◽  
Motion Planning ◽  
Planning Problem ◽  
Geometrical Approach ◽  
Motion Planning Problem

A new motion planning method for discretely actuated hyper-redundant manipulators

Robotica ◽  
2015 ◽  
Vol 35 (1) ◽  
pp. 101-118 ◽  
Author(s):  
Alireza Motahari ◽  
Hassan Zohoor ◽  
Moharam Habibnejad Korayem
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Trajectory Tracking ◽  
Inverse Kinematic ◽  
Planning Problem ◽  
Redundant Manipulators ◽  
Tracking Problem ◽  
Parallel Mechanisms ◽  
Redundant Manipulator ◽  
The Common

SUMMARYA hyper-redundant manipulator is made by mounting the serial and/or parallel mechanisms on top of each other as modules. In discrete actuation, the actuation amounts are a limited number of certain values. It is not feasible to solve the kinematic analysis problems of discretely actuated hyper-redundant manipulators (DAHMs) by using the common methods, which are used for continuous actuated manipulators. In this paper, a new method is proposed to solve the trajectory tracking problem in a static prescribed obstacle field. To date, this problem has not been considered in the literature. Theremoving first collision(RFC) method, which is originally proposed for solving the inverse kinematic problems in the obstacle fields was modified and used to solve the motion planning problem. For verification, the numerical results of the proposed method were compared with the results of thegenetic algorithm(GA) method. Furthermore, a novel DAHM designed and implemented by the authors is introduced.

scholarly journals The Multiagent Planning Problem

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Tamás Kalmár-Nagy ◽  
Giovanni Giardini ◽  
Bendegúz Dezső Bak
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Problem Solver ◽  
Planning Problem ◽  
Genetic Operators ◽  
Total Distance ◽  
Modified Genetic Algorithm ◽  
Multiagent Planning ◽  
Simple Paths ◽  
Better Than

The classical Multiple Traveling Salesmen Problem is a well-studied optimization problem. Given a set ofngoals/targets andmagents, the objective is to findmround trips, such that each target is visited only once and by only one agent, and the total distance of these round trips is minimal. In this paper we describe the Multiagent Planning Problem, a variant of the classical Multiple Traveling Salesmen Problem: given a set ofngoals/targets and a team ofmagents,msubtours (simple paths) are sought such that each target is visited only once and by only one agent. We optimize for minimum time rather than minimum total distance; therefore the objective is to find the Team Plan in which the longest subtour is as short as possible (a min–max problem). We propose an easy to implement Genetic Algorithm Inspired Descent (GAID) method which evolves a set of subtours using genetic operators. We benchmarked GAID against other evolutionary algorithms and heuristics. GAID outperformed the Ant Colony Optimization and the Modified Genetic Algorithm. Even though the heuristics specifically developed for Multiple Traveling Salesmen Problem (e.g.,k-split, bisection) outperformed GAID, these methods cannot solve the Multiagent Planning Problem. GAID proved to be much better than an open-source Matlab Multiple Traveling Salesmen Problem solver.

Motion Planning for Industrial Mobile Robots With Closed-Loop Stability Enhanced Prediction

2019 ◽  
Author(s):  
Jessica Leu ◽  
Masayoshi Tomizuka
Keyword(s):  
Motion Planning ◽  
Closed Loop ◽  
Dynamic Environments ◽  
Open Loop ◽  
Human Robot Interaction ◽  
Planning Problem ◽  
Prediction Horizon ◽  
Robot Systems ◽  
Planning Algorithm ◽  
Loop Stability

Abstract Real-time, safe, and stable motion planning in co-robot systems involving dynamic human robot interaction (HRI) remains challenging due to the time varying nature of the problem. One of the biggest challenges is to guarantee closed-loop stability of the planning algorithm in dynamic environments. Typically, this can be addressed if there exists a perfect predictor that precisely predicts the future motions of the obstacles. Unfortunately, a perfect predictor is not possible to achieve. In HRI environments in this paper, human workers and other robots are the obstacles to the ego robot. We discuss necessary conditions for the closed-loop stability of a planning problem using the framework of model predictive control (MPC). It is concluded that the predictor needs to be able to detect the obstacles’ movement mode change within a time delay allowance and the MPC needs to have a sufficient prediction horizon and a proper cost function. These allow MPC to have an uncertainty tolerance for closed-loop stability, and still avoid collision when the obstacles’ movement is not within the tolerance. Also, the closed-loop performance is investigated using a notion of M-convergence, which guarantees finite local convergence (at least M steps ahead) of the open-loop trajectories toward the closed-loop trajectory. With this notion, we verify the performance of the proposed MPC with stability enhanced prediction through simulations and experiments. With the proposed method, the robot can better deal with dynamic environments and the closed-loop cost is reduced.

