Point-to-Point Motion Planning of a Free-Floating Space Manipulator Using the Rapidly-Exploring Random Trees (RRT) Method

Application of bidirectional rapidly exploring random trees (BiRRT) algorithm for collision-free trajectory planning of free-floating space manipulator

On-orbit servicing and active debris removal missions will rely on the use of unmanned satellite equipped with a manipulator. Capture of the target object will be the most challenging phase of these missions. During the capture manoeuvre, the manipulator must avoid collisions with elements of the target object (e.g., solar panels). The dynamic equations of the satellite-manipulator system must be used during the trajectory planning because the motion of the manipulator influences the position and orientation of the satellite. In this paper, we propose application of the bidirectional rapidly exploring random trees (BiRRT) algorithm for planning a collision-free trajectory of a manipulator mounted on a free-floating satellite. A new approach based on pseudo-velocities method (PVM) is used for construction of nodes of the trajectory tree. Initial nodes of the second tree are selected from the set of potential final configurations of the system. The proposed method is validated in numerical simulations performed for a planar case (3-DoF manipulator). The obtained results are compared with the results obtained with two other trajectory planning methods based on the RRT algorithm. It is shown that in a simple test scenario, the proposed BiRRT PVM algorithm results in a lower manipulator tip position error. In a more difficult test scenario, only the proposed method was able to find a solution. Practical applicability of the BiRRT PVM method is demonstrated in experiments performed on a planar air-bearing microgravity simulator where the trajectory is realised by a manipulator mounted on a mock-up of the free-floating servicing satellite.

A Review of Spatial Robotic Arm Trajectory Planning

With space technology development, the spatial robotic arm plays an increasingly important role in space activities. Spatial robotic arms can effectively replace humans to complete in-orbit service tasks. The trajectory planning is the basis of robotic arm motion. Its merit has an essential impact on the quality of the completed operation. The research on spatial robotic arm trajectory planning has not yet formed a broad framework categorization, so it is necessary to analyze and deeply summarize the existing research systematically. This paper introduces the current situation of space obstacle avoidance trajectory planning and motion trajectory planning. It discusses the basic principle and practical application of the spatial robotic arm trajectory planning method. The future development trend has also been prospected.

A Computer-Assisted Preoperative Path Planning Method for the Parallel Orthopedic Robot

Background: Trajectory planning is the premise of the control of orthopedic robots, which is directly related to the safety of the human body. However, to date, the trajectory of orthopedic robots has been restricted to lines and spline curves. This limits the flexibility of the robot and leads to unsatisfactory performance. In this paper, a trajectory planning method based on improved RRT* and B-spline curve is proposed in order to improve the control accuracy and flexibility. Method: Firstly, combined with the shortcomings of current trajectory planning methods and bone docking task analysis, the characteristics of the trajectory for orthopedic robot are illustrated, and the problem is described. Secondly, a sampling strategy and an extension strategy are proposed to solve the optimal problem of the RRT* algorithm. Meanwhile, B-spline curve is selected for path smoothing. Thirdly, based on our orthopedic robot, kinematics analysis is introduced briefly, and hypotonic polynomial is used to fit the joint variables. Finally, a comparative study of the improved RRT*, RRT*, and other algorithms are completed, and the feasibility of the robot’s trajectory is verified by algorithm simulation and platform simulation. Results: Compared with RRT*, shorter path and high node utilization are shown in the improved RRT*, which cut down about 1mm in the average path length and increased about half in the average node utilization. In the meantime, the fitting results are accepted, and the results of algorithm simulation and platform simulation showed good consistency and feasibility. Conclusions: This study revealed that the improved RRT* was superior to RRT*, and the proposed method could be used for the trajectory planning of parallel orthopedic robots, which has some significance for bone fracture and deformity correction.

Time delay estimation-based reactionless augmented adaptive sliding mode control of a space manipulator’s pregrasping a target

Reaction null space (RNS) planning and control of a planar three-link space manipulator’s pregrasping a spinning target are studied. First, the Lagrange dynamic model of the manipulator was established. Second, the RNS motion planning algorithm was derived, and the vector norm constraint algorithm of RNS planning was addressed to ensure certain joint angular acceleration constraints were satisfied. Furthermore, an augmented adaptive sliding mode controller based on time delay estimation (TDE) was proposed. This controller estimated the unknowns of the system by TDE technology, in which accurate and complete dynamics were not required, and an adaptive TDE was introduced to decrease the estimation errors and avoid serious chattering. Finally, numerical simulations were carried out to verify the effectiveness of the proposed RNS planning and control algorithm.

ADD-RRV for motion planning in complex environments

In this paper, we present a novel sampling-based motion planning method in various complex environments, especially with narrow passages. We use online the results of the planner in the ADD-RRT framework to identify the types of the local configuration space based on the principal component analysis (PCA). The identification result is then used to accelerate the expansion similar to RRV around obstacles and through narrow passages. We also propose a modified bridge test to identify the entrance of a narrow passage and boost samples inside it. We have compared our method with known motion planners in several scenarios through simulations. Our method shows the best performance across all the tested planners in the tested scenarios.