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This paper aims to introduce the human arm movement primitive (HAMP) to express and plan the motions of anthropomorphic arms. The task planning method is established for the minimum task cost and a novel human-like motion planning method based on the HAMPs is proposed to help humans better understand and plan the motions of anthropomorphic arms.

The HAMPs are extracted based on the structure and motion expression of the human arm. A method to slice the complex tasks into simple subtasks and sort subtasks is proposed. Then, a novel human-like motion planning method is built through the selection, sequencing and quantification of HAMPs. Finally, the HAMPs are mapped to the traditional joint angles of a robot by an analytical inverse kinematics method to control the anthropomorphic arms.

For the exploration of the motion laws of the human arm, the human arm motion capture experiments on 12 subjects are performed. The results show that the motion laws of human arm are reflected in the selection, sequencing and quantification of HAMPs. These motion laws can facilitate the human-like motion planning of anthropomorphic arms.

This study presents the HAMPs and a method for selecting, sequencing and quantifying them in human-like style, which leads to a new motion planning method for the anthropomorphic arms. A similar methodology is suitable for robots with anthropomorphic arms such as service robots, upper extremity exoskeleton robots and humanoid robots.

Learning from demonstration (LfD) provides an intuitive way for non-expert persons to teach robots new skills. However, the learned motion is typically fixed for a given scenario, which brings serious adaptiveness problem for robots operating in the unstructured environment, such as avoiding an obstacle which is not presented during original demonstrations. Therefore, the robot should be able to learn and execute new behaviors to accommodate the changing environment. To achieve this goal, this paper aims to propose an improved LfD method which is enhanced by an adaptive motion planning technique.

The LfD is based on GMM/GMR method, which can transform original off-line demonstrations into a compressed probabilistic model and recover robot motion based on the distributions. The central idea of this paper is to reshape the probabilistic model according to on-line observation, which is realized by the process of re-sampling, data partition, data reorganization and motion re-planning. The re-planned motions are not unique. A criterion is proposed to evaluate the fitness of each motion and optimize among the candidates.

The proposed method is implemented in a robotic rope disentangling task. The results show that the robot is able to complete its task while avoiding randomly distributed obstacles and thereby verify the effectiveness of the proposed method. The main contributions of the proposed method are avoiding unforeseen obstacles in the unstructured environment and maintaining crucial aspects of the motion which guarantee to accomplish a skill/task successfully.

Traditional methods are intrinsically based on motion planning technique and treat the off-line training data as a priori probability. The paper proposes a novel data-driven solution to achieve motion planning for LfD. When the environment changes, the off-line training data are revised according to external constraints and reorganized to generate new motion. Compared to traditional methods, the novel data-driven solution is concise and efficient.

The purpose of this paper is to propose a suitable motion planning for omni‐directional mobile robots (OMRs) by taking into account the motion characteristics.

Based on the kinematic and dynamic constraints, the maximum velocity, motion stability and energy consumption of the OMR moving in different directions are analysed, and the anisotropy of the OMR is presented. In order to obtain the optimal motion, the path that the robot can take in order to avoid the obstacle safely and reach the goal in a shorter path is deduced. According to the new concept of anisotropic function, the motion direction derived from traditional artificial potential field (tAPF) is regulated.

A combination of the anisotropic function and tAPF method produces high‐speed, highly stable and efficient motion when compared to the tAPF. Simulations and experiments have proven the validity and effectiveness of this method.

The practical factors, such as the effect of wear on the omni‐directional wheels, are not considered. Typical problems of APF, e.g. local minima, are not addressed here. In our future research, we will deal with these issues.

The proposed motion planning is applicable for any kind of OMRs, both three‐ and four‐wheeled OMRs, which can fully exhibit the advantages of OMRs.

The new concept of an anisotropic function is proposed to indicate the quality of motion in different directions. Different motion effects can be obtained in the same direction with different weights denoted by the anisotropic function, i.e. different trade‐offs can be achieved by varying the weights.

