Motion Planning Of Mobile Robots Research Articles

A novel vector-field-based motion planning algorithm for 3D nonholonomic robots

This paper focuses on the motion planning for mobile robots in 3D, which are modeled by 6-DOF rigid body systems with nonholonomic kinematics constraints. We not only specify the target position, but also impose the requirement of the heading direction at the terminal time, which gives rise to a new and more challenging 3D motion planning problem. The proposed planning algorithm involves a novel velocity vector field (VF) over the workspace, and by following the VF, the robot can be navigated to the destination with the specified heading direction. In order to circumvent potential collisions with obstacles and other robots, a composite VF is designed by composing the navigation VF and an additional VF tangential to the boundary of the dangerous area. Moreover, we propose a priority-based algorithm to deal with the motion coupling issue among multiple robots. Finally, simulations are conducted to verify the theoretical results.

Motion planning for mobile robots based on improved artificial potential field algorithm

Motion planning for mobile robots based on improved artificial potential field algorithm

Safe Motion Planning and Control for Mobile Robots: A Survey

Safe Motion Planning and Control for Mobile Robots: A Survey

Causal deconfounding deep reinforcement learning for mobile robot motion planning

Deep reinforcement learning (DRL) has emerged as an efficient approach for motion planning in mobile robot systems. It leverages the offline training process to enhance real-time computation efficiency. In DRL-based methods, the DRL models are trained to compute an action based on the current state of the robot and the surrounding obstacles. However, the trained models may capture spurious correlations through potential confounders, resulting in non-robust state representations, which limits the models’ robustness and generalizability. In this paper, we propose a Causal Deconfounding DRL method for Motion Planning, CD-DRL-MP, to address spurious correlations and learn robust and generalizable policies. Specifically, we formalize the temporal causal relationships between states and actions using a structural causal model. We then extract the minimal sufficient state representation set by blocking the backdoor paths in the causal model. Finally, using the representation set, CD-DRL-MP learns the causal effect between states and actions while mitigating the detrimental influence of potential confounders and computes motion commands for mobile robots. Comprehensive experiments show that the proposed method significantly outperforms non-causal DRL methods and existing causal methods, while guaranteeing good robustness and generalizability.

Robust motion planning for mobile robots under attacks against obstacle localization

AbstractThanks to its real-time computation efficiency, deep reinforcement learning (DRL) has been widely applied in motion planning for mobile robots. In DRL-based methods, a DRL model computes an action for a robot based on the states of its surrounding obstacles, including other robots that may communicate with it. These methods always assume that the environment is attack-free and the obtained obstacles’ states are reliable. However, in the real world, a robot may suffer from obstacle localization attacks (OLAs), such as sensor attacks, communication attacks, and remote-control attacks, which cause the robot to retrieve inaccurate positions of the surrounding obstacles. In this paper, we propose a robust motion planning method ObsGAN-DRL, integrating a generative adversarial network (GAN) into DRL models to mitigate OLAs in the environment. First, ObsGAN-DRL learns a generator based on the GAN model to compute the approximation of obstacles’ accurate positions in benign and attack scenarios. Therefore, no detectors are required for ObsGAN-DRL. Second, by using the approximation positions of the surrounding obstacles, ObsGAN-DRL can leverage the state-of-the-art DRL methods to compute collision-free motion commands (e.g., velocity) efficiently. Comprehensive experiments show that ObsGAN-DRL can mitigate OLAs effectively and guarantee safety. We also demonstrate the generalization of ObsGAN-DRL.

Sequential control barrier functions for mobile robots with dynamic temporal logic specifications

We address a motion planning and control problem for mobile robots to satisfy rich, time-varying tasks expressed as Signal Temporal Logic (STL) specifications. The specifications may include tasks with nested temporal operators or time-conflicting requirements (e.g., achieving periodic tasks or tasks defined within the same time interval). Moreover, the tasks can be defined in locations changing with time (i.e., dynamic targets), and their future motions are not known a priori. This unpredictability requires an online control approach which motivates us to investigate the use of control barrier functions (CBFs). The proposed CBFs take into account the actuation limits of the robots and a feasible sequence of STL tasks. They define time-varying feasible sets of states the system must always stay inside. We show the feasible sequence generation process that even includes the decomposition of periodic tasks and alternative scenarios due to disjunction operators. The sequence is used to define CBFs, ensuring STL satisfaction. We also show some theoretical results on the correctness of the proposed method. We illustrate the benefits of the proposed method and analyze its performance via simulations and experiments with aerial robots.