scholarly journals Path Planning for a Space-Based Manipulator System Based on Quantum Genetic Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Zhengcang Chen ◽  
Weijia Zhou
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Optimization Problem ◽  
Planning Problem ◽  
Positional Error ◽  
End Effector ◽  
Quantum Genetic Algorithm ◽  
Object A ◽  
Floating Base ◽  
Six Degree Of Freedom

In this study, by considering a space-based, n-joint manipulator system as research object, a kinematic and a dynamic model are constructed and the system’s nonholonomic property is discussed. In light of the nonholonomic property unique to space-based systems, a path planning method is introduced to ensure that when an end-effector moves to the desired position, a floating base achieves the expected pose. The trajectories of the joints are first parameterized using sinusoidal polynomial functions, and cost functions are defined by the pose deviation of the base and the positional error of the end-effector. At this stage, the path planning problem is converted into a target optimization problem, where the target is a function of the joints. We then adopt a quantum genetic algorithm (QGA) to solve this objective optimization problem to attain the optimized trajectories of the joints and then execute nonholonomic path planning. To test the proposed method, we carried out a simulation on a six-degree-of-freedom (DOF) space-based manipulator system (SBMS). The results showed that, compared to traditional genetic optimization algorithms, the QGA converges more rapidly and has a more accurate output.

Interactive Motion Planning for Mobile Robot Navigation in Dynamic Environments

2017 ◽  
Vol 21 (4) ◽  
pp. 667-674 ◽  
Author(s):  
Satoshi Hoshino ◽  
◽  
Kenichiro Uchida
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Robot Navigation ◽  
Dynamic Environments ◽  
Human Robot Interaction ◽  
Mobile Robot Navigation ◽  
Robot Interaction ◽  
Moving Obstacles ◽  
Local Path ◽  
Path Planner

In dynamic environments, taking static and moving obstacles into consideration in motion planning for mobile robot navigation is a technical issue. In this paper, we use a single mobile robot, for which humans are moving obstacles. Since moving humans sometimes get in the way of the robot, it must avoid collisions with them. Furthermore, if a part of the environment is crowded with humans, it is better for the robot to detour around the congested area. For this navigational challenge, we focus on the interaction between humans and the robot, so this paper proposes a motion planner for successfully getting through the human-robot interaction. The interactive motion planner is based on the hybrid use of global and local path planners. Furthermore, the local path planner is executed repetitively during the navigation. Through the human-robot interaction, the robot is enabled not only to avoid the collisions with humans but also to detour around congested areas. The emergence of this movement is the main contribution of this paper. We discuss the simulation results in terms of the effectiveness of the proposed motion planner for robot navigation in dynamic environments that include humans.

scholarly journals Hybrid Motion Planning Method for Autonomous Robots Using Kinect Based Sensor Fusion and Virtual Plane Approach in Dynamic Environments

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Doopalam Tuvshinjargal ◽  
Byambaa Dorj ◽  
Deok Jin Lee
Keyword(s):  
Motion Planning ◽  
Sensor Fusion ◽  
Field Experiments ◽  
Autonomous Robots ◽  
Autonomous Vehicle ◽  
Dynamic Environments ◽  
Obstacle Detection ◽  
Planning Method ◽  
Moving Obstacles ◽  
Virtual Plane

A new reactive motion planning method for an autonomous vehicle in dynamic environments is proposed. The new dynamic motion planning method combines a virtual plane based reactive motion planning technique with a sensor fusion based obstacle detection approach, which results in improving robustness and autonomy of vehicle navigation within unpredictable dynamic environments. The key feature of the new reactive motion planning method is based on a local observer in the virtual plane which allows the effective transformation of complex dynamic planning problems into simple stationary in the virtual plane. In addition, a sensor fusion based obstacle detection technique provides the pose estimation of moving obstacles by using a Kinect sensor and a sonar sensor, which helps to improve the accuracy and robustness of the reactive motion planning approach in uncertain dynamic environments. The performance of the proposed method was demonstrated through not only simulation studies but also field experiments using multiple moving obstacles even in hostile environments where conventional method failed.

Sign in / Sign up

Export Citation Format

Share Document