Avoidance Performance Research Articles

Deep Reinforcement Learning for Autonomous Driving in Amazon Web Services DeepRacer

The development of autonomous driving models through reinforcement learning has gained significant traction. However, developing obstacle avoidance systems remains a challenge. Specifically, optimising path completion times while navigating obstacles is an underexplored research area. Amazon Web Services (AWS) DeepRacer emerges as a powerful infrastructure for engineering and analysing autonomous models, providing a robust foundation for addressing these complexities. This research investigates the feasibility of training end-to-end self-driving models focused on obstacle avoidance using reinforcement learning on the AWS DeepRacer autonomous race car platform. A comprehensive literature review of autonomous driving methodologies and machine learning model architectures is conducted, with a particular focus on object avoidance, followed by hands-on experimentation and the analysis of training data. Furthermore, the impact of sensor choice, reward function, action spaces, and training time on the autonomous obstacle avoidance task are compared. The results of the best configuration experiment demonstrate a significant improvement in obstacle avoidance performance compared to the baseline configuration, with a 95.8% decrease in collision rate, while taking about 79% less time to complete the trial circuit.

Role of an interdependent Wnt, GSK3-β/β-catenin and HB-EGF/EGFR mechanism in arsenic-induced hippocampal neurotoxicity in adult mice

We previously reported the neurotoxic effects of arsenic in the hippocampus. Here, we explored the involvement of Wnt pathway, which contributes to neuronal functions. Administering environmentally relevant arsenic concentrations to postnatal day-60 (PND60) mice demonstrated a dose-dependent increase in hippocampal Wnt3a and its components, Frizzled, phospho-LRP6, Dishevelled and Axin1 at PND90 and PND120. However, p-GSK3-β(Ser9) and β-catenin levels although elevated at PND90, decreased at PND120. Additionally, treatment with Wnt-inhibitor, rDkk1, reduced p-GSK3-β(Ser9) and β-catenin at PND90, but failed to affect their levels at PND120, indicating a time-dependent link with Wnt. To explore other underlying factors, we assessed epidermal growth factor receptor (EGFR) pathway, which interacts with GSK3-β and appears relevant to neuronal functions. We primarily found that arsenic reduced hippocampal phosphorylated-EGFR and its ligand, Heparin-binding EGF-like growth factor (HB-EGF), at both PND90 and PND120. Moreover, treatment with HB-EGF rescued p-GSK3-β(Ser9) and β-catenin levels at PND120, suggesting their HB-EGF/EGFR-dependent regulation at this time point. Additionally, rDkk1, LiCl (GSK3-β-activity inhibitor), or β-catenin protein treatments induced a time-dependent recovery in HB-EGF, indicating potential inter-dependent mechanism between hippocampal Wnt/β-catenin and HB-EGF/EGFR following arsenic exposure. Fluorescence immunolabeling then validated these findings in hippocampal neurons. Further exploration of hippocampal neuronal survival and apoptosis demonstrated that treatment with rDkk1, LiCl, β-catenin and HB-EGF improved Nissl staining and NeuN levels, and reduced cleaved-caspase-3 levels in arsenic-treated mice. Supportively, we detected improved Y-Maze and Passive Avoidance performances for learning-memory functions in these mice. Overall, our study provides novel insights into Wnt/β-catenin and HB-EGF/EGFR pathway interaction in arsenic-induced hippocampal neurotoxicity.

Relative navigation with Bayesian range estimation

The applications of multiple unmanned aerial vehicles (UAVs) require high-precision and reliable relative navigation for flight formation, collision avoidance, and mission performance. Providing high-precision relative navigation is possible by fusing the observations of inertial measurement units, global navigation satellite system receivers, and radio-ranging modules. However, radio-ranging is vulnerable to various types of interference, including additive noise, nonlinear propagation, and multi-user interference. At the same time, UAVs strict energy constraints force us to look for ways to increase interference immunity without increasing the transmitting power of radio-ranging modules. This work is about improving the noise immunity of relative navigation by using a scheme tightly coupled with radio ranging and Bayesian range estimation. We describe the proposed scheme and also carry out a comparative simulation of tightly coupled and conventional, loosely coupled schemes. According to the simulation, a tightly coupled scheme has a quarter smaller RMS error compared to a loosely coupled one at an SNR below -7 dB. The obtained results demonstrate the prospects for further development of the proposed scheme.

Path planning of unmanned vehicles based on adaptive particle swarm optimization algorithm

Path planning technology is the basis of autonomous driving of unmanned vehicles. However, there are some problems in the traditional path planning technology. For example, high-quality global paths can't be generated quickly; Lacking of security verification ability; The performance of dynamic obstacle avoidance is poor. Therefore, this paper proposes a path planning method of unmanned vehicles based on adaptive particle swarm optimization algorithm (APSO). Firstly, a map simplification strategy (MSS) is proposed. The grid map is preprocessed by map simplification strategy to reduce the search space and time of path planning algorithm; Secondly, an APSO algorithm is proposed. The algorithm coordinates the search of particles through three adaptive factors and Levy flight strategy. Then, a security checking strategy is proposed. Security checking strategy can be used to verify the safety of global path; Finally, a dynamic obstacle avoidance strategy based on behavior is proposed. Vehicles can independently analyze the types of collision and adopt corresponding obstacle avoidance strategies. The simulation results show that MSS-APSO algorithm and APSO algorithm surpass original algorithms and comparison algorithms; MSS-APSO algorithm has strong applicability in real map environment; The obstacle avoidance strategy has great obstacle avoidance ability and real-time performance; The map simplification strategy can improve iterations of the algorithm and quality of the global path.

