Motion Planning Research Articles

GPU-Enabled Parallel Trajectory Optimization Framework for Safe Motion Planning of Autonomous Vehicles

GPU-Enabled Parallel Trajectory Optimization Framework for Safe Motion Planning of Autonomous Vehicles

Global reorientation of a free-fall multibody system using periodical joint motions – theory and motion planning

Global reorientation of a free-fall multibody system using periodical joint motions – theory and motion planning

Editorial for Special Issue of Motion Planning and Advanced Control for Robotics

Advancements in robotics are increasingly essential in addressing complex challenges associated with motion planning and control, particularly in environments that require physical interaction with various elements [...]

Deep reinforcement learning augmented decision model for intelligent driving cars

In view of the fact that the state machine decision model cannot effectively handle the rich contextual information and the influence of uncertain factors in ice and snow environments, a deep reinforcement learning agent based on the deep Q network algorithm (DQN) was constructed. The motion planner was used to augment the agent, and the rule-based decision planning module and the deep reinforcement learning model were integrated to establish the DQN-planner model, thereby improving the convergence speed and driving ability of the reinforcement learning agent. Finally, based on the CARLA simulation platform, a comparative experiment was conducted on the driving ability of the DQN model and the DQN-planner model on low-adhesion ice and snow roads, and the training process and verification results were analyzed respectively.

Dynamic event-triggered integrated task and motion planning for process-aware source seeking

The process-aware source seeking (PASS) problem in flow fields aims to find an informative trajectory to reach an unknown source location while taking the energy consumption in the flow fields into consideration. Taking advantage of the dynamic flow field partition technique, this paper formulates this problem as a task and motion planning (TAMP) problem and proposes a bi-level hierarchical planning framework to decouple the planning of inter-region transition and inner-region trajectory by introducing inter-region junctions. An integrated strategy is developed to enable efficient upper-level planning by investigating the optimal solution of the lower-level planner. In order to leverage the information acquisition and computational burden, a dynamic event-triggered mechanism is introduced to enable asynchronized estimation, region partitioning and re-plans. The proposed algorithm provides guaranteed convergence of the trajectory, and achieves automatic trade-offs of both exploration-exploitation and accuracy-efficiency. Simulations in a highly complicated and realistic ocean surface flow field validate the merits of the proposed algorithm, which demonstrates a significant reduction in computational burden without compromising planning optimality.

Trajectory optimization and obstacle avoidance of autonomous robot using Robust and Efficient Rapidly Exploring Random Tree.

One of the key challenges in robotics is the motion planning problem. This paper presents a local trajectory planning and obstacle avoidance strategy based on a novel sampling-based path-finding algorithm designed for autonomous vehicles navigating complex environments. Although sampling-based algorithms have been extensively employed for motion planning, they have notable limitations, such as sluggish convergence rate, significant search time volatility, a vast, dense sample space, and unsmooth search routes. To overcome the limitations, including slow convergence, high computational complexity, and unnecessary search while sampling the whole space, we have proposed the RE-RRT* (Robust and Efficient RRT*) algorithm. This algorithm adapts a new sampling-based path-finding algorithm based on sampling along the displacement from the initial point to the goal point. The sample space is constrained during each stage of the random tree's growth, reducing the number of redundant searches. The RE-RRT* algorithm can converge to a shorter path with fewer iterations. Furthermore, the Choose Parent and Rewire processes are used by RE-RRT* to improve the path in succeeding cycles continuously. Extensive experiments under diverse obstacle settings are performed to validate the effectiveness of the proposed approach. The results demonstrate that the proposed approach outperforms existing methods in terms of computational time, sampling space efficiency, speed, and stability.

See the Unseen: Grid-Wise Drivable Area Detection Dataset and Network Using LiDAR

Drivable Area (DA) detection is crucial for autonomous driving. Camera-based methods rely heavily on illumination conditions and often fail to capture accurate 3D information, while LiDAR-based methods offer accurate 3D data and are less susceptible to illumination conditions. However, existing LiDAR-based methods focus on point-wise detection, so are prone to occlusion and limited by point cloud sparsity, which leads to decreased performance in motion planning and localization. We propose Argoverse-grid, a grid-wise DA detection dataset derived from Argoverse 1, comprising over 20K frames with fine-grained BEV DA labels across various scenarios. We also introduce Grid-DATrNet, a first grid-wise DA detection model utilizing global attention through transformers. Our experiments demonstrate the superiority of Grid-DATrNet over various methods, including both LiDAR and camera-based approaches, in detecting grid-wise DA on the proposed Argoverse-grid dataset. Grid-DATrNet achieves state-of-the-art results with an accuracy of 93.28% and an F1-score of 0.8328. We show that Grid-DATrNet can detect grids even in occluded and unmeasured areas by leveraging contextual and semantic information through global attention, unlike CNN-based DA detection methods. The preprocessing code for Argoverse-grid, experiment code, Grid-DATrNet implementation, and result visualization code are available at AVE Laboratory official git hub.

