Safe Trajectory Research Articles

Enhanced USV path planning through integrated Bi-RRT and DWA algorithms considering environmental factors

This paper presents a novel hybrid dynamic path planning algorithm termed GIBi-RRT-IDWA, which enhances global path planning by amalgamating the Bi-RRT algorithm with the local path planning capabilities of the refined DWA algorithm. Through enhancements in node expansion techniques, Improvements in Tree Expansion to eliminate redundancy, and improvements in Sample Point Generation to augment the Bi-RRT algorithm, the algorithm achieves heightened search efficiency and path security, ultimately leading to the derivation of globally optimal trajectories. The motion model for USV is refined to encompass environmental factors such as winds and currents, pertinent to marine contexts. Furthermore, the evaluation function of the DWA algorithm undergoes enhancements, incorporating elements from heuristic functions within the A* algorithm and radar-based assessment of search range. These improvements facilitate expedited Goal point tracking and fortify the USV's capability to navigate around hazards. Experimental findings corroborate the efficacy of the GIBi-RRT-IDWA algorithm, showcasing its superiority over conventional DWA algorithm and IDWA algorithm. Notably, the algorithm demonstrates prowess in generating shorter and safer trajectories, adeptly navigating through intricate and dynamic waterside environments. Consequently, it improves path planning strategies' adaptability and resilience, allowing USVs to navigate more safely and efficiently.

Automatic evaluation of planners in dynamic scenes during human–robot interaction

AbstractThe evaluation of planners is essential to guarantee the efficiency and performance of trajectories executed by collaborative robots in human–robot interaction (HRI) contexts. The main objective of this research is to develop a method that allows obtaining an automated assessment of the performance of planners widely used in robotic environments. In addition to analyzing specific metrics such as planning time, path length, smoothness, and clearance, this paper introduces novel contributions, including a unique method for dynamic scene evaluation and a new comprehensive and global performance index, named Total Performance Index (TPIx), which encompasses all the aforementioned metrics. The system proposed is composed of a collaborative robot and an RGB-D sensor to monitor the environment using 3D data to perform safe trajectories during multiple tasks. In this quantitative evaluation, two single-query planners (RRT, KPIECE) and two multiple-query planners (SPARS, PRM) were analyzed. The results obtained demonstrate the feasibility and effectiveness of the proposed method to automatically evaluate the planners used by collaborative robots. Therefore, this study contributes to the advancement of collaborative robotics by introducing updated methods for planner evaluation and aims to facilitate the development of more efficient and adaptive systems in light of recent advancements in robotic technologies.

The development of algorithms for safe control of an autonomous ship

The paper presents the results of a research on the development of algorithms for ship’s safe ship trajectory calculation and automatic control of the ship along the determined trajectory. These methods are intended for use in the autonomous navigation system of an Unmanned Surface Vehicle (USV) or a Maritime Autonomous Surface Ship (MASS). The work presents the consecutive stages of a research that must be carried out in order to develop a system that will perform the task of multidimensional ship control in a port. The safe trajectory is calculated with the use of the Ant Colony Optimization (ACO) method. The ship motion control is based on the Linear Matrix Inequalities (LMI) method, implemented using the Mathworks and Yalmip libraries. The results of controlling the ship’s motion in accordance with the calculated trajectory are presented in the paper. With the use of the developed system, the ship can move autonomously based on the information from the DGPS system and a gyrocompass. The presented results concerned a computer simulation of maneuvers in the port of the Blue Lady ship from the Foundation for Shipping Safety and Environmental Protection.

SOMTP: A Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics

SOMTP: A Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics

Method for Bottle Opening with a Dual-Arm Robot.

