Collision Avoidance Research Articles

Computational Design with Kurtboğaz: The Generation of Timber Structures with an Aggregative Design Algorithm

This paper investigates the form-finding capacity of the traditional timber-joint construction method, Kurtboğaz, aiming to explore new architectural forms and possibilities through computational design techniques to preserve vernacular construction methods and integrate them into contemporary architecture. It presents an Aggregative Design Algorithm (ADA) that creates different structures based on designer rules and simple assembly rules of Kurtboğaz, leading to unique emergent forms through random rule application. The paper also explores how reinforcement learning, a type of machine learning, can improve this design process through a theoretical framework. The study tries to use a rule-based generative algorithm to explore the modular and reconfigurable characteristics of the Kurtboğaz. The ADA enables random rule application, leading to diverse forms. However, several challenges may be encountered during the application of ADA because of its random aggregation, such as collision avoidance, structural integrity, boundary detection, and the optimization of structural parameters. The study suggests using Reinforcement Learning (RL) in the ADA framework to address these problems. Incorporating RL is anticipated to enable the algorithm to adaptively learn and optimize the form-finding process, enhancing the performance and applicability of the Kurtboğaz method in contemporary architectural practice. In the future, with this generative process described by the study, designs that create spatial differences with the help of walls, floors, and rooms on a human scale can be realized. The study also plans to explore the synergy between craftsmanship and digital fabrication in the future

Comparative Analysis of the Methods of Planning and Coordinating of Manipulator Robot Movement

This paper presents a comparative analysis of two methods for planning and coordinating the movement of robot manipulators in dynamic environments: a neural network-based approach for solving dynamic production scenarios and the rapidly exploring random trees algorithm. The study aims to enhance the trajectory planning of robot manipulators by leveraging the strengths of intelligent systems. The neural network method is designed to perceive the environment, generate accurate control commands, and adapt to changing conditions in real-time. The paper the processes involved in environmental analysis, collision avoidance, and control signal generation for actuators, with an emphasis on the neural network architecture tailored for these tasks. The results demonstrate that the neural network approach offers significant improvements in adaptability and efficiency, providing a robust solution for optimizing automated processes in dynamic production environments.

COLREGs-Adaptive trajectory planning and decision-making in maritime autonomous surface ships

Decision-making for collision avoidance and trajectory planning are critical technologies for maritime autonomous surface ships. These systems must align with regulatory frameworks such as the International Regulations for Preventing Collisions at Sea (COLREGs) and account for the ship’s maneuvering capabilities for effective control and tracking. This study introduces a novel framework integrating regulatory consideration into the path-searching process, enhancing collision avoidance in both COLREG-compliant and non-compliant scenarios. The framework recasts the trajectory-planning problem into an optimal control problem and employs virtual obstacles and spatial–temporal navigation corridors consistent with collision-free decisions as constraints for trajectory optimization, improving navigation efficiency and comfort. The framework is validated through various encounter scenarios, and the results demonstrate that the proposed framework can produce superior collision avoidance decisions, while planning shorter navigation time and smoother collision-free trajectories, significantly improving the collision avoidance capability of maritime autonomous surface ships.

Strategies for unplanned gait termination at comfortable and fast walking speeds in children with cerebral palsy

Collision avoidance while walking is necessary for safe living, and faster walking speeds tend to increase collision risk. However, gait termination strategies for patients with cerebral palsy (CP), from comfortable to faster speed, remain unexplored. This study aimed to analyze these strategies in children with CP compared to typically developing (TD) children at two different speeds. Study participants included 10 children with CP (mean age, 12.5; five females; mean height, 147.8 cm; mean weight, 41.7 kg) and 10 TD children (mean age, 11.4; nine females; mean height, 142.0 cm; mean weight, 38.1 kg). Effects of walking speed on spatial, force, and temporal parameters were assessed at 100 % (WS1) and 125 % (WS2) speeds of comfortable walking. The TD group exerted a more pronounced braking force at the first step after the stop line appeared on the floor until the contralateral step at both WS1 (P = 0.006) and WS2 (P = 0.019); however, the CP group exerted a more potent force after the second step (WS1: P = 0.026, WS2: P = 0.023) in the anterior-posterior (AP) direction. Additionally, an increase in the center of mass (COM)-center of pressure (COP) divergence in the AP direction (P = 0.032), which decreased in the mediolateral (ML) direction (P = 0.036) at faster walking speeds, influenced the kinetic characteristics of the CP group from WS1 to WS2. The complex adaptations, such as unique braking forces and changes in the COM-COP divergence, suggest that gait interventions should consider the distinctive forces and adopt dynamic balancing strategies to avoid collisions during walking.