Homotopy-Aware Efficiently Adaptive State Lattices for Mobile Robot Motion Planning in Cluttered Environments

Homotopy-Aware Efficiently Adaptive State Lattices for Mobile Robot Motion Planning in Cluttered Environments

Motion planning and control of an autonomous mobile robot

The application of autonomous mobile robots (AMRs) has gradually become crucial in smart factories due to the advantages of improving production efficiency and reducing labour costs. Motion planning has been a key part of AMR control development. This paper presents motion planning and position tracking control systems of an omnidirectional wheel AMR powered by a hybrid fuel cell and battery power source. First, the kinematical and dynamic models of the AMR are introduced. The navigation system comprises three loops, with the first loop being motor control, the second loop being position tracking control, and a motion planning layer. The position data of the AMR for feedback control is obtained through sensor fusion of data from the inertial measurement unit (IMU) sensor, encoder sensor, and ranging sensor with simultaneous localisation and mapping (SLAM) algorithm. The motion planning is then applied to obtain an optimal path with the shortest distance and collision-free movement. In addition, the tracking algorithm is designed to drive the AMR to follow the optimal path and achieve high accuracy. The experimental results show a 30% improvement in tracking accuracy compared to traditional approaches and 8 hours of continuous working, which is promising for industrial applications, and the results are satisfactory in terms of both accuracy and efficiency requirements.

PRM Path Smoothening by Circular Arc Fillet Method for Mobile Robot Navigation

The problem of motion planning and navigation for mobile robots in complex environments has been a central issue in robotics. Navigating these environments requires sophisticated algorithms that handle obstacles and provide smooth, efficient paths. The Probabilistic Roadmap (PRM) method is a widespread technique in robotics for constructing paths for mobile robot navigation. In this study, we propose a novel path-smoothing method using arc fillets for path planning, building on PRM's foundation in the presence of obstacles. Our method operates in two primary stages to improve path efficiency and quality. The first stage generates the shortest path between the initial and goal states in an obstacle-rich environment using PRM, constructing a straight-line, collision-free route. The second stage smooths corners caused by nodes with arc fillets, ensuring smooth turns and minimizing abrupt changes in direction, resulting in more natural and efficient robot motion. We conducted simulations and tests using various PRM features to evaluate the proposed method. The results indicate that the built route offers a smooth turning motion and quicker, more compact movement while evading obstacles. This study contributes to mobile robot navigation by offering a practical approach to improving pathway quality in challenging environments.

An Optimized and Stable CF2 Mobile Robot Motion Planning Approach and Adaptation for Moving Targets

In this paper, the mobile robot motion planning approach (PSO-CF2-mt: PSO-CF2 for moving targets) is improved to track a moving target following a smooth path. PSO-CF2-mt was tested previously in various static and dynamic environments and proved its capacity to track moving targets whatever the form of the target’s trajectory. The problem with PSO-CF2-mt that we want to face in this paper is the stability of the robot’s motion. It is tackled by limiting the angular velocity of the robot. The angular speed limit is PSO selected and the variance of the angular speed is added to the fitness function as a third objective. This new version of PSO-CF2-mt is called OS-CF2-mt (Optimized Stable CF2-mt). Simulation results prove the capacity of OS-CF2-mt in ensuring stable travels for mobile robots following short, secure and smooth paths when tracking moving targets whether the environment is static or dynamic and whatever the form of the target’s trajectory.

Event-triggered reconfigurable reinforcement learning motion-planning approach for mobile robot in unknown dynamic environments

Deep reinforcement learning (DRL) is an essential technique for autonomous motion planning of mobile robots in dynamic and uncertain environments. In attempting to acquire a satisfactory DRL-based motion planning strategy, the mobile robots encountered several difficulties, including poor convergence, insufficient sample information, and low learning efficiency. These problems not only consume plenty of training time, but also bring a negative impact on motion planning performance. One promising research direction is to provide a more effective network framework for DRL-based policies. Along this line of thinking, our paper presents a novel DRL-based motion planning approach called Reconfigurable Structure of Deep Deterministic Policy Gradient (RS-DDPG) for mobile robots. To account for the poor convergence, the proposed approach first introduces an event-triggered reconfigurable actor–critic network framework for motion policy that adaptively changes its network structure to suppress the overestimation of action value. Then, the time convergence of the motion policy can be enhanced based on the value actions with minor valuation deviation. Afterwards, an adaptive reward mechanism is designed for reconfigurable networks to compensate for the lack of sample information. To deal with the problem of low learning efficiency, we developed a sample pretreatment method for the experience samples, which employs three novel techniques to improve the sample utilization, including a double experience memory buffer, a variable proportional sampling principle, and a similarity judgment mechanism. In extensive experiments, the proposed method outperforms the compared approaches.