This paper introduces a novel approach to robotic assistance in bottle opening using the dual-arm robot TIAGo++. The solution enhances accessibility by addressing the needs of individuals with injuries or disabilities who may require help with common manipulation tasks. The aim of this paper is to propose a method involving vision, manipulation, and learning techniques to effectively address the task of bottle opening. The process begins with the acquisition of bottle and cap positions using an RGB-D camera and computer vision. Subsequently, the robot picks the bottle with one gripper and grips the cap with the other, each by planning safe trajectories. Then, the opening procedure is executed via a position and force control scheme that ensures both grippers follow the unscrewing path defined by the cap thread. Within the control loop, force sensor information is employed to control the vertical axis movements, while gripper rotation control is achieved through a Deep Reinforcement Learning (DRL) algorithm trained to determine the optimal angle increments for rotation. The results demonstrate the successful training of the learning agent. The experiments confirm the effectiveness of the proposed method in bottle opening with the TIAGo++ robot, showcasing the practical viability of the approach.

Autonomous Vehicles Traversability Mapping Fusing Semantic–Geometric in Off-Road Navigation

This paper proposes an evaluating and mapping methodology of terrain traversability for off-road navigation of autonomous vehicles in unstructured environments. Terrain features are extracted from RGB images and 3D point clouds to create a traversal cost map. The cost map is then employed to plan safe trajectories. Bayesian generalized kernel inference is employed to assess unknown grid attributes due to the sparse raw point cloud data. A Kalman filter also creates density local elevation maps in real time by fusing multiframe information. Consequently, the terrain semantic mapping procedure considers the uncertainty of semantic segmentation and the impact of sensor noise. A Bayesian filter is used to update the surface semantic information in a probabilistic manner. Ultimately, the elevation map is utilized to extract geometric characteristics, which are then integrated with the probabilistic semantic map. This combined map is then used in conjunction with the extended motion primitive planner to plan the most effective trajectory. The experimental results demonstrate that the autonomous vehicles obtain a success rate enhancement ranging from 4.4% to 13.6% and a decrease in trajectory roughness ranging from 5.1% to 35.8% when compared with the most developed outdoor navigation algorithms. Additionally, the autonomous vehicles maintain a terrain surface selection accuracy of over 85% during the navigation process.

Leveraging Cooperative Intent and Actuator Constraints for Safe Trajectory Planning of Autonomous Vehicles in Uncertain Traffic Scenarios

This study explores the integration of dynamic vehicle trajectories, vehicle safety factors, static traffic environments, and actuator constraints to improve cooperative intent modeling for autonomous vehicles (AVs) navigating uncertain traffic scenarios. Existing models often focus solely on interactions between dynamic trajectories, limiting their ability to fully interpret the intentions of surrounding vehicles. To address this limitation, we present a more comprehensive approach using the Cooperative Intent Multi-Layer Graph Neural Network (CMGNN) model. The CMGNN analyzes not only the dynamic trajectories but also the lane position relationships, vehicle angle changes, and actuator constraints and performs group interaction analysis. This richer information allows the CMGNN to more accurately capture the cooperative intent and better understand the surrounding vehicle behavior. This study investigated the impact of the CMGNN in the Carla simulator on surrounding vehicle trajectory prediction and AV safe trajectory planning. An innovative mechanism for dynamic trajectory risk assessment is introduced, which takes into account the constraints of the actuators when evaluating trajectory planning metrics. The results show that incorporating cooperative intent and considering the actuator limitations enhanced the CMGNN’s safety and driving efficiency in uncertain scenarios, significantly reducing the probability of AVs colliding. This is achieved as the model dynamically adapts its driving strategy based on the real-time traffic conditions, the perceived intentions of the surrounding vehicles, and the physical constraints of the vehicle actuators.

Safe Trajectory Planning for Incremental Robots Based on a Spatiotemporal Variable-Step-Size A* Algorithm.

