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.

scholarly journals Non-inverse kinematics of free-floating space robot based on motion planning of sampling

2021 ◽  
Vol 39 (5) ◽  
pp. 1005-1011
Author(s):  
Hongwen ZHANG ◽  
Zhanxia ZHU ◽  
Jianping YUAN
Keyword(s):  
Motion Planning ◽  
Inverse Kinematics ◽  
Algorithm Design ◽  
Space Robot ◽  
Initial Configuration ◽  
End Effector ◽  
First Order ◽  
Planning Framework ◽  
Present Algorithm ◽  
Differential Relationship

Motion planning is one of the fundamental technologies for robots to achieve autonomy. Free-floating space robots composed manipulators and base satellite that do not actively control its position and attitude has nonholonomic characteristics, and there is a first-order differential relationship between its joint angle and the base attitude. In addition, the planning framework which first converts the goal end-effector pose to its corresponding target configuration, and then plan the trajectory from the initial configuration to the goal configuration still has the following problems: the goal configuration and the initial configuration may not be in the same connected domain. Based on the RRT framework, the motion planning of a free-floating space robot from the initial configuration to the goal end-effector pose is studied. In the algorithm design, in order to deal with the differential constraints of the free-floating space robot, and the requirement that the attitude disturbance of its base cannot exceed its limit, a control-based local planner for random configuration guiding growth of the tree and a control-based local planner for goal end-effector pose guiding growth of the tree that can adjust the attitude of the base when necessary are proposed. The former can ensure the effective exploration of the configuration space, and the latter can avoid the occurrence of singularity while ensuring that the algorithm converges quickly and the base attitude disturbance meets the constraints. The present algorithm does not need to solve the inverse kinematics, can successfully complete the planning task, and ensure that the base attitude disturbance meets the requirements. The simulation verifies the effectiveness of the algorithm.

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.

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.

Velocity Obstacle-Based Collision Avoidance and Motion Planning Framework for Connected and Automated Vehicles

2022 ◽  
pp. 036119812110702
Author(s):  
Shunchao Wang ◽  
Zhibin Li ◽  
Bingtong Wang ◽  
Jingfeng Ma ◽  
Jingcai Yu
Keyword(s):  
Motion Planning ◽  
Collision Avoidance ◽  
Motion Tracking ◽  
Collision Risk ◽  
Automated Vehicles ◽  
Vehicle Collision ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Velocity Obstacle ◽  
Vehicle Dynamic Model

This study proposes a novel collision avoidance and motion planning framework for connected and automated vehicles based on an improved velocity obstacle (VO) method. The controller framework consists of two parts, that is, collision avoidance method and motion planning algorithm. The VO algorithm is introduced to deduce the velocity conditions of a vehicle collision. A collision risk potential field (CRPF) is constructed to modify the collision area calculated by the VO algorithm. A vehicle dynamic model is presented to predict vehicle moving states and trajectories. A model predictive control (MPC)-based motion tracking controller is employed to plan collision-avoidance path according to the collision-free principles deduced by the modified VO method. Five simulation scenarios are designed and conducted to demonstrate the control maneuver of the proposed controller framework. The results show that the constructed CRPF can accurately represent the collision risk distribution of the vehicles with different attributes and motion states. The proposed framework can effectively handle the maneuver of obstacle avoidance, lane change, and emergency response. The controller framework also presents good performance to avoid crashes under different levels of collision risk strength.

scholarly journals Online, Interactive User Guidance for High-dimensional, Constrained Motion Planning

2018 ◽  
Author(s):  
Fahad Islam ◽  
Oren Salzman ◽  
Maxim Likhachev
Keyword(s):  
Motion Planning ◽  
Domain Knowledge ◽  
High Dimensional ◽  
Constrained Motion ◽  
Significant Progress ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Speed Up ◽  
User Guidance ◽  
Intermediate Configuration

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration q^, which is used to bias the planner to go through q^. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafted domain-dependent heuristics.

On-Site Locomotion Planning for a Humanoid Robot with Stable Whole-Body Collision Avoidance Motion Guided by Footsteps and Centroidal Trajectory

2019 ◽  
Vol 17 (01) ◽  
pp. 1950035
Author(s):  
Iori Kumagai ◽  
Mitsuharu Morisawa ◽  
Shin’ichiro Nakaoka ◽  
Fumio Kanehiro
Keyword(s):  
Motion Planning ◽  
Collision Avoidance ◽  
Dynamic Simulation ◽  
Inverse Kinematics ◽  
Humanoid Robot ◽  
Computational Cost ◽  
Body Motion ◽  
Whole Body ◽  
Planning Framework ◽  
Narrow Space