Energy-Aware Hierarchical Control of Joint Velocities

Nowadays, robots are applied in dynamic environments. For a robust operation, the motion planning module must consider other tasks besides reaching a specified pose: (self) collision avoidance, joint limit avoidance, keeping an advantageous configuration, etc. Each task demands different joint control commands, which may counteract each other. We present a hierarchical control that, depending on the robot and environment state, determines online a suitable priority among those tasks. Thereby, the control command of a lower-prioritized task never hinders the control command of a higher-prioritized task. We ensure smooth control signals also during priority rearrangement. Our hierarchical control computes reference joint velocities. However, the underlying concepts of hierarchical control differ when using joint accelerations or joint torques as control signals instead. So, as a further contribution, we provide a comprehensive discussion on how joint velocity control, joint acceleration control, and joint torque control differ in hierarchical task control. We validate our formulation in an experiment on hardware.

A parallel mechanism-based virtual biomechanical shoulder robot model: Mechanism design optimization and motion planning

This paper presents the design and optimization process of a Virtual Biomechanical Shoulder Robot Model (VBSRM) based on a 6-4 parallel mechanism. To address the challenges posed by parallel manipulators and the specific biomechanical constraints of the shoulder joint, a comprehensive design analysis is conducted. First, a kinematic model of the VBSRM is developed, followed by an investigation of its geometric model. To obtain an optimal VBSRM design, performance objectives such as condition number, norm of actuator force, and stiffness are identified and optimized. Initially, only condition number of the robot mechanism is optimized using Genetic Algorithm and performance objectives from the optimal design are analyzed. Later, the three objectives are grouped to form a single function and a single objective-based optimization is also conducted. However, further investigation revealed the conflicting nature of the objectives and hence these were simultaneously optimized using the Non-dominated Sorting Genetic Algorithm (NSGA II). The results obtained from various optimization routines are compared and it is found that the results from the NSGA II provide a better tradeoff between the performance objectives. The motion trajectories from the optimal design of the VBSRM are later analyzed vis-à-vis human shoulder motions for its intended use as a robotic model of the human shoulder joint in various applications.

A comprehensive survey of space robotic manipulators for on-orbit servicing.

On-Orbit Servicing (OOS) robots are transforming space exploration by enabling vital maintenance and repair of spacecraft directly in space. However, achieving precise and safe manipulation in microgravity necessitates overcoming significant challenges. This survey delves into four crucial areas essential for successful OOS manipulation: object state estimation, motion planning, and feedback control. Techniques from traditional vision to advanced X-ray and neural network methods are explored for object state estimation. Strategies for fuel-optimized trajectories, docking maneuvers, and collision avoidance are examined in motion planning. The survey also explores control methods for various scenarios, including cooperative manipulation and handling uncertainties, in feedback control. Additionally, this survey examines how Machine learning techniques can further propel OOS robots towards more complex and delicate tasks in space.

PartwiseMPC: Interactive Control of Contact‐Guided Motions

AbstractPhysics‐based character motions remain difficult to create and control. We make two contributions towards simpler specification and faster generation of physics‐based control. First, we introduce a novel partwise model predictive control (MPC) method that exploits independent planning for body parts when this proves beneficial, while defaulting to whole‐body motion planning when that proves to be more effective. Second, we introduce a new approach to motion specification, based on specifying an ordered set of contact keyframes. These each specify a small number of pairwise contacts between the body and the environment, and serve as loose specifications of motion strategies. Unlike regular keyframes or traditional trajectory optimization constraints, they are heavily under‐constrained and have flexible timing. We demonstrate a range of challenging contact‐rich motions that can be generated online at interactive rates using this framework. We further show the generalization capabilities of the method.

Trajectory optimization and stability control for a novel unmanned intelligent wheel-legged vehicles on unstructured terrains

The trend of intelligent vehicles is currently expanding, and a new class of unmanned intelligent vehicles known as wheel-legged vehicles (WLVs) is emerging. WLVs excel in transportation on unstructured terrain by offering a unique combination of the efficiency of wheels on flat ground and the versatility of legs to tackle obstacles. To enhance the locomotion performance of WLVs on unstructured terrains, this paper presents a novel framework for improving their locomotion through nonlinear programming-based (NLP) trajectory optimization and stability control. The framework optimizes the vehicle’s body and wheel positioning while incorporating terrain information and employs a linear rigid body dynamic model for efficient motion planning. The stability control framework combines feedforward control using ground reaction forces with feedback control through joint PD control and utilizes model predictive control (MPC) to adjust the wheel slip ratio to prevent slip on steep slopes. Experimental validation on the real vehicle with torque-controlled wheels demonstrated the capability of driving over a 1 m height with a 30°slope at an average speed of 0.7 m/s and a maximum speed of 1.03 m/s. Our approach also enables the WLV to overcome obstacles, such as inclines, while dynamically negotiating these challenging terrains.

