Path Planning Of Manipulator Research Articles

Autonomous navigation at unsignalized intersections: A coupled reinforcement learning and model predictive control approach

This paper develops an integrated safety-enhanced reinforcement learning (RL) and model predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized intersections. Researchers have extensively studied how AVs drive along highways. Nonetheless, how AVs navigate intersections in urban environments remains a challenging task due to the constant presence of moving road users, including turning vehicles, crossing or jaywalking pedestrians, and cyclists. AVs are thus required to learn and adapt to a dynamically evolving urban traffic environment. This paper proposes a design benchmark that allows AVs to sense the real-time traffic environment and perform path planning. The agent dynamically generates curves for feasible paths. The ego vehicle attempts to follow these paths under specific constraints. RL and MPC navigation algorithms run in parallel and are suitably selected to enhance ego vehicle safety. The ego AV is modeled with lateral and longitudinal dynamics and trained in a T-intersection using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm under various traffic scenarios. It is then tested on a straight road and a single or multi-lane intersections. All these experiments achieve desirable outcomes in terms of crash avoidance, driving efficiency, comfort, and tracking accuracy. The developed AV navigation system provides a design benchmark for an adaptive AV that can navigate unsignalized intersections.

Investigating the Impact of Triangle and Quadrangle Mesh Representations on AGV Path Planning for Various Indoor Environments: With or Without Inflation

In a factory with different kinds of spatial atmosphere (warehouses, corridors, small or large workshops with varying sizes of obstacles and distribution patterns), the robot’s generated paths for navigation tasks mainly depend on the representation of that environment. Hence, finding the best representation for each particular environment is necessary to forge a compromise between length, safety, and complexity of path planning. This paper aims to scrutinize the impact of environment model representation on the performance of an automated guided vehicle (AGV). To do so, a multi-objective cost function, considering the length of the path, its complexity, and minimum distance to obstacles, is defined for a perfect circular robot. Unlike other similar studies, three types of representation, namely quadrangle, irregular triangle, and varying-size irregular triangle, are then utilized to model the environment while applying an inflation layer to the discretized view. Finally, a navigation scenario is tested for different cell decomposition methods and an inflation layer size. The obtained results indicate that a nearly constant coarse size triangular mesh is a good candidate for a fixed-size robot in a non-changing environment. Moreover, the varying size of the triangular mesh and grid cell representations are better choices for factories with changing plans and multi-robot sizes due to the effect of the inflation layer. Based on the definition of a metric, which is a criterion for quantifying the performance of path planning on a representation type, constant or variable size triangle shapes are the only and best candidate for discretization in about 59% of industrial environments. In other cases, both cell types, the square and the triangle, can together be the best representation.

Metaheuristic Optimization-Based Path Planning and Tracking of Quadcopter for Payload Hold-Release Mission

Under harsh geographical conditions where manned flight is not possible, the ability of the unmanned aerial vehicle (UAV) to successfully carry out the payload hold–release mission by avoiding obstacles depends on the optimal path planning and tracking performance of the UAV. The ability of the UAV to plan and track the path with minimum energy and time consumption is possible by using the flight parameters. This study performs the optimum path planning and tracking using Harris hawk optimization (HHO)–grey wolf optimization (GWO), a hybrid metaheuristic optimization algorithm, to enable the UAV to actualize the payload hold–release mission avoiding obstacles. In the study, the hybrid HHO–GWO algorithm, which stands out with its avoidance of local minima and speed convergence, is used to successfully obtain the feasible and effective path. In addition, the effect of the mass change uncertainty of the UAV on optimal path planning and tracking performance is determined. The effectiveness of the proposed approach is tested by comparing it with the metaheuristic swarm optimization algorithms such as particle swarm optimization (PSO) and GWO. The experimental results obtained indicate that the proposed algorithm generates a fast and safe optimal path without becoming stuck with local minima, and the quadcopter tracks the generated path with minimum energy and time consumption.

Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation

We present a novel approach to path planning for robotic manipulators, in which paths are produced via iterative optimisation in the latent space of a generative model of robot poses. Constraints are incorporated through the use of constraint satisfaction classifiers operating on the same space. Optimisation leverages gradients through our learned models that provide a simple way to combine goal reaching objectives with constraint satisfaction, even in the presence of otherwise non-differentiable constraints. Our models are trained in a task-agnostic manner on randomly sampled robot poses. In baseline comparisons against a number of widely used planners, we achieve commensurate performance in terms of task success, planning time and path length, performing successful path planning with obstacle avoidance on a real 7-DoF robot arm.

Path Planning for Intelligent Vehicle Collision Avoidance of Dynamic Pedestrian Using Att-LSTM, MSFM, and MPC at Unsignalized Crosswalk

In this article, path planning for intelligent vehicle collision avoidance of dynamic pedestrian using attention mechanism-long short-term memory network (Att-LSTM), modified social force model (MSFM), and model predictive control (MPC) is systematically investigated, and pedestrian-dynamic vehicle conflict scene at an unsignalized crosswalk is covered. First, a data-driven stacking fusion model based on the Att-LSTM and MSFM is proposed for pedestrian path prediction. Pedestrian heterogeneity (age and gender) is taken into account for the first time. The data-driven stacking fusion model is verified with the existing methods. Then an MPC-based path planning-tracking system considering pedestrian path prediction is developed. The predicted path of pedestrian is treated as a pedestrian-safety region with the combination of a semiellipse and semicircle, and the front wheel steering angle is calculated to prevent the intelligent vehicle from colliding with the dynamic pedestrians. Simulink–Carsim simulations are presented to simulate the real dynamic environment where crossing pedestrians exist. The results illustrate that the developed MPC system considering pedestrian path prediction can provide dynamic path planning performance acceptably and effectively, and make it possible for the intelligent vehicle to present the great feasibility for pedestrian safety protection and traffic efficiency improvement.

Optimization of triage time and sample delivery path in health infrastructure to combat COVID-19

PurposeThis study presented the experience of improving the nucleic acid sample collection and transportation service in response to the epidemic. The main purpose is that through intelligent path planning, combined with the time scheduling of sample points, the process of obtaining results to determine the state of COVID-19 patients could be speeding up.Design/methodology/approachThe research optimized the process, including finding an optimal path to traverse all sample points in the hospital area via intelligent path planning method and standardizing the operation through the time sequence scheduling of each round of support staff to collect and send samples in the hospital area, so as to ensure the shortest time in each round. And the study examines these real-time experiments through retrospective examination.FindingsThe real-time experiments' data showed that the proposed path planning and scheduling model could provide a reliable reference for improving the efficiency of hospital logistics. Testing is a very important part of diagnosis and prompt results are essential. It shows the possibility of applying the shortest-path algorithms to optimize sample collection processes in the hospital and presents the case study that gives the expected outcomes of such a process.Originality/valueThe value of the study lies in the abstraction of a very practical and urgent problem into a TSP. Combining the ant colony algorithm with the genetic algorithm (ACAGA), the performance of path planning is improved. Under the intervention and guidance, the efficiency of hospital regional logistics planning was greatly improved, which may be of greater benefit to critical patients who must go through fever clinic during the epidemic. By detailing how to more rapidly obtain results through engineering method, the paper contributes ideas and plans for practitioners to use. The experience and lessons learned from Tongji Hospital are expected to provide guidance for supporting service measures in national public health infrastructure management and valuable reference for the development of hospitals in other countries or regions.

Path planning of manipulator based on improved RRT algorithm

Aiming at the problem that the 6-DOF manipulator can quickly avoid obstacles in high-dimensional space, the method of path planning based on the improved rapid expansion random tree (RRT) algorithm is proposed. The improved RRT algorithm combines the advantages of the target bias strategy and proposes a method for setting the intermediate bias point. In random sampling, the sampling point is biased to the intermediate bias point,with a certain probability to reduce the blindness of sampling, and use at the same time the path shortening method reduces path nodes and turning points, and optimizes the motion path of the robotic arm. By comparing the simulation results with the RRT and P-RRT algorithms, the effectiveness of the improved RRT algorithm in the calculation time and the number of path nodes is proved.