Virtual Obstacle Avoidance Strategy: Navigating through a Complex Environment While Interacting with Virtual and Physical Elements.

Robotic walking devices can be used for intensive exercises to enhance gait rehabilitation therapies. Mixed Reality (MR) techniques may improve engagement through immersive and interactive environments. This article introduces an MR-based multimodal human-robot interaction strategy designed to enable shared control with a Smart Walker. The MR system integrates virtual and physical sensors to (i) enhance safe navigation and (ii) facilitate intuitive mobility training in personalized virtual scenarios by using an interface with three elements: an arrow to indicate where to go, laser lines to indicate nearby obstacles, and an ellipse to show the activation zone. The multimodal interaction is context-based; the presence of nearby individuals and obstacles modulates the robot's behavior during navigation to simplify collision avoidance while allowing for proper social navigation. An experiment was conducted to evaluate the proposed strategy and the self-explanatory nature of the interface. The volunteers were divided into four groups, with each navigating under different conditions. Three evaluation methods were employed: task performance, self-assessment, and observational measurement. Analysis revealed that participants enjoyed the MR system and understood most of the interface elements without prior explanation. Regarding the interface, volunteers who did not receive any introductory explanation about the interface elements were mostly able to guess their purpose. Volunteers that interacted with the interface in the first session provided more correct answers. In future research, virtual elements will be integrated with the physical environment to enhance user safety during navigation, and the control strategy will be improved to consider both physical and virtual obstacles.

Autonomous collision avoidance decision-making method for USV based on ATL-TD3 algorithm

To address the issue of decision-making for ship collision avoidance in complex encounter situations, an Autonomous Collision Avoidance Decision-making (ACAD) model based on Attention Long Short-Term Memory Twin Delayed Deep Deterministic Policy Gradient (ATL-TD3) algorithm for Unmanned Surface Vessel (USV) is proposed in this study. The novelty of this method is: (1) Incorporating LSTM and multi-head self-attention mechanism into the existing TD3 algorithm network enhances its focus on pertinent historical state information. Furthermore, the generalization ability of the trained model is further enhanced through optimization of the accumulated experience. (2) A comprehensive reward mechanism is designed, incorporating ship domain, arena, yaw, and other relevant factors, which solve the encounters defined in clauses 13–17 in Chapter 2 of COLREGs and immediate danger. The experiment involved a comparative analysis of the average reward curve and success rate during training under different environments. Additionally, 20 cases of Imazu classic encounters and real navigation data from the Suez Canal were utilized to validate the proposed algorithm. It is highlight that the ACAD model trained by the ATL-TD3 algorithm exhibits remarkable performance in the generalization verification, surpassing conventional collision avoidance methods.

Automatic Optimal Robotic Base Placement for Collaborative Industrial Robotic Car Painting

This paper investigates the problem of optimal base placement in collaborative robotic car painting. The objective of this problem is to find the optimal fixed base positions of a collection of given articulated robotic arms on the factory floor/ceiling such that the possibility of vehicle paint coverage is maximized while the possibility of robot collision avoidance is minimized. Leveraging the inherent two-dimensional geometric features of robotic car painting, we construct two types of cost functions that formally capture the notions of paint coverage maximization and collision avoidance minimization. Using these cost functions, we formulate a multi-objective optimization problem, which can be readily solved using any standard multi-objective optimizer. Our resulting optimal base placement algorithm decouples base placement from motion/trajectory planning. In particular, our computationally efficient algorithm does not require any information from motion/trajectory planners a priori or during base placement computations. Rather, it offers a hierarchical solution in the sense that its generated results can be utilized within already available robotic painting motion/trajectory planners. Our proposed solution’s effectiveness is demonstrated through simulation results of multiple industrial robotic arms collaboratively painting a Ford F-150 truck.

Enhanced Multi-UAV Formation Control and Obstacle Avoidance Using IAAPF-SMC

In response to safety concerns pertaining to multi-UAV formation flights, a novel obstacle avoidance method based on an Improved Adaptive Artificial Potential field (IAAPF) is presented. This approach enhances UAV obstacle avoidance capabilities by utilizing segmented attraction potential fields refined with adaptive factors and augmented with virtual forces for inter-UAV collision avoidance. To further enhance the control and stability of multi-UAV formations, a Sliding Mode Control (SMC) method is integrated into the IAAPF-based obstacle avoidance framework. Renowned for its robustness and ability to handle system uncertainties and disturbances, the SMC method is combined with a feedback control system that utilizes inner and outer loops. The outer loop generates the desired path based on the leader’s state and control commands, while the inner loop tracks these trajectories and adjusts the follower UAVs’ motions. This design ensures that obstacle feedback is accounted for before the desired state information is received, enabling effective obstacle avoidance while maintaining formation integrity. Integrating leader-follower formation control techniques with SMC-based multi-UAV obstacle avoidance strategies ensures the effective convergence of the formation velocity and spacing to predetermined values, meeting the cooperative obstacle avoidance requirements of multi-UAV formations. Simulation results validate the efficacy of the proposed method in reaching otherwise unreachable destinations within obstacle-rich environments, while ensuring robust collision avoidance among UAVs.

