Method For Motion Planning Research Articles

Foot terrain impact modeling and motion performance research of hexapod robot based on passive elastic leg

In order to improve the performance of the multi-legged robot, the impact of foot-terrain is an important target to measure the performance of the robot. In order to ensure the compliance contact of foot-terrain for the legged robot, it is essential to add passive spring between the calf and foot. The mechanical structure makes the leg of hexapod robot retractable passive elastic structure. For multi-legged robots with passive elastic legs, the foot-terrain impact model is presented to measure the dynamic performance and provide theoretical support for the low impact force motion planning. The traditional planning method will cause additional impact for the switching case between the swing and stance phase. This paper presents a motion planning method that considers the deformable leg structure and impact performance. The experiments have been carried out, and the results have demonstrated the effectiveness of the foot terrain impact model and motion planning method in comparison with a conventional method.

DeepGait: Planning and Control of Quadrupedal Gaits Using Deep Reinforcement Learning

This letter addresses the problem of legged locomotion in non-flat terrain. As legged robots such as quadrupeds are to be deployed in terrains with geometries which are difficult to model and predict, the need arises to equip them with the capability to generalize well to unforeseen situations. In this work, we propose a novel technique for training neural-network policies for terrain-aware locomotion, which combines state-of-the-art methods for model-based motion planning and reinforcement learning. Our approach is centered on formulating Markov decision processes using the evaluation of dynamic feasibility criteria in place of physical simulation. We thus employ policy-gradient methods to independently train policies which respectively plan and execute foothold and base motions in 3D environments using both proprioceptive and exteroceptive measurements. We apply our method within a challenging suite of simulated terrain scenarios which contain features such as narrow bridges, gaps and stepping-stones, and train policies which succeed in locomoting effectively in all cases.

Motion Planning for Bandaging Task With Abnormal Posture Detection and Avoidance

With the aim of enabling the aged to obtain timely nursing care and improve their quality of life, a novel method of motion planning for a six-degree-of-freedom (6-DoF) anthropomorphic robot arm to perform bandaging tasks is proposed. The motion planning of bandaging tasks is challenging because the collision avoidance problem and maintaining tension in the bandage should be considered simultaneously. To solve such problems, a new planning strategy is proposed, including: 1) collision detection criterion to detect abnormal posture beforehand, 2) a collision avoidance strategy by extending the bandage along the tangential direction (EBATD) to adjust the end effector's trajectory, enabling the robot to avoid collision and singularity posture, and 3) velocity planning of the end effector to keep the bandage tight during application. In addition, a new end effector that can be passively rotated for releasing the bandage was designed. The simulation experimental results in MATLAB demonstrated that the proposed strategies allowed the designed 6-DoF robot to avoid collision with an injured arm and complete the bandaging task. Compared with planning without EBATD, the proposed method effectively enlarges the bandaging space of the robot arms (approximately 1.5 times in a human forearm bandaging task). In addition, the proposed method was successfully tested using a prototype robot arm to perform bandaging on a human arm.

Robust Motion Planning in Dynamic Environments Based on Sampled-Data Hamilton–Jacobi Reachability

SUMMARYWe propose a novel formal approach to robust motion planning (MP) in dynamic environments based on reachability analysis. While traditional MP methods usually fail to provide formal robust safety and performance guarantees, our approach provably ensures safe task achievement in time-varying and adversarial environments under parametric uncertainty. We leverage recent results on Hamilton–Jacobi (HJ) reachability and differential games in order to compute offline guaranteed motion plans that are compatible with the sampled-data (SD) paradigm. Also, we synthesize online provably robust safety-preserving and target-reaching feedback controls. Unlike earlier applications of reachability analysis to MP, our methodology handles arbitrary time-varying constraints, adversarial agents such as pursuing obstacles or evading targets, and takes into account the robot’s configuration. Furthermore, we use HJ projections in order to reduce significantly the computational burden without trading off safety guarantees. The validity of this approach is demonstrated through the case study of a robot arm subject to measurement errors, which is tasked with safely reaching a goal in a known time-varying workspace while avoiding capture by an unpredictable pursuer. Finally, the performance of the approach and research perspectives are discussed.