A Deep Reinforcement Learning Strategy Combining Expert Experience Guidance for a Fruit-Picking Manipulator

When using deep reinforcement learning algorithms for path planning of a multi-DOF fruit-picking manipulator in unstructured environments, it is much too difficult for the multi-DOF manipulator to obtain high-value samples at the beginning of training, resulting in low learning and convergence efficiency. Aiming to reduce the inefficient exploration in unstructured environments, a reinforcement learning strategy combining expert experience guidance was first proposed in this paper. The ratios of expert experience to newly generated samples and the frequency of return visits to expert experience were studied by the simulation experiments. Some conclusions were that the ratio of expert experience, which declined from 0.45 to 0.35, was more effective in improving learning efficiency of the model than the constant ratio. Compared to an expert experience ratio of 0.35, the success rate increased by 1.26%, and compared to an expert experience ratio of 0.45, the success rate increased by 20.37%. The highest success rate was achieved when the frequency of return visits was 15 in 50 episodes, an improvement of 31.77%. The results showed that the proposed method can effectively improve the model performance and enhance the learning efficiency at the beginning of training in unstructured environments. This training method has implications for the training process of reinforcement learning in other domains.

Path Planning Strategy for a Manipulator Based on a Heuristically Constructed Network

Collision-free path planning of manipulators is becoming indispensable for space exploration and on-orbit operation. Manipulators in these scenarios are restrained in terms of computing resources and storage, so the path planning method used in such tasks is usually limited in its operating time and the amount of data transmission. In this paper, a heuristically constructed network (HCN) construction strategy is proposed. The HCN construction contains three steps: determining the number of hub configurations and selecting and connecting hub configurations. Considering the connection time and connectivity of HCN, the number of hub configurations is determined first. The selection of hub configurations includes the division of work space and the optimization of the hub configurations. The work space can be divided by considering comprehensively the similarity among the various configurations within the same region, the dissimilarity among all regions, and the correlation among adjacent regions. The hub configurations can be selected by establishing and solving the optimization model. Finally, these hub configurations are connected to obtain the HCN. The simulation indicates that the path points number and the planning time is decreased by 45.5% and 48.4%, respectively, which verify the correctness and effectiveness of the proposed path planning strategy based on the HCN.

Research on Trajectory Planning in Thunderstorm Weather Based on Dynamic Window Algorithm during Approach Segment

Dynamic window algorithm (DWA) is a local path-planning algorithm, which can be used for obstacle avoidance through speed selection and obtain the optimal path, but the algorithm mainly plans the path for fixed obstacles. Based on DWA algorithm, this paper proposes an improved DWA algorithm based on space-time correlation, namely, space-time dynamic window approach. In SDWA algorithm, a DWA associated with obstacle position and time is proposed to achieve the purpose of path planning for moving obstacles. Then, by setting the coordinates of the initial moving obstacle and identifying safety distance, we can define the shape of the obstacle and the path planning of the approach segment in thunderstorm weather based on the SDWA model was realized. Finally, the superior performance of the model was verified by setting moving obstacles for path planning and selecting the aircraft approach segment in actual thunderstorm weather. The results showed that SDWA has good path-planning performance in a dynamic environment. Its path-planning results were very similar to an actual aircraft performing thunderstorm-avoidance maneuvers, but with more smooth and economical trajectory. The proposed SDWA model had great decision-making potential for approach segment planning in thunderstorm weather.

