Motion Planning Algorithms Research Articles

Optimization Algorithms for Dynamic Environmental Sensing and Motion Planning of Quadruped Robots in Complex Environments on Unmanned Offshore Platforms

Optimization Algorithms for Dynamic Environmental Sensing and Motion Planning of Quadruped Robots in Complex Environments on Unmanned Offshore Platforms

Robot learning-enhanced tree-based algorithms for kinodynamic motion planning: A comparative analysis

Kinodynamic motion planning is pivotal in advancing robotics, en- abling autonomous systems to navigate dynamic environments effectively while adhering to both kinematic and dynamic constraints. This study delves into the efficacy of tree sampling-based planners, namely the Rapidly- exploring Random Tree (RRT), Rapidly-exploring Random Tree Star (RRT*), and Dominance Informed Region Trees (DIRT), in kinodynamic motion planning. Through a comparative analysis focusing on both fully informed and uninformed versions of these algorithms, I explore their performance in environments with dynamic constraints. Special emphasis is placed on the integration of learned controls, aiming to enhance maneuver planning. My research reveals significant differences in success rates, iterations, and path costs among the algorithms, underscoring DIRTs superiority under certain conditions and the beneficial impact of learned controls. These findings contribute valuable insights into the selection and optimization of motion planning algorithms, paving the way for more efficient and adapt- able autonomous systems.

Robot Motion Planning Based on an Adaptive Slime Mold Algorithm and Motion Constraints

The rapid advancement of artificial intelligence technology has significantly enhanced the intelligence of mobile robots, facilitating their widespread utilization in unmanned driving, smart home systems, and various other domains. As the scope, scale, and complexity of robot deployment continue to expand, there arises a heightened demand for enhanced computational power and real-time performance, with path planning emerging as a prominent research focus. In this study, we present an adaptive Lévy flight–rotation slime mold algorithm (LRSMA) for global robot motion planning, which incorporates LRSMA with the cubic Hermite interpolation. Unlike traditional methods, the algorithm eliminates the need for a priori knowledge of appropriate interpolation points. Instead, it autonomously detects the convergence status of LRSMA, dynamically increasing interpolation points to enhance the curvature of the motion curve when it surpasses the predefined threshold. Subsequently, it compares path lengths resulting from two different objective functions to determine the optimal number of interpolation points and the best path. Compared to LRSMA, this algorithm reduced the minimum path length and average processing time by (2.52%, 3.56%) and (38.89%, 62.46%), respectively, along with minimum processing times. Our findings demonstrate that this method effectively generates collision-free, smooth, and curvature-constrained motion curves with the least processing time.

Multi-Vehicle Collaborative Planning Technology under Automatic Driving

Autonomous vehicles hold the potential to significantly improve traffic efficiency and advance the development of intelligent transportation systems. With the progression of autonomous driving technology, collaborative planning among multiple vehicles in autonomous driving scenarios has emerged as a pivotal challenge in realizing intelligent transportation systems. Serving as the cornerstone of unmanned mission decision-making, collaborative motion planning algorithms have garnered increasing attention in both theoretical exploration and practical application. These methods often follow a similar paradigm: the system initially discerns the driving intentions of each vehicle, subsequently assesses the surrounding environment, engages in path-planning, and formulates specific behavioral decisions. The paper discusses trajectory prediction, game theory, following behavior, and lane merging issues within the paradigm mentioned above. After briefly introducing the background of multi-vehicle autonomous driving, it provides a detailed description of the technological prerequisites for implementing these techniques. It reviews the main algorithms in motion planning, their functionalities, and applications in road environments, as well as current and future challenges and unresolved issues.

Space and time joint optimization for vertical takeoff and landing aircraft in dense obstacles and interference environments

ABSTRACT The Vertical Takeoff and Landing Aircraft (VTOL) is capable of both low-speed hovering and high-speed flight. This paper proposes an autonomous motion planning and anti-wind disturbance control algorithm for the VTOL, implemented spatial and temporal joint motion planning for VTOL, under the limitation of maximum flight speed of the aircraft, VTOL can achieve fully autonomous flight in complex environments, which can realize the autonomous flight of a single aircraft in a complex and dense environment, achieve real-time perception and avoidance of static and dynamic obstacles. The information generated by motion planning includes the position, velocity, and acceleration information of the aircraft, and proposes a wind disturbance estimation and active control strategy in a wind disturbance environment, accurate estimation of wind disturbance is achieved through a state observer, and the wind disturbance was suppressed through nonlinear error feedback control law, implemented wind disturbance estimation and suppression for aircraft in wind disturbance environments, and achieved complete autonomous motion planning and trajectory tracking of VTOL in a complex and wind disturbance environment. Our work can provide a basis for the collaborative control of multiple UAVs.

