Navigational path planning for a vision-based mobile robot

Robotica ◽  
1989 ◽  
Vol 7 (1) ◽  
pp. 49-63 ◽  
Author(s):  
Ronald C. Arkin
Keyword(s):  
Path Planning ◽  
Free Space ◽  
Search Algorithm ◽  
Optimal Path ◽  
Space Representation ◽  
Transition Zones ◽  
Recursive Decomposition ◽  
Robot Architecture ◽  
Multi Level ◽  
Sensor Navigation

SUMMARYThe Autonomous Robot Architecture (AuRA) provides multi-level representation and planning capabilities. This paper addresses the task of navigational path-planning, which provides the robot with a path guaranteed to be free of collisions with any modeled obstacles. Knowledge supporting visual perception can also be embedded, facilitating the actual path traversal by the vehicle.A multi-level representation and architecture to support multi-sensor navigation (predominantly visual) are described. A hybrid vertex-graph free-space representation based upon the decomposition of free space into convex regions capable for use in both indoor and limited outdoor navigation is discussed. This “meadow map” is produced via the recursive decomposition of the initial bounding area of traversability and its associated modeled obstacles. Of particular interest is the ability to handle diverse terrain types (sidewalks, grass, gravel, etc.) “Transition zones” ease the passage of the robot from one terrain type to another.The navigational planner that utilizes the data available in the above representational scheme is described. An A* search algorithm incorporates appropriate cost functions for multi-terrain navigation. Consideration is given to just what constitutes an “optimal” path in this context.

scholarly journals Time-efficient path planning using two virtual robots

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141988674
Author(s):  
Jonghoek Kim
Keyword(s):  
Path Planning ◽  
Finite Time ◽  
Free Space ◽  
Path Length ◽  
Optimal Path ◽  
Three Dimensional ◽  
Computational Time ◽  
Topological Map ◽  
Planning Algorithms ◽  
Safe Path

This article introduces time-efficient path planning algorithms handling both path length and safety within a reasonable computational time. The path is planned considering the robot’s size so that as the robot traverses the constructed path, it doesn’t collide with an obstacle boundary. This article introduces two virtual robots deploying virtual nodes which discretize the obstacle-free space into a topological map. Using the topological map, the planner generates a safe and near-optimal path within a reasonable computational time. It is proved that our planner finds a safe path to the goal in finite time. Using MATLAB simulations, we verify the effectiveness of our path planning algorithms by comparing it with the rapidly-exploring random tree (RRT)-star algorithm in three-dimensional environments.

Priority-based speed control strategy for automated guided vehicle path planning in automated container terminals

2020 ◽  
Vol 42 (16) ◽  
pp. 3079-3090 ◽  
Author(s):  
Meisu Zhong ◽  
Yongsheng Yang ◽  
Shu Sun ◽  
Yamin Zhou ◽  
Octavian Postolache ◽  
...  
Keyword(s):  
Path Planning ◽  
Control Strategy ◽  
Search Algorithm ◽  
Waiting Times ◽  
Optimal Path ◽  
Speed Control ◽  
Container Terminals ◽  
Automated Guided Vehicles ◽  
Operating Efficiency ◽  
Continuous Increase

With the continuous increase in labour costs and the demands of the supply chain, improving the efficiency of automated container terminals has been a key factor in the development of ports. Automated guided vehicles (AGVs) are the main means of horizontal transport in such terminals, and problems in relation to their use such as vehicle conflict, congestion and waiting times have become very serious, greatly reducing the operating efficiency of the terminals. In this article, we model the minimum driving distance of AGVs that transport containers between quay cranes (QCs) and yard cranes (YCs). AGVs are able to choose the optimal path from pre-planned paths by testing the overlap rate and the conflict time. To achieve conflict-free AGV path planning, a priority-based speed control strategy is used in conjunction with the Dijkstra depth-first search algorithm to solve the model. The simulation experiments show that this model can effectively reduce the probability of AGVs coming into conflict, reduce the time QCs and YCs have to wait for their next task and improve the operational efficiency of AGV horizontal transportation in automated container terminals.

