Feedback motion planning of unmanned surface vehicles via random sequential composition

2019 ◽  
Vol 41 (12) ◽  
pp. 3321-3330 ◽  
Author(s):  
Emre Ege ◽  
Mustafa Mert Ankarali
Keyword(s):  
Motion Planning ◽  
Broad Class ◽  
Random Trees ◽  
Main Approach ◽  
Unmanned Surface Vehicles ◽  
Sequential Composition ◽  
Planning Algorithm ◽  
Overall Performance ◽  
Simulation Results ◽  
Feasible Solutions

In this paper, we propose a new motion planning method that aims to robustly and computationally efficiently solve path planning and navigation problems for unmanned surface vehicles (USVs). Our approach is based on synthesizing two different existing methodologies: sequential composition of dynamic behaviours and rapidly exploring random trees (RRT). The main motivation of this integrated solution is to develop a robust feedback-based and yet computationally feasible motion planning algorithm for USVs. In order to illustrate the main approach and show the feasibility of the method, we performed simulations and tested the overall performance and applicability for future experimental applications. We also tested the robustness of the method under relatively extreme environmental uncertainty. Simulation results indicate that our method can produce robust and computationally feasible solutions for a broad class of USVs.

scholarly journals Sampling-Based Motion Planning for Free-Floating Space Robot without Inverse Kinematics

2020 ◽  
Vol 10 (24) ◽  
pp. 9137
Author(s):  
Hongwen Zhang ◽  
Zhanxia Zhu
Keyword(s):  
Motion Planning ◽  
Inverse Kinematics ◽  
Random Trees ◽  
Space Robot ◽  
Initial Configuration ◽  
First Order ◽  
System A ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Differential Relationship

Motion planning is one of the most important technologies for free-floating space robots (FFSRs) to increase operation safety and autonomy in orbit. As a nonholonomic system, a first-order differential relationship exists between the joint angle and the base attitude of the space robot, which makes it pretty challenging to implement the relevant motion planning. Meanwhile, the existing planning framework must solve inverse kinematics for goal configuration and has the limitation that the goal configuration and the initial configuration may not be in the same connected domain. Thus, faced with these questions, this paper investigates a novel motion planning algorithm based on rapidly-exploring random trees (RRTs) for an FFSR from an initial configuration to a goal end-effector (EE) pose. In a motion planning algorithm designed to deal with differential constraints and restrict base attitude disturbance, two control-based local planners are proposed, respectively, for random configuration guiding growth and goal EE pose-guiding growth of the tree. The former can ensure the effective exploration of the configuration space, and the latter can reduce the possibility of occurrence of singularity while ensuring the fast convergence of the algorithm and no violation of the attitude constraints. Compared with the existing works, it does not require the inverse kinematics to be solved while the planning task is completed and the attitude constraint is preserved. The simulation results verify the effectiveness of the algorithm.

On Receding Horizon Chance Constraint Motion Planning for Uncertain Multi-Agent Systems

2019 ◽  
Author(s):  
Yash Bagla ◽  
Vaibhav Srivastava
Keyword(s):  
Motion Planning ◽  
Autonomous Systems ◽  
Random Trees ◽  
Chance Constraint ◽  
Multi Agent Systems ◽  
Receding Horizon ◽  
Uncertain Environments ◽  
Uncertainty Model ◽  
Planning Algorithm ◽  
Multi Agent

Abstract We propose and study a motion planning algorithm for multi-agent autonomous systems to navigate through uncertain and dynamic environments. We use a receding horizon chance constraint framework that allows for tuning the trade-off between the risk of collision and the infeasibility of paths. We consider sampling-based incremental planning algorithms and extend them to the case of multiple agents and dynamic and uncertain environments. The receding horizon control framework is used to incorporate sensor measurements at a fixed interval of time to reduce uncertainty about agents’ state and environment. Our presentation focuses on rapidly-exploring random trees (RRTs) and the assumption of Gaussian noise in the uncertainty model. Our algorithm is illustrated using several examples.

scholarly journals Hybrid Partition-Based Patrolling Scheme for Maritime Area Patrol with Multiple Cooperative Unmanned Surface Vehicles

2020 ◽  
Vol 8 (11) ◽  
pp. 936
Author(s):  
Jiajia Xie ◽  
Rui Zhou ◽  
Jun Luo ◽  
Yan Peng ◽  
Yuan Liu ◽  
...  
Keyword(s):  
Forest Fires ◽  
Low Cost ◽  
Search And Rescue ◽  
Unmanned Surface Vehicles ◽  
Planning Algorithm ◽  
Simulation Results ◽  
Path Planning Algorithm ◽  
The Given ◽  
Maritime Environment ◽  
Multi Robot

Multi-robot cooperative patrolling systems have been extensively employed in the civilian and military fields, including monitoring forest fires, marine search-and-rescue, and area patrol. Multi-robot area patrol problems refer to the activity that a team of robots works cooperatively and regularly to visit the key targets in the given area for security. Following consideration of the low cost and high safety of unmanned surface vehicles (USV), a team of USVs is organized to perform area patrol in a sophisticated maritime environment. In this paper, we establish a mathematical model considering the characteristics of the cooperative patrol task and the limited conditions of USVs. A hybrid partition-based patrolling scheme is proposed for a multi-USV system to visit targets with different importance levels in a maritime area. Firstly, a centralized area partition algorithm is utilized to partition the patrolling area according to the number of USVs. Secondly, a distributed path planning algorithm is applied to planning the patrolling path for each USV to visit the targets in a maritime environment to minimize the length of the patrolling path for the USV team. Finally, comparative experiments between the proposed scheme and other methods are carried out to validate the performance of the hybrid partition-based patrolling scheme. Simulation results and experimental analysis show the efficiency of the proposed hybrid partition-based patrolling scheme compared to several previous patrolling algorithms.