An Overview of Motion-Planning Algorithms for Autonomous Ground Vehicles with Various Applications

<div>With the rapid development and the growing deployment of autonomous ground vehicles (AGVs) worldwide, there is an increasing need to design reliable, efficient, robust, and scalable motion-planning algorithms. These algorithms are crucial for fulfilling the desired goals of safety, comfort, efficiency, and accessibility. To design optimal motion-planning algorithms, it is beneficial to explore existing techniques and make improvements by addressing the limitations of associated techniques, utilizing hybrid algorithms, or developing novel strategies. This article categorizes and overviews numerous motion-planning algorithms for AGVs, shedding light on their strengths and weaknesses for a comprehensive understanding. For various applications of AGVs, such as urban and off-road autonomous driving, the features of driving conditions and vehicle kinodynamics are outlined, and sample-tailored motion-planning algorithms built upon relevant canonical techniques are briefly introduced. As a result of the overview, future research efforts on motion-planning techniques are identified and discussed.</div>

V2X assisted co‐design of motion planning and control for connected automated vehicle

AbstractThe rapid development of vehicle‐to‐everything (V2X) communication technologies significantly promotes the revolution of intelligent transportation systems. V2X communication is expected to play a critical role in enhancing the safety and efficiency of connected and automated vehicles (CAVs), especially for mixed traffic scenarios. Additionally, the computational and storage capabilities of roadside units (RSUs) will be harnessed to effectively enhance the motion planning and control performance of CAVs within the constraints of limited on‐board computational resources. Thus, a V2X assisted co‐design of motion planning and control algorithm for CAVs to improve their situational awareness and computational efficiency is proposed. Under this architecture, a pre‐planning algorithm is proposed first to utilize the computational and storage capabilities of RSUs and generate feasible trajectories for different driving tasks. By analysing the relationship between driving risk index and motion planning performance, an online‐planning algorithm is derived to modify the pre‐planned trajectories in real‐time with static or dynamic obstacles. Furthermore, the lateral and longitudinal control of the vehicle using the Frenet coordinate system is decoupled. The lateral control employs an offline linear quadratic regulator (LQR) from RSUs to control the steering angle of the vehicle. The longitudinal control employs a dual‐loop PID to control the throttle opening of the vehicle. The performance of the proposed framework is evaluated and demonstrated by a Carsim‐Prescan simulation study in different mixed traffic scenarios. Compared with conventional methods, the proposed method improves the computational efficiency by 23% and reduces the collision rate by 13%.

Developing a model for unmanned aerial vehicle with fixed-wing using 3D-map exploring rapidly random tree technique

While the motion planning algorithms consider the obstacles that were known in the map, it is possible to use obstacle avoidance algorithms to take over and send commands to theunmanned aerial vehicle (UAV), when there is an unknown obstacle on the way. The rapidly random tree (RRT) algorithm is used to plan paths for a quad-copter or a fixed-wing UAV. This work develops a model for UAV with fixed-wing using a 3D map exploring the RRT technique. The first step is to obtain a 3D occupancy map from the map data stored in the UAV city to provide a map with some pre-generated obstacles. The contribution of this work is to use RRT planning for 3D state space, where the motion segment or motion primitive connecting the two consecutive states should be defined in a 3D space while satisfying the motion constraints of a UAV. The simulation includes setting up a 3D map, providing the starting and destination pose, planning a way using RRT and 3D Dubins moving primitives, smoothing the acquired trajectory, and simulating the UAV flight. The results obtained demonstrate that the smoothed-generated waypoints significantly improved tracking in general with shorter paths.