Differential Game-Based Cooperative Interception Guidance Law with Collision Avoidance

To deal with the offense-defense confrontation problem of multi-missile cooperative intercepting a high-speed and large-maneuvering target, a differential game-based cooperative interception guidance law with collision avoidance is proposed, in which the offense-defense parties are the incoming target and the interceptors, respectively. Given that both offense-defense parties have uniformly decreasing speeds and first-order biproper dynamics, the relative motion models among the offense-defense parties are established, and the performance indices of the target and the interceptors are proposed. After that, the cooperative interception guidance law with collision avoidance is derived based on a differential game. The guidance law considers the effects of speed variations and rudder layouts on the motions of both offense-defense parties, ensuring excellent algorithmic real-time property and interception accuracy while introducing inter-missile collision avoidance constraints. In addition, the parameters of the target performance index are set according to the target acceleration information estimated by the interceptors. The simulation results verify the effectiveness of the guidance law designed in this paper, under various three-to-one scenarios, the interceptors could achieve collision-free interceptions with the interception accuracy of less than 5 m and the interception time difference of less than 0.1 s.

The study of design and optimisation for CFRP combined pier anti-collision device

A CFRP honeycomb combination anti-collision device with a larger anti-collision level, greater crashworthiness, and longer service life was designed to safeguard the ship and piers in the event of an unintentional collision. Taking large-tonnage cargo ships and common bridge piers in the middle reaches of the Yangtze River as the research objects, the energy conversion response, impact force response and impact depth response of the collision were studied considering the collision conditions between the ship and the bare pier and the collision avoidance device. On this basis, the response surface method, variance analysis method and efficacy coefficient method were further used to take the shell thickness of the collision avoidance device and the thickness of the internal steel structure as the design parameters. The multi-objective optimisation design of the CFRP composite anti-collision device is carried out by selecting the ship impact force, the total internal energy absorbed by the anti-collision device, the total mass, and the inner diameter of the honeycomb steel structure as the optimisation objectives. The results show that the impact force reduction rate reaches 82.7%, the overall mass is greatly reduced, the maximum deformation is within a reasonable range, and the collision resistance is optimised.

Optimal robot task scheduling in cluttered environments considering mechanical advantage

Abstract In various industrial robotic applications, the effective traversal of a manipulator amidst obstacles and its ability to reach specific task-points are imperative for the execution of predefined tasks. In certain scenarios, the sequence in which the manipulator reaches these task-points significantly impacts the overall cycle time required for task completion. Moreover, some tasks necessitate significant force exertion at the end-effector. Therefore, establishing an optimal sequence for the task-points reached by the end-effector’s tip is crucial for enhancing robot performance, ensuring collision-free motion and maintaining high-force application at the end-effector’s tip. To maximize the manipulator’s manipulability, which serves as a performance index for assessing its force capability, we aim to establish an optimal collision-free task sequence considering higher mechanical advantage. Three optimization criteria are considered: the cycle time, collision avoidance and the manipulability index. Optimization is accomplished using a genetic algorithm coupled with the Bump-Surface concept for collision avoidance. The effectiveness of this approach is confirmed through simulation experiments conducted in 2D and 3D environments with obstacles employing both redundant and non-redundant robots.

Formation Control with Feedback Nash Strategy for Multiple Unmanned Aerial Vehicles in Unknown Environments

In the paper, the formation control problem in unknown environments for networked multi-unmanned aerial vehicle systems (UAVSs) is resolved under a nonlinear differential game (NL-DDG) framework. The main challenge of this framework is how to obtain feedback Nash strategies, which typically overly rely on global information and cannot ensure the existence of Nash strategies in unknown environments. Toward this goal, we initially design collision avoidance rules to ensure the safety of each UAV. Subsequently, we utilize an inverse optimal control method to construct the NL-DDG that incorporates both formation control and collision avoidance costs, enabling the derivation of analytical forms of Nash strategies relying solely on local information and minimizing performance metrics. In addition, the existence of feedback Nash strategy can be guaranteed with an undirected and connected information topology, which represents the optimality for the UAVSs. Moreover, we analyze the stability of the closed-loop system. Finally, the simulation results validate the effectiveness of the proposed scheme.