Motion planning for an unmanned surface vehicle based on topological position maps

This paper investigates the motion-planning problem for an unmanned surface vehicle (USV), in which the goal is to find the shortest search time, the shortest path in navigational waters, all subject to collision avoidance and USV dynamics constraints. A new motion-planning method is proposed, based on topological position relationships (TPR), to achieve this solution. Firstly, the TPR of the obstacles and the USV are constructed, based on the spatial distribution of the obstacles. This gives an overall topological navigation map, which is different from the usual grid-based map. Secondly, a numerical model of unit decomposition is built to constrain the dynamics of the USV, so that the motion of the USV better fits the exact situation. Motion planning in this study is achieved by combining the topological navigation map and a numerical model of the USV. Finally, Numerical simulations and field tests verify the effectiveness of our formulated model and proposed algorithm.

Design of realistic chewing trajectory for dynamic analysis of the dental prosthesis

The chewing trajectory in the dynamic analysis of dental prosthesis is always defined as a two-segmental straight polyline without enough consideration about chewing force and motion laws. The study was aimed to design a realistic human chewing trajectory for the dynamic analysis based on force and motion planning methods. The all-ceramic crown restored in the mandibular first molar was selected as the representative prosthesis. Firstly, a dynamic model containing two molar components and one flat food component was built, and an approximate chewing plane was predefined. According to the desired forces (25 N, 150 N and 25 N), three force planning points were calculated by using tentative trajectories. The motion planning was then executed based on four-segment cubic spline model. Finally, the new trajectory was re-imported into the dynamic model as the displacement load for evaluating its stress influence. The maximum lateral velocity was 26.81 mm/s. Besides, the forces in the three force planning points were 14.11 N, 126.75 N and 13.56 N. The overall repetition rate of chewing force was 77.21%. The force and stress profiles were similar to the sine curve on the whole. The maximum dynamic stress of the crown prosthesis was 398.5 MPa. The motion law was effectively brought into the chewing trajectory to introduce the dynamic effect. The global force performance was acceptable, and the force profile was more realistic than the traditional chewing trajectory. The additional reliable characteristic feature of the stress distribution of the dental prosthesis was observed.

HPPRM: Hybrid Potential Based Probabilistic Roadmap Algorithm for Improved Dynamic Path Planning of Mobile Robots

Path planning and navigation is a very important problem in robotics, especially for mobile robots operating in complex environments. Sampling based planners such as the probabilistic roadmaps (PRM) have been widely used for different robot applications. However, due to the random sampling of nodes in PRM, it suffers from narrow passage problem that generates unconnected graph. The problem is addressed by increasing the number of nodes but at higher computation cost affecting real-time performance. To address this issue, in this paper, we propose an improved sampling-based path planning method for mobile robot navigation. The proposed method uses a layered hybrid Probabilistic Roadmap (PRM) and the Artificial Potential Field (APF) method for global planning. We used a decomposition method for node distribution that uses map segmentation to produce regions of high and low potential, and propose a method of reducing the dispersion of sample set during the roadmap construction. Our method produces better goal planning queries with a smaller graph and is computationally efficient than the traditional PRM. The proposed planner called the Hybrid Potential based Probabilistic Roadmap (HPPRM) is an improved sampling method with respect to success rate and calculation cost. Furthermore, we present a method for reactive local motion planning in the presence of static and dynamic obstacles in the environment. The advantage of the proposed method is that it can avoid local minima and successfully generate plans in complex maps such as narrow passages and bug trap scenarios that are otherwise difficult for the traditional sample-based methods. We show the validity of our method with experiments in simulation and real environments for both local and global planning. The results indicate that the proposed HPPRM is effective for autonomous mobile robot navigation in complex environments. The success rate of the proposed method is higher than 95% both for local and global planning.