Multi-Risk-RRT: An Efficient Motion Planning Algorithm for Robotic Autonomous Luggage Trolley Collection at Airports

Multi-Risk-RRT: An Efficient Motion Planning Algorithm for Robotic Autonomous Luggage Trolley Collection at Airports

Trajectory Planning, Kinematics, and Experimental Validation of a 3D-Printed Delta Robot Manipulator

This paper presents the implementation, trajectory planning, kinematics, and experimental validation of an open-source 3D-printed delta robot manipulator. The robot’s hardware consists of three servo motors, an Arduino Uno microcontroller, and a Pulse Width Modulation (PWM) servo module. The software includes a trapezoidal velocity profile planner and an inverse and forward kinematics solver to calculate the motor angles required to achieve a desired end-effector position in task space. The 3D printed parts were obtained from an open-source Thingiverse project and assembled to form the robot’s kinematic structure. The robot’s performance was evaluated in terms of its accuracy, repeatability, and maximum speed for a pick-and-place task. Experimental results show that the robot can achieve a positioning accuracy of 2.2 mm, with a top speed of 0.4 m/s, 30 picks per minute, and load carrying capacity up to 200 g. These results are satisfactory, considering the ease of assembly and the cost-effectiveness of the robot. The comparison of the calculated motor angles with actual motor angles obtained through additional potentiometer wires soldering shows promising results. The proposed robot’s cost-effective characteristics, utilization of open-source additive manufacturing, and motion planning algorithms make it suitable for various applications, including education, research, and small-scale industrial applications.

An Uncertainty-Aware Lane Change Motion Planning Algorithm Based on Probabilistic Trajectory Prediction Distribution

An Uncertainty-Aware Lane Change Motion Planning Algorithm Based on Probabilistic Trajectory Prediction Distribution

A Multi-Source-Data-Assisted AUV for Path Cruising: An Energy-Efficient DDPG Approach

As marine activities expand, deploying underwater autonomous vehicles (AUVs) becomes critical. Efficiently navigating these AUVs through intricate underwater terrains is vital. This paper proposes a sophisticated motion-planning algorithm integrating deep reinforcement learning (DRL) with an improved artificial potential field (IAPF). The algorithm incorporates remote sensing information to overcome traditional APF challenges and combines the IAPF with the traveling salesman problem for optimal path cruising. Through a combination of DRL and multi-source data optimization, the approach ensures minimal energy consumption across all target points. Inertial sensors further refine trajectory, ensuring smooth navigation and precise positioning. The comparative experiments confirm the method’s energy efficiency, trajectory refinement, and safety excellence.

Multi-robot geometric task-and-motion planning for collaborative manipulation tasks

We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple robots to goal regions in the presence of other movable objects. We focus on collaborative manipulation tasks where the robots have to adopt intelligent collaboration strategies to be successful and effective, i.e., decide which robot should move which objects to which positions, and perform collaborative actions, such as handovers. To endow robots with these collaboration capabilities, we propose to first collect occlusion and reachability information for each robot by calling motion-planning algorithms. We then propose a method that uses the collected information to build a graph structure which captures the precedence of the manipulations of different objects and supports the implementation of a mixed-integer program to guide the search for highly effective collaborative task-and-motion plans. The search process for collaborative task-and-motion plans is based on a Monte-Carlo Tree Search (MCTS) exploration strategy to achieve exploration-exploitation balance. We evaluate our framework in two challenging MR-GTAMP domains and show that it outperforms two state-of-the-art baselines with respect to the planning time, the resulting plan length and the number of objects moved. We also show that our framework can be applied to underground mining operations where a robotic arm needs to coordinate with an autonomous roof bolter. We demonstrate plan execution in two roof-bolting scenarios both in simulation and on robots.

GPU-Accelerated Interaction-Aware Motion Prediction

Before their massive deployment, autonomous vehicles need to prove in complex scenarios such that they can reach human driving proficiency while guaranteeing higher safety levels. One of the most important human traits to negotiating traffic is the ability to predict the future behavior of surrounding vehicles as a basis for agile and safe navigation. This capability is particularly challenging for an autonomous system in highly interactive driving situations, such as intersections or roundabouts. In this paper, a set of techniques to bring a computationally expensive state-of-the-art motion prediction algorithm to real-time execution are presented with the goal of meeting a standard motion-planning algorithm execution frequency of 5 Hz, which is the primary consumer of motion predictions. This is achieved by applying novel and existing parallelization algorithms that take advantage of graphic processing units (GPUs) through the compute unified device architecture (CUDA) programming language and managing to produce an average 5× speedup over raw C++ in the cases studied. The optimizations are then evaluated in public datasets and a real vehicle on a test track.