Risk Assessment and Motion Planning for MAVs in Dynamic Uncertain Environments

Risk assessment to quantify the danger associated with a planned trajectory is critical for micro aerial vehicles (MAVs) navigating in dynamic uncertain environments. Existing works usually perform risk assessment by reasoning the occupancy status of the MAV’s surrounding space which only incorporates the position information of the MAV and the obstacles in the environment. In this paper, we further consider the MAV’s motion direction in risk assessment to reflect the fact that the obstacles in front of the MAV pose a higher risk while those behind pose a lower risk. In particular, we rely on a particle-based dynamic map which consists of a large number of particles to represent the local environment. The risk is defined to evaluate the safety level of a subspace in the map during some time interval and assessed by reasoning the occurrence of particles in the subspace. Those particles around the MAV are assigned different weights taking into account their relative positions to the MAV and its motion direction. We then incorporate the proposed risk assessment method into MAV motion planning by minimizing both the path length and the associated risk to achieve safer navigation. We compared our method with several state-of-the-art approaches in PX4+Gazebo simulations and real-world experiments. The results show that our method can achieve a 15% higher collision avoidance rate and a 20% lower flight risk in various environments with static and dynamic obstacles.

An integrated decision support system for ship collision avoidance

The ship encounter process is divided into the stages of free action, risk of collision, close-quarter, and immediate danger according to the four-stage theory of ship encounters. Current research on ship collision avoidance primarily focuses on the risk of collision stage, aiming to prevent ships from entering the close-quarter stage. However, various navigational equipment and environmental factors may cause ships to enter the close-quarter or immediate danger stages. Therefore, this paper establishes an integrated decision support system for the above stages to ensure that ships can handle all situations. First, the system includes a new collision risk detection module based on the ship domain model and the four-stage theory. Subsequently, in the collision avoidance strategy formulation module, two sets of avoidance strategies are established based on the convention on the COLREGs and the most effective avoidance principle for ships in different stages. Finally, in the avoidance action optimization module, a new fitness function is constructed based on ship maneuverability, and the sparrow search algorithm is introduced to solve for the optimal avoidance actions. The results demonstrate the capability of the decision support system presented in this paper to effectively avoid other ships in different stages of the encounter process.

Guaranteed real-time cooperative collision avoidance for n-DOF manipulators

Abstract This paper presents a decentralized, cooperative, real-time avoidance control strategy for robotic manipulators. The proposed avoidance control law builds on the concepts of artificial potential field functions and provides tighter bounds on the minimum safe distance when compared to traditional potential-based controllers. Moreover, the proposed avoidance control law is given in analytical, continuous closed form, avoiding the use of optimization techniques and discrete algorithms, and is rigorously proven to guarantee collision avoidance at all times. Examples of planar and 3D manipulators with cylindrical links under the proposed avoidance control are given and compared with the traditional approach of modeling links and obstacles with multiple spheres. The results show that the proposed avoidance control law can achieve, in general, faster convergence, smaller tracking errors, and lower control torques than the traditional approach. Furthermore, we provide extensions of the avoidance control to robotic manipulators with bounded control torques.

How many moving obstacles do we respond to at once? A temporal threshold model best accounts for collision avoidance in a crowd

How many moving obstacles do we respond to at once? A temporal threshold model best accounts for collision avoidance in a crowd

A hybrid RVO-MPPI approach for efficient collision avoidance for multiple autonomous underwater vehicles

Collision-avoidance path planning for Autonomous Underwater Vehicles (AUVs) in complex underwater environments presents a significant challenge, particularly considering the maneuverability limitations of fin-rudder-controlled AUVs. This study proposes a novel navigation approach that combines the strengths of Reciprocal Velocity Obstacle (RVO) principles and Model Predictive Path Integral (MPPI) control to enhance autonomous obstacle avoidance for multiple AUVs. The key innovation lies in leveraging RVO-based obstacle avoidance velocities to enhance both action sampling and cost function optimization in MPPI framework. Specifically, a neural network trained through supervised learning is utilized to convert the RVO-provided avoidance velocities into desired actions for MPPI. Additionally, these velocities are incorporated into the MPPI cost function, thereby enhancing the optimization process and facilitating convergence towards optimal, collision-free navigation solutions. Simulation experiments validated the method's capability to provide effective obstacle avoidance behavior across various complex scenarios. Results indicate that this approach successfully integrates MPPI's predictive capabilities with RVO's velocity avoidance logic, enabling AUVs to navigate efficiently through complex underwater environments while ensuring mission safety and effectiveness. This approach offers a promising new solution for autonomous navigation of AUVs, with potential applications in ocean exploration, underwater search and rescue, and other maritime operations. Future research will explore the performance of this approach in real-world underwater environments and consider the impact of additional dynamic factors.