Implementing B-Spline Path Planning Method Based on Roundabout Geometry Elements

This paper presents a parametric B-spline-based path planning approach for roundabout exit traverse planners to handle successively generated trajectories. A roundabout is a complex driving scene because of its large curvature and the varying intersection leg angles. A few studies focus on the relationship between roundabout intersection leg angle and path performance. This paper discusses B-spline advantages to curve road conditions and the impact of roundabout geometry elements on path planning performance. Knot vectors are defined, and control points are calculated to meet the requirements for generating paths that fit in different roundabout direction maneuvers. Four countries’ roundabout design standards are summarized with their unique geometry elements. Additionally, the Stanley tracking controller was used to calculate the steering behavior. The impacts of maneuver directions and intersection angles are discussed. It is explained stepwise how the maneuver system framework was built and what notable impacts the geometric structure of the roundabout has on the paths.

Path Planning of Long-reach Coupled Tendon-driven Manipulator

Path Planning of Long-reach Coupled Tendon-driven Manipulator

Path planning of mobile manipulator for navigation and object clean-up

Path planning of mobile manipulator for navigation and object clean-up

Neural Path Planning With Multi-Scale Feature Fusion Networks

Path planning is critical for planetary rovers that perform observation and exploration missions in unknown and dangerous environment. And due to the communication delay, it is difficult for the planet rover to receive instructions from Earth in time to guide its own movement. In this work, we present a novel neural network-based algorithm to solve the global path planning problem for planetary rovers. Inspired by feature pyramid networks used for object detection, we construct a deep neural network model, termed the Pyramid Path Planning Network (P3N), which has a well-designed backbone that efficiently learns a global feature representation of the environment, and a feature pyramid branch that adaptively fuses multi-scale features from different levels to generate the local feature representation with rich semantic information. The P3N learns environmental dynamics from terrain images of planetary surface taken by satellites, without using additional elevation information to construct an explicit environmental model in advance, and can perform path planning policy after end-to-end training. We evaluate the effectiveness of the proposed method on synthetic grid maps and a realistic data set constructed from the lunar terrain images. Experimental results demonstrate that our P3N has higher prediction accuracy and faster computation speed compared to the baseline methods, and generalize better in large-scale environments.

Research on Robot Path Perception and Optimization Technology Based on Whale Optimization Algorithm

With the development of modern sensor technology, the automatic movement of robot has become a reality, and improving the path planning performance of robot in dynamic and complex environment is an important development direction of mobile robot intelligence. This time, based on the idea of hybrid path planning, whale optimization algorithm and computer perception technology are introduced to plan the path of the robot. In order to improve the performance of the traditional whale optimization algorithm, it is optimized and improved by genetic algorithm. Through the performance simulation analysis of the improved whale optimization algorithm, it can be seen that the improved algorithm has better path optimization performance and significantly improved efficiency compared with other traditional algorithms. It can perceive and calculate an optimized path.

Multi-strategy fusion differential evolution algorithm for UAV path planning in complex environment

The path planning of Unmanned Aerial Vehicle (UAV) is a real-world optimization problem, and even develops into a hard optimization problem with many objectives and constraints when UAVs work in a complex environment. In a complex environment, the resulting constraints can lead to decrease the quantity of the feasible solutions, so that can bring difficulties to plan routes for UAVs. Therefore, it is necessary to design a high-quality planner for a UAV in a complex environment. In this work, we have proposed a Multi-Strategy Fusion Differential Evolution algorithm (MSFDE). The proposed algorithm integrates the multi-population strategy, the novel self-adaptive strategy and the ensemble of the interactive mutation strategy in order to balance the exploitation and exploration capabilities. The multi-population strategy is used to divide the whole population into the three indicator subpopulations and a reward subpopulation for maintaining the diversity of the whole population; the novel self-adaptive strategy is introduced to control the parameters F and CR based on the teaching-learning-based optimization method; the ensemble of the interactive mutation strategy is to exchange the information among the three indicator subpopulations on each generation for boosting the population diversity. The constraints in the UAV path planning are transformed into the objective functions by the linear weighted sum method. Scenario 1, 2, 3, and 4 are designed with different complex level, and other eight algorithms are introduced to be compared with MSFDE. The simulation results confirm that MSFDE has an outstanding performance for the UAV three-dimensional path planning in the complex environment.