A survey of 3D Space Path-Planning Methods and Algorithms

Due to their agility, cost-effectiveness, and high maneuverability, Unmanned Aerial Vehicles (UAVs) have attracted considerable attention from researchers and investors alike. Path planning is one of the practical subsets of motion planning for UAVs. It prevents collisions and ensures complete coverage of an area. This study provides a structured review of applicable algorithms and coverage path planning solutions in Three-Dimensional (3D) space, presenting state-of-the-art technologies related to heuristic decomposition approaches for UAVs and the forefront challenges. Additionally, it introduces a comprehensive and novel classification of practical methods and representational techniques for path-planning algorithms. This depends on environmental characteristics and optimal parameters in the real world. The first category presents a classification of semi-accurate decomposition approaches as the most practical decomposition method, along with the data structure of these practices, categorized by phases. The second category illustrates path-planning processes based on symbolic techniques in 3D space. Additionally, it provides a critical analysis of crucial influential approaches based on their importance in path quality and researchers’ attention, highlighting their limitations and research gaps. Furthermore, it will provide the most pertinent recommendations for future work for researchers. The studies demonstrate an apparent inclination among experimenters toward using the semi-accurate cellular decomposition approach to improve 3D path planning.

Kineto-static analysis of a hybrid manipulator consisting of rigid and flexible limbs with locking function for planar shape morphing

The incorporation of lockable passive backbones into active compliant morphing systems efficiently results in lightweight, high-load, and large deformation systems. However, there exist challenges in kineto-static analysis due to the interaction between rigid reconfigurable kinematic constraints and the nonlinear deformation of actuated flexible limbs. This paper addresses these issues by developing a kineto-static method to analyze the motion in a novel planar 3-DOF shape-morphing manipulator. The manipulator features two actuated flexible limbs with a lockable variable geometry truss (LVGT). In this study, two isostatic topologies are selected for reconfigurable motion control under external tip loads. A multi-step sequential control strategy is proposed to maneuver the manipulator's platform for desired poses. Then, a constrained-trajectory-based kinematic model is proposed for an inverse kinematic solution considering motion planning. Subsequently, a kineto-static model is introduced, considering constraints from rigid and flexible limbs, aiming to distribute distributing redundant actuation forces. Finally, nonlinear finite element analysis (FEA) and experiments are carried out to validate the effectiveness of the proposed method.

Identification of physically consistent dynamics parameter of the ABB IRB 360-6/1600 delta robot and its use for time-optimal motion planning under consideration of constraint forces

Model-based control schemes, forward dynamics simulations, constraint force computation and time-optimal motion planning have one major thing in common, they all depend on the dynamics parameters of the system. Physical consistency of the dynamics parameters ensures a positive definite mass matrix and correct constraint forces. The most common inverse dynamics identification method – the base-parameters – lack physical consistency. This paper proposes an identification method to identify physically consistent dynamics parameters for Delta-like robots while further showing the effects of friction in passive joints. A tailored model to compute the crucial constraint forces appearing in the mechanism based on the identified dynamics parameters is derived. This model is used to additionally consider constraint forces besides actuation torques for time-optimal motion planning of a typical pick and place task. This is done without any prior CAD data of the robot from the manufacturer.

Distributed Multi-Vehicle Task Assignment and Motion Planning in Dense Environments

Distributed Multi-Vehicle Task Assignment and Motion Planning in Dense Environments

Transformer-Enhanced Motion Planner: Attention-Guided Sampling for State-Specific Decision Making

Transformer-Enhanced Motion Planner: Attention-Guided Sampling for State-Specific Decision Making

Dynamic Voxels Based on Ego-Conditioned Prediction: An Integrated Spatio-Temporal Framework for Motion Planning

Dynamic Voxels Based on Ego-Conditioned Prediction: An Integrated Spatio-Temporal Framework for Motion Planning

Spatio-Temporal Corridor-Based Motion Planning of Lane Change Maneuver for Autonomous Driving in Multi-Vehicle Traffic

Spatio-Temporal Corridor-Based Motion Planning of Lane Change Maneuver for Autonomous Driving in Multi-Vehicle Traffic

Safe Motion Planning and Control for Mobile Robots: A Survey

Safe Motion Planning and Control for Mobile Robots: A Survey