In this paper, we propose a locomotion planning framework for a humanoid robot with stable whole-body collision avoidance motion, which enables the robot to traverse an unknown narrow space on the spot based on environmental measurements. The key idea of the proposed method is to reduce a large computational cost for the whole-body locomotion planning by utilizing global footstep planning results and its centroidal trajectory as a guide. In the global footstep planning phase, we modify the bounding box of the robot approximating the centroidal sway amplitude of the candidate footsteps. This enables the planner to obtain appropriate footsteps and transition time for next whole-body motion planning. Then, we execute sequential whole-body motion planning by prioritized inverse kinematics considering collision avoidance and maintaining its ZMP trajectory, which enables the robot to plan stable motion for each step in 223[Formula: see text]ms at worst. We evaluated the proposed framework by a humanoid robot HRP-5P in the dynamic simulation and the real world. The major contribution of our paper is solving the problem of increasing computational cost for whole-body motion planning and enabling a humanoid robot to execute adaptive on-site locomotion planning in an unknown narrow space.

scholarly journals Generation and Selection of Driver-behavior-based Transferable Motion Primitives

2020 ◽  
Author(s):  
Haijie Guan ◽  
Boyang Wang ◽  
Jiaming Wei ◽  
Yaomin Lu ◽  
Huiyan Chen ◽  
...  
Keyword(s):  
Motion Planning ◽  
Driver Behavior ◽  
Single Step ◽  
Driving Experience ◽  
Motion Primitives ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Behavior Based ◽  
Motion Characteristics ◽  
Selection Of

Abstract In order to achieve the integration of driver experience and heterogeneous vehicle platform characteristics in the motion planning algorithm, based on the driver-behavior-based transferable motion primitives, a general motion planning framework for oine generation and online selection of motion primitives (MPs) is proposed. The optimal control theory is applied to solve the boundary value problems in the process of generating MPs, where the driver behaviors and the vehicle motion characteristics are integrated into the optimization in the form of constraints. Moreover, this paper proposes a layered, unequal-weighted MPs selection framework and utilizes the combination of environmental constraints, nonholonomic vehicle constraints, trajectory smoothness, and collision risk as the single-step extension evaluation index. The library of MPs generated oine demonstrates that the proposed generation method realizes the eective expansion of the MP types and achieves the diverse generation of MPs with various velocity attributes and platform types. We also present how the MP selection algorithm utilizes the unique MP library to achieve the online extension of MP sequences. The results show that the proposed motion planning framework can not only improve the eciency and rationality of the algorithm based on driving experience but also can transfer between heterogeneous vehicle platforms and highlight the unique motion characteristics of the platform.

scholarly journals Generation and Selection of Driver-Behavior-Based Transferable Motion Primitives

2021 ◽  
Author(s):  
Haijie Guan ◽  
Boyang Wang ◽  
Jiaming Wei ◽  
Yaomin Lu ◽  
Huiyan Chen ◽  
...  
Keyword(s):  
Motion Planning ◽  
Driver Behavior ◽  
Single Step ◽  
Driving Experience ◽  
Motion Primitives ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Behavior Based ◽  
Motion Characteristics ◽  
Selection Of

Abstract In order to achieve the integration of driver experience and heterogeneous vehicle platform characteristics in the motion planning algorithm, based on the driver-behavior-based transferable motion primitives, a general motion planning framework for offline generation and online selection of motion primitives (MPs) is proposed. The optimal control theory is applied to solve the boundary value problems in the process of generating MPs, where the driver behaviors and the vehicle motion characteristics are integrated into the optimization in the form of constraints. Moreover, this paper proposes a layered, unequal-weighted MPs selection framework and utilizes the combination of environmental constraints, nonholonomic vehicle constraints, trajectory smoothness, and collision risk as the single-step extension evaluation index. The library of MPs generated offline demonstrates that the proposed generation method realizes the effective expansion of the MP types and achieves the diverse generation of MPs with various velocity attributes and platform types. We also present how the MP selection algorithm utilizes the unique MP library to achieve the online extension of MP sequences. The results show that the proposed motion planning framework can not only improve the efficiency and rationality of the algorithm based on driving experience but also can transfer between heterogeneous vehicle platforms and highlight the unique motion characteristics of the platform.

scholarly journals Motion Planning of a Second-Order Nonholonomic Chained Form System Based on Holonomy Extraction

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1337
Author(s):  
Masahide Ito
Keyword(s):  
Motion Planning ◽  
Nonholonomic Systems ◽  
Second Order ◽  
Literature Survey ◽  
First Order ◽  
Underactuated Manipulator ◽  
Planning Algorithm ◽  
The One ◽  
Specific Control ◽  
Form System

This paper proposes a motion planning algorithm for dynamic nonholonomic systems represented in a second-order chained form. The proposed approach focuses on the so-called holonomy resulting from a kind of motion that traverses a closed path in a reduced configuration space of the system. According to the author’s literature survey, control approaches that make explicit use of holonomy exist for kinematic nonholonomic systems but does not exist for dynamic nonholonomic systems. However, the second-order chained form system is controllable. Also, the structure of the second-order chained form system analogizes with the one of the first-order chained form for kinematic nonholonomic systems. These survey and perspectives brought a hypothesis that there exists a specific control strategy for extracting holonomy of the second-order chained form system to the author. To verify this hypothesis, this paper shows that the holonomy of the second-order chained form system can be extracted by combining two appropriate pairs of sinusoidal inputs. Then, based on such holonomy extraction, a motion planning algorithm is constructed. Furthermore, the effectiveness is demonstrated through some simulations including an application to an underactuated manipulator.

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