In this paper, a planning method based on the spatiotemporal variable-step-size A* algorithm is proposed to address the problem of safe trajectory planning for incremental, wheeled, mobile robots in complex motion scenarios with multiple robots. After constructing the known conditions, the spatiotemporal variable-step-size A* algorithm is first used to perform a collision-avoiding initial spatiotemporal trajectory search, and a variable time step is utilized to ensure that the robot completes the search at the target speed. Subsequently, the trajectory is instantiated using B-spline curves in a numerical optimization considering constraints to generate the final smooth trajectory. The results of simulation tests in a field-shaped, complex, dynamic scenario show that the proposed trajectory planning method is more applicable, and the results indicate higher efficiency compared to the traditional method in the incremental robot trajectory planning problem.

Control strategy for trading off solar power and control input while rendezvous and docking

This paper presents a novel strategy for decoupling the attitude and orbit equations of a CubeSat for rendezvous and docking with an uncontrollable cooperative target. For computing a safe trajectory, the proposed control algorithm takes into account the solar energy received by the CubeSat. The CubeSat is equipped with a thruster for orbit maneuvers, magnetic coils for attitude control, and four fixed single-sided rectangular solar panels. The coupled dynamics arising from the dynamical model of the spacecraft is solved using a novel weighted vector-based approach. This paper presents a linearized nonlinear optimal control problem arising in small spacecrafts while trying to maximize solar energy input in the rendezvous and docking process. An intermediate orbit is defined and used to divide the problem into two different optimal control problems: rendezvous optimization and docking optimization problems. A geometrical approach based on the shape of the chaser and the target is contemplated for collision avoidance while docking. The proposed controller design is modified whenever the sunlight is obstructed by the Earth, and the maneuvering controls are redesigned accordingly for optimal rendezvous and docking. Numerical simulations have been carried out to show the efficacy of the proposed concept for steering the trade-off between solar energy and control input during the rendezvous and docking of CubeSat with a tumbling target.

Trajectory planning and control of spacecraft avoiding dynamic debris swarm

The continuous accumulation of space debris poses a significant threat to spacecraft on orbit. This paper introduces a dynamic trajectory planning and control framework aimed at ensuring the safe traversal of spacecraft through space debris swarms. Initially, a threat model is established, considering the movement of the debris swarm and the constraints of spacecraft detection range. This model utilizes a dynamically updated, redefined Gaussian mixture model to characterize the threat posed by the debris swarm to spacecraft, with proximity to debris correlating to increased threat levels. Building upon this threat model, four guidance rules are defined, including target guidance, minimal threat, threat threshold, and local path optimization, to facilitate the generation of the shortest collision-free local trajectory for spacecraft. Combining the concept of receding horizon control, an online real-time planning and control framework is formulated for spacecraft navigating through debris swarms. Finally, numerical and semi-physical simulations verify that the proposed framework can stably generate spacecraft safe trajectories and control commands under the threat of numerous dynamic space debris, showing the characteristics of optimal planning trajectory, rapid computation speed and robust safety features.

Safe Maneuvering Planning for Flights in Complex Environments

Safe trajectory planning of unmanned aerial vehicles (UAVs) has drawn significant attention. For avoiding obstacles, the two prevailing methods of distance penalty and convex polyhedra confinement ignore the flight velocity, aggressive maneuvering increases the risk of collision. Other approaches ensure safety by limiting the flight speed, leading to conservative and slow trajectories. This paper presents a motion planning method that ensures flexible maneuverability and a high level of UAV safety in unknown complex environments. Firstly, an improved path planning method considering safety and search efficiency is proposed to generate discrete primitives. By representing the confident flight area with ball-shaped areas expected no obstacles included, the safe region constraint is designed and incorporated into the first-stage optimization with the low-order kinematic model. Subsequently, an agile maneuvering approach with environmental adaptability is integrated into the second-stage optimization with the high-order kinematic model. This approach can effectively adjust the agile flight according to the obstacle distribution and motion velocity. Simulations and real-world experiments are conducted to demonstrate the robustness and effectiveness of the presented approach. Moreover, benchmark comparison manifests that the proposed method outperforms the cutting-edge methods from the aspects of the perception uncertainty adaptation and flight safety.