Adaptive Hybrid Optimization Learning-Based Accurate Motion Planning of Multi-Joint Arm.

Motion planning is important to the automatic operation of the manipulator. It is difficult for traditional motion planning algorithms to achieve efficient online motion planning in a rapidly changing environment and high-dimensional planning space. The neural motion planning (NMP) algorithm based on reinforcement learning provides a new way to solve the above-mentioned task. Aiming to overcome the difficulty of training the neural network in high-accuracy planning tasks, this article proposes to combine the artificial potential field (APF) method and reinforcement learning. The neural motion planner can avoid obstacles in a wide range; meanwhile, the APF method is exploited to adjust the partial position. Considering that the action space of the manipulator is high-dimensional and continuous, the soft-actor-critic (SAC) algorithm is adopted to train the neural motion planner. By training and testing with different accuracy values in a simulation engine, it is verified that, in the high-accuracy planning tasks, the success rate of the proposed hybrid method is better than using the two algorithms alone. Finally, the feasibility of directly transferring the learned neural network to the real manipulator is verified by a dynamic obstacle-avoidance task.

Multi-Point path planning for robots based on deep reinforcement learning

Motion Planning is a key technology for mobile robots, which decomposes a Motion task that cannot be completed by a single action into multiple discrete actions that can be performed. This paper aims to design a robot motion planning algorithm based on reinforcement learning and make a robot carry out continuous multi-objective point motion planning. Motion planning network is a planning algorithm based on a neural network, and DQN is a classical algorithm in the field of reinforcement learning. Based on the two kinds of algorithm for motion planning, the Deep Q - learning algorithm chooses the robot’s next target, and then through the motion planning of the network between the current coordinates to the next target path planning. This paper analyzes the performance of the multi-point motion planning algorithm, and the results show that the algorithm is able to a higher success rate of successful completion of the task planning, but the reward strategy derived from the experiment still has the possibility of optimization.

Astrodynamics-Informed Kinodynamic Sampling-Based Motion Planning for Relative Spacecraft Motion

This paper presents a sampling-based spacecraft relative motion planning algorithm to reconfigure a spacecraft from a given initial state to a desired final state, subject to kinodynamic (simultaneous kinematic and dynamics) constraints. We leverage theoretical advancement in the field of sampling-based motion planning in robotics and extend their application to achieve motion planning in astrodynamics. We present an astrodynamics-informed kinodynamic motion planning (AIKMP) algorithm that takes advantage of a spacecraft’s natural motion to compute a safe, fuel-efficient motion plan for spacecraft reconfiguration in a cluttered environment. This algorithm is developed in the relative orbital element space of the deputy spacecraft around a chief spacecraft that provides very useful geometric insight to relative spacecraft motion, as well as helps in generating an overall smooth final transfer trajectory without demanding additional postprocessing of the transfer solution. Through several example scenarios, we demonstrate that the AIKMP algorithm is regularly able to compute safe and fuel-efficient solutions to relative transfer problems. We also show that this algorithm iteratively improves on its computed solutions and thus holds the capability of finding near-optimal transfer solutions given a sufficient number of algorithm iterations—without requiring an initial guess of the solution.

Interleaved Predictive Control and Planning for an Unmanned Aerial Manipulator With on-the-Fly Rapid Re-Planning in Unknown Environments