RETRACTED: Research on sports planning and stability control of humanoid robot table tennis

The humanoid robot has the human shape and has great advantages in assisting human life and work. The ability to work, especially in a dynamic, unstructured environment, is an important prerequisite for humanoid robots to assist humans in their mission. Table tennis hitting involves a variety of key technologies such as visual inspection, trajectory planning, and artificial intelligence. It is an important research example that can reflect the ability of humanoid robots. First, according to the requirements of humanoid robots in the human living environment and the requirements of coordinating table tennis batting movements throughout the body, a method of establishing a humanoid robot model was analyzed, and a control system was designed to meet the needs of rapid table tennis batting. Second, a motion model construction and optimization algorithm based on intelligent learning training is proposed. Based on the parameter knowledge base established by the multiple trajectories of table tennis, a kind of electromagnetic mechanism and D-optimality regularized orthogonal minima are introduced. Design a two-pass method (regularized orthogonal least squares method + D-optimality) to learn the two-level learning method, which is used to learn the key parameters of the table tennis model. Third, for human-like robotic table tennis fast-moving, it is necessary to satisfy both the task and the stability requirements and to propose a stability-optimized whole-system coordinated trajectory planning method. The effectiveness of the proposed humanoid robot table tennis hitting motion planning and stability control method is verified by experiments.

Motion Planning of Redundant Manipulator With Variable Joint Velocity Limit Based on Beetle Antennae Search Algorithm

Redundant manipulators play important roles in many industrial and service applications by assisting people fulfill heavy and repetitive jobs. However, redundant manipulators are coupled highly-nonlinear systems which exert difficulty of redundancy resolution computation. Conventional methods such as pseudo-inverse-based approaches obtain the resolved joint angles from joint velocity level, which may bring about more computational cost and may neglect joint velocity limits. In this work, a motion planning method based on beetle antennae search algorithm (BAS) is proposed for motion planning of redundant manipulators with the variable joint velocity limit. Such proposed work does not need to resolve the velocity kinematics equation as the conventional methods do, and the proposed method can directly deal with the forward kinematics equation to resolve the desired joint angles. The simulation and experiment on the five-link planar manipulator and the Kuka industrial manipulator system demonstrate the efficiency of the proposed method for motion planning of redundant manipulator, and reveal the reliable performance of the BAS algorithm as compared with genetic algorithm (GA), particle swarm optimization (PSO), firefly algorithm(FA) and quantum behaved particle swarm algorithm(QPSO) methods.

Nonprehensile Manipulation: a Trajectory-Planning Perspective

This article discusses nonprehensile manipulation of an asymmetric object using a robotic manipulator from a motion planning point of view. Four different aspects of the problem will be analyzed:object stability, motion planning, manipulator control, and experimental validation. Specifically, via an analysis of marginal stability of an object resting on a moving tray, the work establishes the critical accelerations of the manipulator's end-effector, below which the object's stability is guaranteed. These critical accelerations guide the design of the end-effector's motion for successful nonprehensile manipulation of the object. In particular, we propose two methods to formulate polynomial asymmetric s-curve trajectories such that the end-effector completes its motion in minimum time. In one method, the trajectory is divided into segments whose time intervals are then computed via a recursive algorithm. In the other method, we formulate an optimization problem and design the minimum-time trajectory by balancing the tradeoff between the travel time and actuator effort. A series of experiments with a robotic arm is designed to validate and compare these motion planning methods in the context of nonprehensile manipulation. In addition, the experimental results demonstrate the advantages of the asymmetric s-curve motion profiles over the traditional symmetric s-curves.

Underactuated mechanical systems: Whether orbital stabilization is an adequate assignment for a controller design?

The paper contributes to developing algorithms for motion planning and motion control for mechanical systems with two and more passive degrees of freedom by exploring a challenging example in details. As shown, some of arguments of motion planning methods developed for systems of underactuation degree one can be generalized for novel demanding settings, while corresponding arguments and concepts for controller design should be substantially reconsidered and updated. Rigorous theoretical results are well supported by numerical studies.