Multi-Agent target allocation and safe trajectory planning for artificial pollination tasks

This paper presents an autonomous multi-agent drone system utilized for an artificial pollination task to address the declining bee population and its impact on natural pollination. The main goal is to visit all the targets (flowers) by drones. The system receives the position of the targets located on a vertical fruiting wall from a single sensor mounted on an unmanned ground vehicle (UGV). Then, it determines the sequence of target allocation to minimize the task time and to minimize the accumulated distance each drone travels. The limited payload capacity of each drone and the need to refill stock carried by the UGV are also considered. After allocating the targets, a safe trajectory is generated for each drone to avoid collision with other drones, considering uncertainty in the drones’ positions. When a drone reaches a target, the system allocates a new target, and the cycle repeats until the task is completed. The system is reactive to the evolution of the task and adjusts accordingly. An additional tested factor is the impact of the positioning error on the task time. The system was evaluated through dynamic simulations using real datasets of peach and pear orchards and finally in a laboratory proof-of-concept experiment using a micro-drone system.

Trajectory Planning and Singularity Avoidance Algorithm for Robotic Arm Obstacle Avoidance Based on an Improved Fast Marching Tree

The quest for efficient and safe trajectory planning in robotic manipulation poses significant challenges, particularly in complex obstacle environments where the risk of encountering singularities and obstacles is high. Addressing this critical issue, our study presents a novel enhancement of the Fast Marching Tree (FMT) algorithm, ingeniously designed to navigate the complex terrain of Cartesian space with an unprecedented level of finesse. At the heart of our approach lies a sophisticated two-stage path point sampling strategy, ingeniously coupled with a singularity avoidance mechanism that leverages geometric perception to assess and mitigate the risk of encountering problematic configurations. This innovative method not only facilitates seamless obstacle navigation but also adeptly circumvents the perilous zones of singularity, ensuring a smooth and uninterrupted path for the robotic arm. To further refine the trajectory, we incorporate a quasi-uniform cubic B-spline curve, optimizing the path for both efficiency and smoothness. Our comprehensive simulation experiments underscore the superiority of our algorithm, showcasing its ability to consistently achieve shorter, more efficient paths while steadfastly avoiding obstacles and singularities. The practical applicability of our method is further corroborated through successful implementation in real-world robotic arm trajectory planning scenarios, highlighting its potential to revolutionize the field with its robustness and adaptability.

Safe and Scalable Real-Time Trajectory Planning Framework for Urban Air Mobility

This paper presents a real-time trajectory planning framework for urban air mobility (UAM) that is both safe and scalable. The proposed framework employs a decentralized, free-flight concept of operation in which each aircraft independently performs separation assurance and conflict resolution, generating safe trajectories by accounting for the future states of nearby aircraft. The framework consists of two main components: a data-driven reachability analysis tool and an efficient Markov-decision-process-based decision maker. The reachability analysis overapproximates the reachable set of each aircraft through a discrepancy function learned online from simulated trajectories. The decision maker, on the other hand, uses a 6-degree-of-freedom guidance model of fixed-wing aircraft to ensure collision-free trajectory planning. Additionally, the proposed framework incorporates reward shaping and action shielding techniques to enhance safety performance. The proposed framework was evaluated through simulation experiments involving up to 32 aircraft in a generic city-scale area with a 15 km radius, with performance measured by the number of near-midair collisions (NMAC) and computational time. The results demonstrate the planner’s ability to generate safe trajectories for the aircraft in polynomial time, showing its scalability. Moreover, the action shielding and reward shaping strategies show up to a 78.71 and 85.14% reduction in NMAC compared to the baseline planner, respectively.