Unmanned Aerial Manipulators (UAM) are gaining attention within the unmanned aerial systems research community. They can be used for aerial manipulation tasks such as object retrieval from confined and hard-to-reach spaces. The coupled dynamics between the arm and the base and also the limited flight time would require the development and implementation of optimal motion planning and robust flight control strategies. In this paper, we propose a novel integrated planning and control strategy for object retrieval. A new kinodynamic version of the Rapidly-exploring Random Tree (RRT*), called Lazy-Steering-RRT*, is developed for planning UAM&#x2019;s motion from its start to a pre-grasp state, while keeping the motion of the arm to a minimum. This planning can be carried out on the fly by using Machine-Learning-based techniques to construct the edges in the search tree in a time-efficient way. This facilitates re-planning, as the environment is gradually sensed by limited-range sensors onboard. Once the UAM reaches the pre-grasp state at the end of the motion cycle, an RRT approach is then utilized, where motions of the base and the arm are coordinated for reaching and grasping the object. A novel partitioned control approach, composed of model predictive and PID controllers, is utilized for the UAM to track the planned trajectory. The overall motion planning and control algorithms have been implemented in simulation using Multibody Physics Engines and a number of representative simulation runs are presented. Our results show that the approach is effective in successfully executing the object retrieval task even in confined spaces. <i>Note to Practitioners</i>&#x2014;This research was motivated by the problem of retrieving an object from a confined space such as that in a collapsed building using an unmanned aerial manipulator. Our approach has the following advantages over existing methods: a) Planning and re-planning can be carried out on the fly, b) No prior information about the environment is needed, c) High-frequency energy-optimal control of the UAM is carried out, and d) the planner and the controller are coupled. In this paper, we suggest some novel approaches on both planning and control aspects of the navigation and also on the integration of these two modules to provide a navigation package that meets all the requirements in the retrieval task. Furthermore, our algorithm is computationally-light, therefore, well suited for on the fly planning and control.

Deep Learning-Based Trajectory Planning and Control for Autonomous Ground Vehicle Parking Maneuver

In this paper, a novel integrated real-time trajectory planning and tracking control framework capable of dealing with autonomous ground vehicle (AGV) parking maneuver problems is presented. In the motion planning component, a newly-proposed idea of utilizing deep neural networks (DNNs) for approximating optimal parking trajectories is further extended by taking advantages of a recurrent network structure. The main aim is to fully exploit the inherent relationships between different vehicle states in the training process. Furthermore, two transfer learning strategies are applied such that the developed motion planner can be adapted to suit various AGVs. In order to follow the planned maneuver trajectory, an adaptive learning tracking control algorithm is designed and served as the motion controller. By adapting the network parameters, the stability of the proposed control scheme, along with the convergence of tracking errors, can be theoretically guaranteed. In order to validate the effectiveness and emphasize key features of our proposal, a number of experimental studies and comparative analysis were executed. The obtained results reveal that the proposed strategy can enable the AGV to fulfill the parking mission with enhanced motion planning and control performance. <i>Note to Practitioners</i>&#x2014;This article was motivated by the problem of optimal automatic parking planning and tracking control for autonomous ground vehicles (AGVs) maneuvering in a restricted environment (e.g., constrained parking regions). A number of challenges may arise when dealing with this problem (e.g., the model uncertainties involved in the vehicle dynamics, system variable limits, and the presence of external disturbances). Existing approaches to address such a problem usually exploit the merit of optimization-based planning/control techniques such as model predictive control and dynamic programming in order for an optimal solution. However, two practical issues may require further considerations: 1). The nonlinear (re)optimization process tends to consume a large amount of computing power and it might not be affordable in real-time; 2). Existing motion planning and control algorithms might not be easily adapted to suit various types of AGVs. To overcome the aforementioned issues, we present an idea of utilizing the recurrent deep neural network (RDNN) for planning optimal parking maneuver trajectories and an adaptive learning NN-based (ALNN) control scheme for robust trajectory tracking. In addition, by introducing two transfer learning strategies, the proposed RDNN motion planner can be adapted to suit different AGVs. In our follow-up research, we will explore the possibility of extending the developed methodology for large-scale AGV parking systems collaboratively operating in a more complex cluttered environment.

Deep Imitation Learning of Nonlinear Model Predictive Control Laws for a Safe Physical Human–Robot Interaction

This paper proposes motion planning algorithms for industrial manipulators in the presence of human operators based on deep neural networks (DNNs), aimed at imitating the behavior of a nonlinear model predictive control (NMPC) scheme. The proposed DNN solutions retain the safety features of NMPC in terms of speed and separation monitoring, defined according to the guidelines in the ISO/TS 15066 standard. At the same time, they improve the robot performance in terms of task completion time, and of a-posteriori evaluation of the NMPC cost function on experimental data. The reasons for this improvement are the reduced computational delay of running a DNN compared to solving the nonlinear programs associated to NMPC, and the ability to implicitly learn how to predict the human operator's motion from the training set.