Change in the collision avoidance performance of autonomous emergency braking systems

Objective This study quantifies the change in collision avoidance performance of autonomous emergency braking (AEB) systems for traffic accidents in Japan since 2015. Method This study used data on Japanese traffic accidents compiled by Japan’s National Police Agency. The data included only accidents involving loss of or injury to human life; accidents involving only property damage were excluded. We restricted our analysis to collisions between two 4-wheel vehicles and considered only collisions for which we could determine whether the primary party’s car was equipped with an AEB system. Both Poisson and negative binomial mixed-effects regression analyses were conducted using the data for 2021 and 2022 to measure the collision avoidance performance of first registered cars in 2015 to 2020 equipped with AEB systems compared with cars without AEB systems first registered in 2015. Collision avoidance performance was measured for 2 types of intervehicle collisions: rear-end collisions and right-turn collisions. Collision avoidance performance for rear-end collisions was also measured for each of the 3 car types—Standard, small, and light cars. Results The collision reduction rate for rear-end collisions increases with the year of first registration and for cars equipped with AEB systems first registered in 2020 compared with non-AEB-equipped cars first registered in 2015 is 69.2% (95% confidence interval [CI] 67.0%–71.1%), indicating that the performance of AEB systems has dramatically improved in terms of preventing rear-end collisions. For right-turn collisions, the rate increased to 20.4% (95% CI 5.9%–32.6%) for cars equipped with AEB systems first registered in 2019. However, no clear trend is observed. Conclusions This study evaluated a time series of the collision reduction performance of AEB systems using an original methodology. Japan’s New Car Assessment Program (JNCAP) has included AEB’s effectiveness in reducing damage from traffic collisions as an evaluation item since FY2014. The results could demonstrate the effectiveness of JNCAP.

Robust Stochastic Model Predictive Control for Autonomous Vehicle Motion Planning

This work presents a Robust Stochastic Model Predictive Control (RSMPC) framework for real-time motion planning autonomous vehicles, addressing the complex multi-modal vehicle interactions. The proposed framework involves adding expert policy from observations to the dataset and applying the Data Aggregation (DAgger) method to filter unsafe demonstrations and resolve expert conflicts. A Dual-Stage Attention-based Recurrent Neural Network (DA-RNN) model is integrated to predict dual class variables from the dataset, producing a set containing constraints collision-avoidance predicted to be active. The RSMPC framework enhances formulation optimization by eliminating irrelevant collision avoidance constraints, resulting in faster control signals. The framework is applied iteratively, continuously updating observations and solving the RSMPC optimization formulation in real-time. Evaluation of the DA-RNN model achieved a recall value of 0.97 and a high accuracy rate of 98.1% in predicting dual interactions, with a minimal false negative rate of 0.026, highlighting its effectiveness in capturing interaction intricacies. Validated through simulations of interactive traffic intersections, the proposed framework demonstrably excels, showing high feasibility of 99.84% and a 15-fold increase in response speed compared to the baseline. This approach ensures autonomous vehicles navigate safely and efficiently in complex traffic scenarios, paving the way for more reliable and scalable autonomous driving solutions.

Multi-robot collision avoidance method in sweet potato fields.

Currently, precise spraying of sweet potatoes is mainly accomplished through semi-mechanized or single spraying robots, which results in low operating efficiency. Moreover, it is time-consuming and labor-intensive, and the pests and diseases cannot be eliminated in time. Based on multi robot navigation technology, multiple robots can work simultaneously, improving work efficiency. One of the main challenges faced by multi robot navigation technology is to develop a safe and robust collision avoidance strategy, so that each robot can safely and efficiently navigate from its starting position to the expected target. In this article, we propose a low-cost multi-robot collision avoidance method to solve the problem that multiple robots are prone to collision when working in field at the same time. This method has achieved good results in simulation. In particular, our collision avoidance method predicts the possibility of collision based on the robot's position and environmental information, and changes the robot's path in advance, instead of waiting for the robot to make a collision avoidance decision when it is closer. Finally, we demonstrate that a multi-robot collision avoidance approach provides an excellent solution for safe and effective autonomous navigation of a single robot working in complex sweet potato fields. Our collision avoidance method allows the robot to move forward effectively in the field without getting stuck. More importantly, this method does not require expensive hardware and computing power, nor does it require tedious parameter tuning.