Stereotactic frame-based biopsy of infratentorial lesions via the suboccipital-transcerebellar approach with the Zamorano-Duchovny stereotactic system—a retrospective analysis of 79 consecutive cases

ObjectiveLesions of the posterior fossa (brainstem and cerebellum) are challenging in diagnosis and treatment due to the fact that they are often located eloquently and total resection is rarely possible. Therefore, frame-based stereotactic biopsies are commonly used to asservate tissue for neuropathological diagnosis and further treatment determination. The aim of our study was to assess the safety and diagnostic success rate of frame-based stereotactic biopsies for lesions in the posterior fossa via the suboccipital-transcerebellar approach.MethodsWe performed a retrospective database analysis of all frame-based stereotactic biopsy cases at our institution since 2007. The aim was to identify all surgical cases for infratentorial lesion biopsies via the suboccipital-transcerebellar approach. We collected clinical data regarding outcomes, complications, diagnostic success, radiological appearances, and stereotactic trajectories.ResultsA total of n = 79 cases of stereotactic biopsies for posterior fossa lesions via the suboccipital-transcerebellar approach (41 female and 38 male) utilizing the Zamorano-Duchovny stereotactic system were identified. The mean age at the time of surgery was 42.5 years (± 23.3; range, 1–87 years). All patients were operated with intraoperative stereotactic imaging (n = 62 MRI, n = 17 CT). The absolute diagnostic success rate was 87.3%. The most common diagnoses were glioma, lymphoma, and inflammatory disease. The overall complication rate was 8.7% (seven cases). All patients with complications showed new neurological deficits; of those, three were permanent. Hemorrhage was detected in five of the cases having complications. The 30-day mortality rate was 7.6%, and 1-year survival rate was 70%.ConclusionsOur data suggests that frame-based stereotactic biopsies with the Zamorano-Duchovny stereotactic system via the suboccipital-transcerebellar approach are safe and reliable for infratentorial lesions bearing a high diagnostic yield and an acceptable complication rate. Further research should focus on the planning of safe trajectories and a careful case selection with the goal of minimizing complications and maximizing diagnostic success.

Maximum Principle in Autonomous Multi-Object Safe Trajectory Optimization

The following article presents the task of optimizing the control of an autonomous object within a group of other passing objects using Pontryagin’s bounded maximum principle. The basis of this principle is a multidimensional nonlinear model of the control process, with state constraints reflecting the motion of passing objects. The analytical synthesis of optimal multi-object control became the basis for the algorithm for determining the optimal and safe object trajectory. Simulation tests of the algorithm on the example of real navigation situations with various numbers of objects illustrate their safe trajectories in changing environmental conditions. The optimal object trajectory obtained using Pontryagin’s maximum principle was compared with the trajectory calculated using the Bellman dynamic programming method. The analysis of the research allowed for the formulation of valuable conclusions and a plan for further research in the field of autonomous vehicle control optimization. The maximum principle algorithm allows one to take into account a larger number of objects whose data are derived from ARPA anti-collision radar systems.

Vessel intrusion interception utilising unmanned surface vehicles for offshore wind farm asset protection

Remotely located offshore wind farms (OWFs) are usually in unmanned operation, and are severely at risk from accidental or deliberate man-made damages, such as cable faults caused by vessel anchoring. In this work, a proactive alarming and interception method utilising unmanned surface vehicles (USV) is proposed to prevent suspect vessels from intruding OWF areas and damaging the assets. More specifically, the vessel intrusion interception system keeps monitoring the motion of surrounding vessels and assessing their potential intrusive risks. When a suspect intruder is identified, USV will be automatically deployed to proactively intercept the target vessel with an alarming and expelling system. In particular, a risk assessment model based on the analytic hierarchy process (AHP) and entropy weight method (EWM) is established to predict the potential intrusive risk, and an improved fast marching square (FMS) algorithm is designed for USV path planning to guarantee dynamic and reliable vessel interception. In order to verify the effectiveness of the proposed method, case studies have been conducted for the Zhejiang Jiaxing 2 OWF with authentic vessel traffic data. Results indicate that the proposed risk assessment model based on AHP-EMW is able to correctly predict the potential intrusion risk in various scenarios, and the improved FMS path planning algorithm with multi-objective optimisation is capable of providing both efficient and safe trajectories for USV interception.