Surrounding Vehicles Motion Prediction for Risk Assessment and Motion Planning of Autonomous Vehicle in Highway Scenarios

Safety is the cornerstone of autonomous driving vehicles. For autonomously controlled vehicles driving safely in complex and dynamic traffic scenarios, it is essential to precisely predict the evolution of the current traffic situation in the near future and make an accurate situational risk assessment. The precise motion prediction of surrounding vehicles is an essential prerequisite for risk assessment and motion planning of autonomous vehicles. In this paper, we propose a risk assessment and motion planning method for autonomously controlled vehicles based on motion prediction of surrounding vehicles. Firstly, surrounding vehicles’ trajectories are predicted based on fusing constant turn rate and acceleration-based motion prediction model and maneuver-based motion prediction model with interactive multiple models. Then, considering both the probability of collision event and collision severity, the collision risk assessment between autonomously controlled vehicle and surrounding vehicles is conducted with a collision risk index. After that, the motion planning of the autonomously controlled vehicle is formulated as a multi-objectives and multi-constraints optimization problem with a model predictive control framework. Finally, the proposed method is applied to several traffic scenarios to validate its feasibility and effectiveness.

Motion planning methods for autonomous vehicles in disordered traffic systems: a comparative analysis and future research directions

Most works that exist in the field of autonomous vehicle motion planning are in the context of orderly or lane-oriented traffic. However, the traffic conditions that exist in many countries like India, China, Bangladesh, etc. are disordered where the vehicles move anywhere laterally without complying with the lane discipline rule. This characteristic makes the task of planning by an autonomous vehicle manoeuvring in disordered traffic streams highly complex and computationally rigorous. The motion planning problem of autonomous vehicles in traffic systems where lane-discipline is not necessarily followed bears a resemblance with the mobile-robot motion planning. Thus, this paper reviews various techniques available for motion planning of robots in conjunction with the issue of motion planning of autonomous vehicles in disordered traffic conditions, highlights the advantages and limitations of different approaches alongside simulation-based comparative analysis, and finally puts forth some research directions.

Motion planning methods for autonomous vehicles in disordered traffic systems: a comparative analysis and future research directions

Motion planning methods for autonomous vehicles in disordered traffic systems: a comparative analysis and future research directions

Penetration effect of connected and automated vehicles on cooperative on‐ramp merging

Earlier work has established a centralised cooperative merging framework of optimally coordinating two strings of connected and automated vehicles (CAVs) passing through the on-ramp merging zone. The proposed merging strategy is capable of making a good trade-off between performance and computational cost. In this study, the authors address the problem of optimally coordinating CAVs under mixed traffic conditions, where both CAVs and human-driven vehicles (non-CAVs) travel on the roads, so as to enhance efficiency while guaranteeing safety constraints. A hierarchical cooperative merging framework is proposed for CAVs, which integrates merging sequence scheduling strategies (high level) and motion planning methods (low level). The impacts of CAV penetration (i.e. the fraction of CAVs relative to all vehicles) on throughput, delay, fuel consumption and emission are also investigated under different traffic demands. Simulation-based case studies indicate that the performance improvement becomes more significant as the CAV penetration rate increases and about 30% CAV penetration can effectively mitigate the shockwave and reduce traffic congestion.

Aerial Wilderness Search and Rescue with Ground Support

Unmanned aerial vehicles (UAVs) have been proposed for a wide range of applications. Their use in wilderness search and rescue (WiSAR), in particular, has been investigated for fast search-area coverage from a high vantage point. The probability of success in such searches, however, can be further improved utilizing cooperative systems that employ both UAVs and unmanned ground vehicles (UGVs). In this paper, we present a new coordinated-search planning method, for collaborative UAV-UGV teams. The proposed method, particularly developed for WiSAR, considers the search area to be continuously growing and that the search is sparse. It is also assumed that targets detected by UAVs must be identified by a ground-level searcher. The UAV/UGV motion-planning method presented herein, therefore, has two major components: (i) coordinated search and (ii) joint target identification. The novelty of the proposed method lies in its use of (i) time-dependent target-location iso-probability curves, and (ii) an effective and efficient coordinated target-identification algorithm. The method has been validated via numerous simulated WiSAR searches for varying scenarios. Furthermore, extensive comparative experiments with other methods have shown that our method has higher rates of target detection and shorter search times, significantly outperforming alternative techniques by 75% – 255% in terms of target detection probability.