Online Search-Based Collision-Inclusive Motion Planning and Control for Impact-Resilient Mobile Robots

This article focuses on the emerging paradigm shift of collision-inclusive motion planning and control for impact-resilient mobile robots, and develops a unified hierarchical framework for navigation in unknown and partially observable cluttered spaces. At the lower level, we develop a deformation recovery control and trajectory replanning strategy that handles collisions that may occur at run time, locally. The low-level system actively detects collisions (via embedded Hall effect sensors on a mobile robot built in-house), enables the robot to recover from them, and locally adjusts the postimpact trajectory. Then, at the higher level, we propose a search-based planning algorithm to determine how to best utilize potential collisions to improve certain metrics, such as control energy and computational time. Our method builds upon A* with jump points. We generate a novel heuristic function, and a collision checking and adjustment technique, thus making the A* algorithm converge faster to reach the goal by exploiting and utilizing possible collisions. The overall hierarchical framework generated by combining the global A* algorithm and the local deformation recovery and replanning strategy, as well as individual components of this framework, are tested extensively both in simulation and experimentally. An ablation study draws links to related state-of-the-art search-based collision-avoidance planners (for the overall framework), as well as search-based collision-avoidance and sampling-based collision-inclusive global planners (for the higher level). Results demonstrate our method's efficacy for collision-inclusive motion planning and control in unknown environments with isolated obstacles for a class of impact-resilient robots operating in 2-D.

A Specification-Guided Framework for Temporal Logic Control of Nonlinear Systems

This article proposes a specification-guided framework for control of nonlinear systems with linear temporal logic (LTL) specifications. In contrast with well-known abstraction-based methods, the proposed framework directly characterizes the winning set, i.e., the set of initial conditions from which a given LTL formula can be realized, over the continuous state space of the system via a monotonic operator. Following this characterization, an algorithm is proposed to practically approximate the operator via an adaptive interval subdivision scheme, which yields a finite-memory control strategy. We show that the proposed algorithm is sound for full LTL specifications, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">robustly complete</i> for specifications recognizable by deterministic Büchi automaton (DBA), the latter in the sense that control strategies can be found whenever the given specification can be satisfied with additional bounded disturbances. Without having to compute and store the abstraction and the resulting product system with the DBA, the proposed method is more memory efficient, which is demonstrated by complexity analysis and performance tests. A preprocessing stage is also devised to reduce computational cost via a decomposition of the specification. We show that the proposed method can effectively solve real-world control problems, such as jet engine compressor control and motion planning for manipulators and mobile robots.

Multiple Mobile Robot Task and Motion Planning: A Survey

With recent advances in mobile robotics, autonomous systems, and artificial intelligence, there is a growing expectation that robots are able to solve complex problems. Many of these problems require multiple robots working cooperatively in a multi-robot system. Complex tasks may also include the interconnection of task-level specifications with robot motion-level constraints. Many recent works in the literature use multiple mobile robots to solve these complex tasks by integrating task and motion planning. We survey recent contributions to the field of combined task and motion planning for multiple mobile robots by categorizing works based on their underlying problem representations, and we identify possible directions for future research. We propose a taxonomy for task and motion planning based on system capabilities, applicable to multi-robot and single-robot systems.