Humanoid robots should have the ability of walking in complex environment and overcoming large obstacles in rescue mission. Previous research mainly discusses the problem of humanoid robots stepping over or on/off one obstacle statically or dynamically. As an extreme case, this paper aims to demonstrate how the robots can step over two large obstacles continuously.

The robot model uses linear inverted pendulum (LIP) model. The motion planning procedure includes feasibility analysis with constraints, footprints planning, legs trajectory planning with collision-free constraint, foot trajectory adapter and upper body motion planning.

The motion planning with the motion constraints is a key problem, which can be considered as global optimization issue with collision-free constraint, kinematic limits and balance constraint. With the given obstacles, the robot first needs to determine whether it can achieve stepping over, if feasible, and then the robot gets the motion trajectory for the legs, waist and upper body using consecutive obstacles stepping over planning algorithm which is presented in this paper.

The consecutive stepping over problem is proposed in this paper. First, the paper defines two consecutive stepping over conditions, sparse stepping over (SSO) and tight stepping over (TSO). Then, a novel feasibility analysis method with condition (SSO/TSO) decision criterion is proposed for consecutive obstacles stepping over. The feasibility analysis method’s output is walking parameters with obstacles’ information. Furthermore, a modified legs trajectory planning method with center of mass trajectory compensation using upper body motion is proposed. Finally, simulations and experiments for SSO and TSO are carried out by using the XT-I humanoid robot platform with the aim to verify the validity and feasibility of the novel methods proposed in this paper.

This paper aims to study using a mobile manipulator with a collaborative robotic arm component to manipulate objects beyond the robot’s maximum payload.

This paper proposes a single-short probabilistic roadmap-based method to plan and optimize manipulation motion with environment support. The method uses an expanded object mesh model to examine contact and randomly explores object motion while keeping contact and securing affordable grasping force. It generates robotic motion trajectories after obtaining object motion using an optimization-based algorithm. With the proposed method’s help, the authors plan contact-rich manipulation without particularly analyzing an object’s contact modes and their transitions. The planner and optimizer determine them automatically.

The authors conducted experiments and analyses using simulations and real-world executions to examine the method’s performance. The method successfully found manipulation motion that met contact, force and kinematic constraints. It allowed a mobile manipulator to move heavy objects while leveraging supporting forces from environmental obstacles.

This paper presents an automatic approach for solving contact-rich heavy object manipulation problems. Unlike previous methods, the new approach does not need to explicitly analyze contact states and build contact transition graphs, thus providing a new view for robotic grasp-less manipulation, nonprehensile manipulation, manipulation with contact, etc.

This paper aims to present a hierarchical motion planner for planning the manipulation motion to repose long and heavy objects considering external support surfaces.

The planner includes a task-level layer and a motion-level layer. This paper formulates the manipulation planning problem at the task level by considering grasp poses as nodes and object poses for edges. This paper considers regrasping and constrained in-hand slip (drooping) during building graphs and find mixed regrasping and drooping sequences by searching the graph. The generated sequences autonomously divide the object weight between the arm and the support surface and avoid configuration obstacles. Cartesian planning is used at the robot motion level to generate motions between adjacent critical grasp poses of the sequence found by the task-level layer.

Various experiments are carried out to examine the performance of the proposed planner. The results show improved capability of robot arms to manipulate long and heavy objects using the proposed planner.

The authors’ contribution is that they initially develop a graph-based planning system that reasons both in-hand and regrasp manipulation motion considering external supports. On one hand, the planner integrates regrasping and drooping to realize in-hand manipulation with external support. On the other hand, it switches states by releasing and regrasping objects when the object is in stably placed. The search graphs' nodes could be retrieved from remote cloud servers that provide a large amount of pre-annotated data to implement cyber intelligence.