Research on Adaptive Reaction Null Space Planning and Control Strategy Based on VFF–RLS and SSADE–ELM Algorithm for Free-Floating Space Robot

With the increase of on-orbit maintenance and support requirements, the application of a space manipulator is becoming more promising. In actual operation, the strong coupling of the free-floating space robot itself and the unknown disturbance of the contact target caused a major challenge to the robot base posture control. Traditional Reaction Null Space (RNS) motion planning and control methods require the construction of precise dynamic models, which is impossible in reality. In order to solve this problem, this paper proposes a new Adaptive Reaction Null Space (ARNS) path planning and control strategy for the contact of free-floating space robots with unknown targets. The ARNS path planning strategy is constructed by the Variable Forgetting Factor Recursive Least Squares (VFF–RLS) algorithm. At the same time, a robust adaptive control strategy based on the Strategy Self-Adaption Differential Evolution–Extreme Learning Machine (SSADE–ELM) algorithm is proposed to track the dynamic changes of the planned path. The algorithm enables us to intelligently learn and compensate for the unknown disturbance. Then, this paper constructs a robust controller to compensate model uncertainty. A striking feature of the proposed strategy is that it does not require an accurate system model or any information about unknown attributes. This design can dynamically implement RNS path tracking performance. Finally, through simulation and experiment, the proposed algorithm is compared with the existing methods to prove its effectiveness and superiority.

Risk-Aware Motion Planning and Control Using CVaR-Constrained Optimization

We propose a risk-aware motion planning and decision-making method that systematically adjusts the safety and conservativeness in an environment with randomly moving obstacles. The key component of this method is the conditional value-at-risk (CVaR) used to measure the safety risk that a robot faces. Unlike chance constraints, CVaR constraints are coherent, convex, and distinguish between tail events. We propose a two-stage method for safe motion planning and control: A reference trajectory is generated by using RRT* in the first stage, and then a receding horizon controller is employed to limit the safety risk by using CVaR constraints in the second stage. However, the second stage problem is nontrivial to solve, as it is a triple-level stochastic program. We develop a computationally tractable approach through 1) a reformulation of the CVaR constraints; 2) a sample average approximation; and 3) a linearly constrained mixed integer convex program formulation. The performance and utility of this risk-aware method are demonstrated via simulation using a 12-dimensional model of quadrotors.

Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments

This letter presents a method for local motion planning in unstructured environments with static and moving obstacles, such as humans. Given a reference path and speed, our optimization-based receding-horizon approach computes a local trajectory that minimizes the tracking error while avoiding obstacles. We build on nonlinear model-predictive contouring control (MPCC) and extend it to incorporate a static map by computing, online, a set of convex regions in free space. We model moving obstacles as ellipsoids and provide a correct bound to approximate the collision region, given by the Minkowsky sum of an ellipse and a circle. Our framework is agnostic to the robot model. We present experimental results with a mobile robot navigating in indoor environments populated with humans. Our method is executed fully onboard without the need of external support and can be applied to other robot morphologies such as autonomous cars.

Path Tracking of a Cable-Driven Snake Robot With a Two-Level Motion Planning Method

Due to high degree of freedoms (DOFs), motion planning of the cable-driven snake robot is challenging and usually tackled by the iterative Jacobian-based method. This iterative method adopts discontinuous velocity and tolerance to determine intermediate poses, which deteriorate the tracking performance of robots. In this paper, a two-level motion planning method is proposed to schedule the motion of cable-driven snake robot owning a serial–parallel hybrid structure. First, kinematics model of the robot among actuators, joints, and end effectors are introduced. Second, in the upper level, a jerk-bounded algorithm is employed to plan the position, velocity, and acceleration profiles of the end effector for a given path, whereas in the lower level, the joint angles and cable length changes are calculated by the inverse kinematics of the robot according to the position and velocity profiles of the end effector from the upper level. Therefore, the iterative process is eliminated by the presented method, which enhances the tracking performance and motion smoothness of the robot. Finally, simulations and experiments on a 24 DOFs snake robot prototype demonstrate the effectiveness of the presented method. According to the experimental results, the two-level method improves the repeatability and accuracy of the prototype by about 60% and 6%, respectively, compared with the iterative method.