D$$^2$$WA: “dynamic” DWA for motion planning of mobile robots in dynamic environments

D$$^2$$WA: “dynamic” DWA for motion planning of mobile robots in dynamic environments

Motion Planning and Cooperative Manipulation for Mobile Robots With Dual Arms

With large work space and dexterous manipulation capability, mobile robots with dual arms can be used in complex operation scenes such as active detection, disassembly, spraying, cleaning and assembly, is a promising direction for robot development. In this paper, a hybrid control approach is proposed for a mobile robot with dual arms to fulfil an explosive disposal task. Firstly, the motion planning for mobile base and trajectory planning for dual arms are developed, and a hierarchical task planner is designed using the finite state machine to make the robot system to deal with events sensed by active vision. A rapidly-exploring random tree-based motion planning is developed in task space for the mobile base to realize dynamic collision avoidance, while the trajectory planning approach is proposed in the framework of a dual-arm master-slave coordinated mechanism. Next, for mobile robots to complete complex task such as explosive disposal using two dexterous hands, the impedance control approach with slippage detection is developed by considering slippage tendency and slippage intensity to produce a stable in-hand manipulation. Furthermore, the Faster R-CNN is employed to determine the grasping region for robot manipulation through object detection and learning. Finally, the explosive disposal scene is designed to justify the effectiveness and good performance of the proposed methods.

A new method on motion planning for mobile robots using jump point search and Bezier curves

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on motion planning systems that meet the shortest path and obstacle avoidance requirements. This article proposes a novel path planning algorithm based on jump point search and Bezier curves. The proposed algorithm consists of two main steps. In the front end, the improved heuristic function based on distance and direction is used to reduce the cost, and the redundant turning points are trimmed. In the back end, a novel trajectory generation method based on Bezier curves and a straight line is proposed. Our experimental results indicate that the proposed algorithm provides a complete motion planning solution from the front end to the back end, which can realize an optimal trajectory from the initial point to the target point used for robot navigation.

A Decision Algorithm for Motion Planning of Car-Like Robots in Dynamic Environments

Motion planning in dynamic environments is essential for many applications such as in search and rescue missions and in servicing tasks. In this paper, I present a new approach for motion planning for an autonomous mobile robot which is requested to operate in a dynamic environment. The robot’s working environment is cluttered with static obstacles with a priori knowledge of their shapes and positions and with moving obstacles with unknown geometry and motion. In order to ensure a safe motion for the robot I propose a two-stage approach. First, using the Bump-Surface concept I construct a path by considering only the static obstacles of the environment. Then, the robot starts its motion on the given path. When the robot detects a potential danger situation (i.e., collision), a decision algorithm tries to evaluate the risk of collision and simultaneously to find the safest action for the robot. The proposed approach is evaluated in randomly generated simulated scenarios.

Multi-Resolution 3D Mapping With Explicit Free Space Representation for Fast and Accurate Mobile Robot Motion Planning

With the aim of bridging the gap between high quality reconstruction and robot motion planning, we propose an efficient system that leverages the concept of adaptive-resolution volumetric mapping, which naturally integrates with the hierarchical decomposition of space in an octree data structure. Instead of a Truncated Signed Distance Function (TSDF), we adopt mapping of occupancy probabilities in log-odds representation, which allows to represent both surfaces, as well as the entire free, i.e. observed space, as opposed to unobserved space. We introduce a method for choosing resolution -on the fly- in real-time by means of a multi-scale max-min pooling of the input depth image. The notion of explicit free space mapping paired with the spatial hierarchy in the data structure, as well as map resolution, allows for collision queries, as needed for robot motion planning, at unprecedented speed. We quantitatively evaluate mapping accuracy, memory, runtime performance, and planning performance showing improvements over the state of the art, particularly in cases requiring high resolution maps.

Motion Planning and Control of an Omnidirectional Mobile Robot in Dynamic Environments

Motion control in dynamic environments is one of the most important problems in using mobile robots in collaboration with humans and other robots. In this paper, the motion control of a four-Mecanum-wheeled omnidirectional mobile robot (OMR) in dynamic environments is studied. The robot’s differential equations of motion are extracted using Kane’s method and converted to discrete state space form. A nonlinear model predictive control (NMPC) strategy is designed based on the derived mathematical model to stabilize the robot in desired positions and orientations. As a main contribution of this work, the velocity obstacles (VO) approach is reformulated to be introduced in the NMPC system to avoid the robot from collision with moving and fixed obstacles online. Considering the robot’s physical restrictions, the parameters and functions used in the designed control system and collision avoidance strategy are determined through stability and performance analysis and some criteria are established for calculating the best values of these parameters. The effectiveness of the proposed controller and collision avoidance strategy is evaluated through a series of computer simulations. The simulation results show that the proposed strategy is efficient in stabilizing the robot in the desired configuration and in avoiding collision with obstacles, even in narrow spaces and with complicated arrangements of obstacles.