scholarly journals Exploring implicit spaces for constrained sampling-based planning

2019 ◽  
Vol 38 (10-11) ◽  
pp. 1151-1178 ◽  
Author(s):  
Zachary Kingston ◽  
Mark Moll ◽  
Lydia E Kavraki
Keyword(s):  
Motion Planning ◽  
Open Source ◽  
Configuration Space ◽  
Broad Class ◽  
Asymptotic Optimality ◽  
Configuration Spaces ◽  
Local Planning ◽  
Planning Algorithm

We present a review and reformulation of manifold constrained sampling-based motion planning within a unifying framework, IMACS (implicit manifold configuration space). IMACS enables a broad class of motion planners to plan in the presence of manifold constraints, decoupling the choice of motion planning algorithm and method for constraint adherence into orthogonal choices. We show that implicit configuration spaces defined by constraints can be presented to sampling-based planners by addressing two key fundamental primitives, sampling and local planning, and that IMACS preserves theoretical properties of probabilistic completeness and asymptotic optimality through these primitives. Within IMACS, we implement projection- and continuation-based methods for constraint adherence, and demonstrate the framework on a range of planners with both methods in simulated and realistic scenarios. Our results show that the choice of method for constraint adherence depends on many factors and that novel combinations of planners and methods of constraint adherence can be more effective than previous approaches. Our implementation of IMACS is open source within the Open Motion Planning Library and is easily extended for novel planners and constraint spaces.

Fault Tolerant Motion Planning With Avoidance of the Sudden Change of Joint Velocity for Two Coordinating Redundant Manipulators

2005 ◽  
Author(s):  
Jing Zhao ◽  
Kailiang Zhang ◽  
Xuebin Yao
Keyword(s):  
Motion Planning ◽  
Fault Tolerant ◽  
Dynamic Properties ◽  
Sudden Change ◽  
Redundant Manipulators ◽  
Motion Stability ◽  
Planning Algorithm ◽  
Simulation Results ◽  
The Difference ◽  
Joint Velocity

The sudden change of joint velocity in fault tolerant operation of two coordinating manipulators for joint-locking failures is studied. First, the difference between the joint velocity of original manipulator and that of reduced manipulator is defined as the sudden change in joint velocity. Then, a corresponding fault tolerant planning algorithm based on this criterion is proposed. At last, a simulation example of fault tolerant operations is implemented with two planar 3R manipulators. Simulation results show that this algorithm can effectively avoid the sudden change of joint velocity occurring in fault tolerant operation of two coordinating manipulators, strengthen their motion stability and so improve their kinematical and dynamic properties in fault tolerant operations.

scholarly journals A new method on motion planning for mobile robots using jump point search and Bezier curves

2021 ◽  
Vol 18 (4) ◽  
pp. 172988142110192
Author(s):  
Ben Zhang ◽  
Denglin Zhu
Keyword(s):  
Motion Planning ◽  
Initial Point ◽  
Target Point ◽  
Jump Point ◽  
Bezier Curves ◽  
Bézier Curves ◽  
Front End ◽  
Planning Algorithm ◽  
Point Search ◽  
Path Planning Algorithm

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on motion planning systems that meet the shortest path and obstacle avoidance requirements. This article proposes a novel path planning algorithm based on jump point search and Bezier curves. The proposed algorithm consists of two main steps. In the front end, the improved heuristic function based on distance and direction is used to reduce the cost, and the redundant turning points are trimmed. In the back end, a novel trajectory generation method based on Bezier curves and a straight line is proposed. Our experimental results indicate that the proposed algorithm provides a complete motion planning solution from the front end to the back end, which can realize an optimal trajectory from the initial point to the target point used for robot navigation.

New repetitive motion planning scheme with cube end-effector planning precision for redundant robotic manipulators

Robotica ◽  
2021 ◽  
pp. 1-22
Author(s):  
Limin Shen ◽  
Yuanmei Wen
Keyword(s):  
Theoretical Analysis ◽  
Motion Planning ◽  
Robotic Manipulators ◽  
Robotic Manipulator ◽  
Repetitive Motion ◽  
End Effector ◽  
Difference Formula ◽  
Planning Scheme ◽  
Simulation Results ◽  
The Difference

Abstract Repetitive motion planning (RMP) is important in operating redundant robotic manipulators. In this paper, a new RMP scheme that is based on the pseudoinverse formulation is proposed for redundant robotic manipulators. Such a scheme is derived from the discretization of an existing RMP scheme by utilizing the difference formula. Then, theoretical analysis and results are presented to show the characteristic of the proposed RMP scheme. That is, this scheme possesses the characteristic of cube pattern in the end-effector planning precision. The proposed RMP scheme is further extended and studied for redundant robotic manipulators under joint constraint. Based on a four-link robotic manipulator, simulation results substantiate the effectiveness and superiority of the proposed RMP scheme and its extended one.

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