Path planning for manipulators based on an improved probabilistic roadmap method

An indispensable feature of an intelligent manipulator is its capability to quickly plan a short and safe path in the presence of obstacles in its workspace. Among the path planning methods, the probabilistic roadmap (PRM) method has been widely applied in path planning for a high-dimensional manipulator to avoid obstacles. However, its efficiency remains disappointing when the free space of manipulators contains narrow passages. To solve this problem, an improved PRM method is proposed in this paper. Based on a virtual force field, a new sampling strategy of PRM is presented to generate configurations more appropriate for practical application in the free space. Correspondingly, in order to interconnect these configurations to form a roadmap, a new connection strategy is designed, which consists of three stages and can gradually improve the connectivity of the roadmap. The contributions of this paper are as follows. The new sampling strategy can increase the sampling density at the narrow passages of the free space and reduce the redundancy of the samples in the wide-open regions of the free space; the three-stage connection strategy for interconnecting samples can ensure a high-connectivity roadmap; through synthesizing the above strategies, the improved PRM method is more suitable for path planning of manipulators to avoid obstacles efficiently in a cluttered environment. Simulations and experiments are carried out to evaluate the validity of the proposed method, and the method is available for manipulator of any degrees of freedom.

Planning Fail-Safe Trajectories for Space Robotic Arms.

A frequent concern for robot manipulators deployed in dangerous and hazardous environments for humans is the reliability of task executions in the event of a joint failure. A redundant robotic manipulator can be used to mitigate the risk and guarantee a post-failure task completion, which is critical for instance for space applications. This paper describes methods to analyze potential risks due to a joint failure, and introduces tools for fault-tolerant task design and path planning for robotic manipulators. The presented methods are based on off-line precomputed workspace models. The methods are general enough to cope with robots with any type of joint (revolute or prismatic) and any number of degrees of freedom, and might include arbitrarily shaped obstacles in the process, without resorting to simplified models. Application examples illustrate the potential of the approach.

Planning on topological map using omnidirectional images and spherical CNNs*

Map building and path planning are essential for long-term visual navigation. Methods, such as semi-parametric topological memory (SPTM), use deep learning to build a topological map consisting of nodes sampled from an agent's past observations and edges generated by a neural network (NN) and perform path planning on the map. Such methods require neither accurate sensor nor advanced expertise for map building needed in classical metric-map-based techniques such as visual simultaneous localization and mapping. However, they often plan improper paths including teleportations and detours, even if we have collected enough training data. In this paper, we propose a topological-map-based planning method that includes two modifications to SPTM to address this problem. The first modification is that we observe omnidirectional images and construct an NN on the basis of spherical convolutional NNs, which guarantee rotation invariance on omnidirectional images. The second modification is that we train the NN on a metric learning framework. We conducted experiments to show the effectiveness and applicability to real world of our method for path planning in visual navigation. The results indicate that the first modification prevents detouring and the second one prevents teleportations in path planning.

Visibility graph-based path-planning algorithm with quadtree representation

In recent years, unmanned surface vehicles (USVs) have been widely used in various applications. To fulfill target missions, USVs must be able to plan their path to the destination. Although many path-planning methods have been developed, most are inappropriate for application in complex marine environments. In this study, we propose a novel path-planning algorithm, which combines the merits of quadtree representation and visibility graphs, for efficient path planning of USVs in complex marine environments. Quadtree representation reduced the computation time and memory consumption. Additionally, the wind and wave data were considered to shorten the voyage time from the departure to destination points. Furthermore, the Dijkstra algorithm was applied to the quadtree to determine the suboptimal path. We then constructed a visibility graph with path waypoints using an improved line-of-sight algorithm to perform path planning on the visibility graph. The proposed algorithm not only facilitates path planning with minimum voyage time but also maintains the USV clearance distance from the coastline. To verify the performance, the proposed algorithm was compared with other methods for South Korean case studies. Overall, the proposed algorithm was able to generate a high-quality suboptimal path for the USV within a short time span.