Real-Time Robot Path Planning via a Distance-Propagating Dynamic System with Obstacle Clearance (original) (raw)

A CRITICAL EVALUATION OF LITERATURE ON ROBOT PATH PLANNING IN DYNAMIC ENVIRONMENT

Robot Path Planning (RPP) in dynamic environments is a search problem based on the examination of collision-free paths in the presence of dynamic and static obstacles. Many techniques have been developed to solve this problem. Trapping in a local minima and maintaining a Real-Time performance are known as the two most important challenges that these techniques face to solve such problem. This study presents a comprehensive survey of the various techniques that have been proposed in this domain. As part of this survey, we include a classification of the approaches and identify their methods.

Robot Motion Planning in Dynamic Environments with Moving Obstacles and Target

This paper presents a new sensor-based online method for generating collision-free near-optimal paths for mobile robots pursuing a moving target amidst dynamic and static obstacles. At each iteration, first the set of all collision-free directions are calculated using velocity vectors of the robot relative to each obstacle and target, forming the Directive Circle (DC), which is a novel concept. Then, a direction close to the shortest path to the target is selected from feasible directions in DC. The DC prevents the robot from being trapped in deadlocks or local minima. It is assumed that the target’s velocity is known, while the speeds of dynamic obstacles, as well as the locations of static obstacles, are to be calculated online. Extensive simulations and experimental results demonstrated the efficiency of the proposed method and its success in coping with complex environments and obstacles.

Path Planning with Real Time Obstacle Avoidance

International Journal of Computer Applications, 2013

One of the most important areas of research on mobile robots is that of their moving from one point in a space to the other and that too keeping aloof from the different objects in their path i.e. real time obstacle detection. This is the basic problem of path planning through a continuous domain. For this a large number of algorithms have been proposed of which only a few are really good as far as local and global path planning are concerned as to some extent a trade of has to be made between the efficiency of the algorithm and its accuracy. In this project an integrated approach for both local as well as global path planning of a robot has been proposed. The primary algorithm that has been used for path planning is the artificial Potential field approach [1] and a* search algorithm has been used for finding the most optimum path for the robot. Obstacle detection for collision avoidance (a high level planning problem) can be effectively done by doing complex robot operations in real time and distributing the whole problem between different control levels. This project proposes the artificial potential field algorithm not only for static but also for moving obstacles using real time potential field values by creating sub-goals which eventually lead to the main goal of the most optimal complete path found by the A* search algorithm. Apart from these scan line and convex hull techniques have been used to improve the robustness of the algorithm. To some extent the shape and size of a robot has also been taken into consideration. The effectiveness of these algorithms has been verified with a series of simulations.

Efficient Robot Path Planning in the Presence of Dynamically Expanding Obstacles

This paper presents a framework for robot path planning based on the A* search algorithm in the presence of dynamically expanding obstacles. The overall method follows Cellular Automata (CA) based rules, exploiting the discrete nature of CAs for both obstacle and robot state spaces. For the search strategy, the discrete properties of the A* algorithm were utilized, allowing a seamless merging of both CA and A* theories. The proposed algorithm guarantees both a collision free and a cost efficient path to target with optimal computational cost. More particular, it expands the map state space with respect to time using adaptive time intervals in order to predict the necessary future expansion of obstacles for assuring both a safe and a minimum cost path. The proposed method can be considered as being a general framework in the sense that it can be applied to any arbitrary shaped obstacle.

Real-time obstacle avoidance for polygonal robots with a reduced dynamic window

2002

In this paper we present an approach to obstacle avoidance and local path planning for polygonal robots. It decomposes the task into a model stage and a planning stage. The model stage accounts for robot shape and dynamics using a reduced dynamic window. The planning stage produces collision-free local paths with a velocity profile. We present an analytical solution to the distance to collision problem for polygonal robots, avoiding thus the use of look-up tables. The approach has been tested in simulation and on two non-holonomic rectangular robots where a cycle time of 10 Hz was reached under full CPU load. During a longterm experiment over 5 km travel distance, the method demonstrated its practicability. 3050 0-7803-7272-7/02/$17.00

RIS: A Framework for Motion Planning Among Highly Dynamic Obstacles

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)

We present here a framework to integrate into a motion planning method the interaction zones of a moving robot with its future surroundings, the reachable interaction sets. It can handle highly dynamic scenarios when combined with path planning methods optimized for quasi-static environments. As a demonstrator, it is integrated here with an artificial potential field reactive method and with a Bézier curve path planning. Experimental evaluations show that this approach significantly improves dynamic path planning methods, especially when the speeds of the obstacles are higher than the one of the robot. The presented approach is used together with a global planning approach in order to handle complex static environments in presence of fast-moving obstacles. When the ego vehicle is not holonomic the presented approach is able to take dynamic constraints into account, which improve the prediction accuracy.

Robotic Motion Planning in Dynamic Environments and its Applications

IJRCS, 2022

The fundamental problem of robot motion planning in dynamic environment (RMPDE) is to find an optimal collision-free path from the start to the goal in a dynamic environment. Our literature survey of over 100 papers from the last four decades reveals that there are more than 30 models of RMPDE, and there is no benchmarking criterion to select one that is the best in a given situation. In this context, generating a regression-based model with 10 attributes is the first and foremost contribution of our research. Given a highly human-interactive environment like a cafeteria or a bus stand, the gross hidden Markov model has a special importance for modelling a robot path. A variant of the growing hidden Markov model for a serving robot in a cafeteria is the second contribution of this paper. We simulated the behavior of GHMM in a cafeteria with static and dynamic obstacles (static obstacles were both convex and concave) and with three different arrangements of the tables and obstacles. Robots have been employed in mushroom harvesting. A novel proposition discussed in this paper is probabilistic road map planning for a robot that finds an optimum path for reaching the ripened mushrooms in a randomly planted mushroom farm and a dexterous hand to pluck the selected mushrooms by employing inverse kinematics. Further, two biologically inspired meta-heuristic algorithms ant colony optimisation and firefly has been studied for their application to latex collection. The simulation results with this environment show that the firefly algorithm outperforms ant colony optimization in the general case. Finally, we have proposed a few pointers for future research in this domain. The compilation and comparison of various approaches of robot motion planning in highly dynamic environments, and the simulation of a few models for some typical scenarios, have been the contributions of this paper.

Local Path Planner for Mobile Robot in Dynamic Environment based on Distance Time Transform Method

Advanced Robotics, 2012

For unknown field explorations in disaster areas, mobile robots that can replace human workers in dangerous environments can greatly improve disaster response efforts by reducing additional risk to human life. However, realizing such robot systems requires various technologies. In particular, path planning is quite important because mobile robots in the real world are surrounded by dynamic obstacles such as people, which may hinder a robot's activities. In this research, we propose a collision avoidance method for a mobile robot in dynamic environments, considering the near-term motion and "personal space" of dynamic obstacles. Our method consists of the following three steps: "estimation," "conversion," and "planning." In the estimation step, dynamic and static obstacles are recognized and their future positions are estimated from their previous motions. Next, in the conversion step, a time axis is added to construct a 3-D time-space coordinate system. Finally, in the planning step, a distance-time transform is applied to plan a safe 3-D path from the robot's current position to the desired goal. The proposed method has been implemented on our mobile robot and mobile robot simulator and experiments were conducted to verify its usefulness.