Resilient Time-Varying Formation Tracking for Heterogeneous High-Order Multiagent Systems With Multiple Dynamic Leaders

This paper investigates time-varying output formation tracking problems for heterogeneous multiagent systems in an adversarial environment, where both leaders and followers are vulnerable to cyber-attacks. Despite the presence of adversarial agents, the normal followers not suffering from cyber-attacks seek to achieve a desired for-mation configuration and track a safe trajectory generated by normal leaders' outputs simultaneously. To this end, a resilient time-varying output formation (RTVOF) tracking scheme is presented in this work. Firstly, to counteract the effect of adversarial agents, a safety strategy is proposed for general linear heterogeneous systems, which is a syn-thesis of the mean-subsequence-reduced family algorithms and an attack detection procedure. Secondly, using the neighboring interaction, a resilient distributed observer and a local formation controller are designed for each normal follower to estimate the state and track the output of the safe formation reference. Then, it is proved that the hetero-geneous multiagent system with multiple dynamic leaders and time-varying topologies can accomplish the RTVOF tracking if specific graph-theoretic properties and forma-tion feasibility conditions are satisfied. Finally, simulation examples are given to demonstrate the effectiveness of obtained results.

Computational Intelligence Supporting the Safe Control of Autonomous Multi-Objects

The essence of this work, which is an extension of the author’s previous research, is an analysis of computational intelligence algorithms that the support safe control of an autonomous object moving in a large group of other autonomous objects. Linear and dynamic programming methods with neural constraints on the process state, as well as positional and matrix game methods, were used to synthesize computational algorithms for the safe trajectory of one’s own object. The aim of the comparative analysis of intelligent computational methods for the safe trajectory of an object was to show, through their use, the possibility of taking into account the risk of collision resulting from both the degree of cooperation of objects while observing traffic laws and the impact of the environment in the form of visibility and the complexity of the situation. Simulation tests of the algorithms were carried out on the example of a real navigation situation of several dozen objects passing each other at sea.

Cross-sectoral exchange of nurses: An intervention study.

In health policy, much attention has been paid to collaboration between the primary and secondary health care sectors, especially in relation to hospitalisation and discharge. Despite ideal plans for collaboration, the research literature shows that inadequate communication is a well-known problem that can be a barrier to a safe trajectory for the citizen. Based on the assumption that better knowledge of each other's work will lead to better collaboration, a cross-sectoral exchange program with nurses was initiated. The aim was to investigate which barriers to good patient trajectories the involved nurses attributed to cross-sectoral collaboration and what impact the exchange to the opposite sector had for them. Twenty-eight nurses were exchanged: 14 from a cardiology department and 14 from municipal home care. The nurses shadowed a colleague from the opposite sector in their daily work. Subsequently, six focus group interviews were conducted. The transcribed material was analysed based on Ricoeur's interpretation theory. Two main themes, including sub-themes emerged: (1) Challenging communicative conditions: (a) Inadequate digital communication, (b) Inadequate care plans and discharge reports, (c) Conversation promotes understanding, and (d) Challenging collaboration and communication with the discharge coordinators. (2) Perceived importance of the exchange: (a) Cross-sectoral relationship, prejudice and gaining respect for each other and (b) Working in two different worlds. Electronic communication is inadequate, and the IT systems do not support sufficient cross-sectoral communication. The organisational model in the municipal care sector is inflexible in terms of allocations for the current needs of citizens, and professionals feel that their professional judgements are not recognised. The nurses gained insight into each other's work and working conditions and respect for each other's professionalism. The exchange has the potential to both improve the relationship and communication between the sectors for the benefit of a better and more coherent patient course.