Optimal Path Planning of Redundant Cooperative Robots Under Equality and Inequality Constraints

2003 ◽  
Author(s):  
Ali Hosseini ◽  
Mehdi Keshmiri
Keyword(s):  
Path Planning ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Minimum Principle ◽  
Optimal Path ◽  
Inequality Constraints ◽  
Optimal Path Planning ◽  
Nonlinear Inequality ◽  
Nonlinear Inequality Constraints ◽  
Cooperative Manipulators

Using kinematic resolution, the optimal path planning for two redundant cooperative manipulators carrying a solid object on a desired trajectory is studied. The optimization problem is first solved with no constraint. Consequently, the nonlinear inequality constraints, which model obstacles, are added to the problem. The formulation has been derived using Pontryagin Minimum Principle and results in a Two Point Boundary Value Problem (TPBVP). The problem is solved for a cooperative manipulator system consisting of two 3-DOF serial robots jointly carrying an object and the results are compared with those obtained from a search algorithm. Defining the obstacles in workspace as functions of joint space coordinates, the inequality constrained optimization problem is solved for the cooperative manipulators.

scholarly journals Robot Motion Planning in an Unknown Environment with Danger Space

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 201 ◽  
Author(s):  
Hadi Jahanshahi ◽  
Mohsen Jafarzadeh ◽  
Naeimeh Najafizadeh Sari ◽  
Viet-Thanh Pham ◽  
Van Van Huynh ◽  
...  
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Markov Decision Processes ◽  
Free Space ◽  
Optimal Path ◽  
Humanoid Robots ◽  
Decision Processes ◽  
Inference System ◽  
Unknown Environment ◽  
Markov Decision

This paper discusses the real-time optimal path planning of autonomous humanoid robots in unknown environments regarding the absence and presence of the danger space. The danger is defined as an environment which is not an obstacle nor free space and robot are permitted to cross when no free space options are available. In other words, the danger can be defined as the potentially risky areas of the map. For example, mud pits in a wooded area and greasy floor in a factory can be considered as a danger. The synthetic potential field, linguistic method, and Markov decision processes are methods which have been reviewed for path planning in a free-danger unknown environment. The modified Markov decision processes based on the Takagi–Sugeno fuzzy inference system is implemented to reach the target in the presence and absence of the danger space. In the proposed method, the reward function has been calculated without the exact estimation of the distance and shape of the obstacles. Unlike other existing path planning algorithms, the proposed methods can work with noisy data. Additionally, the entire motion planning procedure is fully autonomous. This feature makes the robot able to work in a real situation. The discussed methods ensure the collision avoidance and convergence to the target in an optimal and safe path. An Aldebaran humanoid robot, NAO H25, has been selected to verify the presented methods. The proposed methods require only vision data which can be obtained by only one camera. The experimental results demonstrate the efficiency of the proposed methods.

scholarly journals A new path planning method based on concave polygon convex decomposition and artificial bee colony algorithm

2020 ◽  
Vol 17 (1) ◽  
pp. 172988141989478 ◽  
Author(s):  
Zhaoying Li ◽  
Zhao Zhang ◽  
Hao Liu ◽  
Liang Yang
Keyword(s):  
Path Planning ◽  
Computational Efficiency ◽  
Free Space ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Path ◽  
Convex Polygons ◽  
Convex Decomposition ◽  
Bee Colony ◽  
High Computational Efficiency

Free space algorithms are kind of graphics-based methods for path planning. With previously known map information, graphics-based methods have high computational efficiency in providing a feasible path. However, the existing free space algorithms do not guarantee the global optimality because they always search in one connected domain but not all the possible connected domains. To overcome this drawback, this article presents an improved free space algorithm based on map decomposition with multiple connected domains and artificial bee colony algorithm. First, a decomposition algorithm of single-connected concave polygon is introduced based on the principle of concave polygon convex decomposition. Any map without obstacle is taken as single-connected concave polygon (the convex polygon map can be seen as already decomposed and will not be discussed here). Single concave polygon can be decomposed into convex polygons by connecting concave points with their visible vertex. Second, decomposition algorithm for multi-connected concave polygon (any map with obstacles) is designed. It can be converted into single-connected concave polygon by excluding obstacles using virtual links. The map can be decomposed into several convex polygons which form multiple connected domains. Third, artificial bee colony algorithm is used to search the optimal path in all the connected domains so as to avoid falling into the local minimum. Numerical simulations and comparisons with existing free space algorithm and rapidly exploring random tree star algorithm are carried out to evaluate the performance of the proposed method. The results show that this method is able to find the optimal path with high computational efficiency and accuracy. It has advantages especially for complex maps. Furthermore, parameter sensitivity analysis is provided and the suggested values for parameters are given.

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