For robot motion planning there exists a large number of different algorithms, each appropriate for a certain domain, and the right choice of planner depends on the specific use case. The purpose of this paper is to consider the application of bin picking and benchmark a set of motion planning algorithms to identify which are most suited in the given context.

The paper presents a selection of motion planning algorithms and defines benchmarks based on three different bin-picking scenarios. The evaluation is done based on a fixed set of tasks, which are planned and executed on a real and a simulated robot.

The benchmarking shows a clear difference between the planners and generally indicates that algorithms integrating optimization, despite longer planning time, perform better due to a faster execution.

The originality of this work lies in the selected set of planners and the specific choice of application. Most new planners are only compared to existing methods for specific applications chosen to demonstrate the advantages. However, with the specifics of another application, such as bin picking, it is not obvious which planner to choose.

Aim for efficient motion planning of industrial robot with high degree of freedoms in both static and dynamic environments.

A multi‐agent based general path planner for serial manipulator is proposed in this work. A hierarchical structure developed based on fuzzy reasoning is employed in the planner. The high level in the hierarchical structure is designed to dynamically assign each link an appropriate behaviour and the low level is designed to determine the joint speed according to the behaviours assigned by the high level.

Combination of multi‐agent concept and fuzzy reasoning approach can obtain both flexibility and efficiency in motion planning of serial manipulator with high degree of freedoms.

Multiple local minima problem occurred in complex manipulation scenario has not yet been considered.

Applicable for real time motion planning of serial industrial robots with high degrees of freedom in 3D space.

In this research work, we make use of the of multi‐agent plus fuzzy logic concept to design a novel manipulator motion path planner, in particular, we introduce a novel mechanism into the fuzzy logic algorithm with the “back‐tracking” ability to avoid the local minima problem.The proposed motion planner has advantages on low computational cost, the suitability for real time path planning in 3D space and the capability to escape simple local minima.

Traditional robot arm trajectory planning methods have problems such as insufficient generalization performance and low adaptability. This paper aims to propose a method to plan the robot arm’s trajectory using the trajectory learning and generalization characteristics of dynamic motion primitives (DMPs).

This study aligns multiple demonstration motion primitives using dynamic time warping; use the Gaussian mixture model and Gaussian mixture regression methods to obtain the ideal primitive trajectory actions. By establishing a system model that improves DMPs, the parameters of the nonlinear function are learned based on the ideal primitive trajectory actions of the robotic arm, and the robotic arm motion trajectory is reproduced and generalized.

Experiments have proven that the robot arm motion trajectory learned by the method proposed in this article can not only learn to generalize and demonstrate the movement trend of the primitive trajectory, but also can better generate ideal motion trajectories and avoid obstacles when there are obstacles. The maximum Euclidean distance between the generated trajectory and the demonstration primitive trajectory is reduced by 29.9%, and the average Euclidean distance is reduced by 54.2%. This illustrates the feasibility of this method for robot arm trajectory planning.

It provides a new method for the trajectory planning of robotic arms in unstructured environments while improving the adaptability and generalization performance of robotic arms in trajectory planning.

The purpose of this paper is to maximize the speed of industrial robots by obtaining the minimum‐time trajectories that satisfy various constraints commonly given in the application of industrial robots.

The method utilizes the dynamic model of the robot manipulators to find the maximum kinematic constraints that are used with conventional trajectory patterns, such as trapezoidal velocity profiles and cubic polynomial functions.

The experimental results demonstrate that the proposed method can decrease the motion times substantially compared with the conventional kinematic method.

Although the method used a dynamic model, the computational burden is minimized by calculating dynamics only at certain points, enabling implementation of the method online. The proposed method is tested on more than 40 different types of robots made by Hyundai Heavy Industries Co. Ltd (HHI). The method is successfully implemented in Hi5, a new generation of HHI robot controller.

The paper shows that the method is computationally very simple compared with other minimum‐time trajectory‐planning methods, thus making it suitable for online implementation.