Collision avoidance trajectory planning for a dual-robot system: using a modified APF method

AbstractDual-robot system has been widely applied to the field of handling and palletizing for its high efficiency and large workspace. It is one of the key problems of the trajectory planning to determine the collision avoidance method of the dual-robot system. In the present study, a collision avoidance trajectory planning method for the dual-robot system was proposed on the basis of a modified artificial potential field (APF) algorithm. The interference and collision criterion of the dual-robot system was given firstly, which was established based on the method of kinematic analysis in robotics. And then, in consideration of the problem of excessive virtual potential field force induced by using the traditional APF algorithm in the process of dual-robot trajectory planning, a modified APF algorithm was proposed. Finally, the modified APF algorithm was used for motion control of a dual-robot palletizing process, and the collision avoidance performance of the proposed collision avoidance algorithm was studied through a dual-robot palletizing simulation and experiment. The results have shown that with the proposed collision avoidance trajectory planning algorithm, the two robots in dual-robot system can maintain a safe distance at all times during palletizing process. Compared with the traditional APF and rapidly-exploring random tree (RRT) algorithm, the trajectory solution time of the modified APF algorithm is greatly reduced. And the modified APF algorithm’s convergence time is 14.2% shorter than that of the traditional APF algorithm.

Obstacle avoidance decision and trajectory tracking control of intelligent vehicle considering surrounding vehicles

To enhance the obstacle avoidance performance and trajectory tracking control stability of vehicles in emergency scenarios, and to solve the problem that the existing decision-making methods based on the minimum safe distance model cannot effectively make decisions in some scenarios, a novel lane-changing obstacle avoidance decision-making and control method is proposed. Initially, when emergency braking cannot avoid obstacles safely, the lane-changing trajectory is planned by quintic polynomial, and the minimum distance and collision detection (MDCD) algorithm is then employed to ascertain whether the lane-changing trajectory meets the safety conditions. Subsequently, a Linear Time-Varying Model Predictive Control (LTV MPC) trajectory tracking controller is designed based on the 7-DOF vehicle dynamics model. Finally, the effectiveness of the proposed method is verified through two representative scenarios. The simulation results indicate that the MDCD algorithm can ensure the safety and effectiveness of obstacle avoidance decisions in all emergency obstacle avoidance scenarios, and when tracking the emergency lane-changing trajectory in different typical scenarios, compared with the LTV MPC controller based on the single-track vehicle dynamics model (STVDM), the maximum tracking error can be reduced by 50.7% and 60.1%, respectively, while ensuring near-equivalent real-time performance operation.

The Effect of Corporate Social Responsibility on Firm Value with Tax Avoidance and Sustainable Financial Performance as Mediators

Research confirms the influence of corporate social responsibility on firm value through tax avoidance and sustainable financial performance. Corporate social responsibility is measured based on the ESG disclosure score. In contrast, firm value is measured using Tobin's Q. Tax avoidance is measured using the effective tax rate, and sustainable financial performance is measured using return on assets. The samples analyzed in this research were 93 public companies listed on the IDX from all sectors. This research uses a purposive sampling method to produce reports on 93 companies in 5 years of company financial reports in 2017-2021. Data analysis used the Partial Least Square SEM technique. The analysis results show that corporate social responsibility has a significant direct effect on firm value, while the mediating role of tax avoidance and sustainable financial performance is not proven. In this research, it can be seen that corporate social responsibility (CSR) has a direct influence on firm value. This shows that stakeholders rely on CSR as a positive image of the company, as in several previous research results, it can describe the value of the company, which has an impact on investors' decisions to invest but indirectly does not influence tax avoidance and sustainable financial performance. The results of this research can provide an additional understanding of the previous theoretical concept that CSR can directly influence firm value. The strategic implications, of course, depend on company decision-makers in communicating CSR activities in their reports when they want investors' attention to the firm value.

Application of fuzzy reinforcement learning in IoT-based robotics for autonomous navigation and collision avoidance

In this study, we investigate the viability of applying fuzzy reinforcement learning (FRL) to Internet of Things-based robots for purposes of autonomous navigation and collision avoidance. The proposed approach utilises FRL, IoT, and a sensor network to give the robot the ability to learn from its environment and act in accordance with those policies. The authors used FRL to train a mobile robot with wheels to move around and avoid obstacles, and then they put the robot through its paces in a virtual world. Results showed that the FRL-based technique improved the robot’s navigation and collision avoidance performance compared to traditional rule-based approaches. The results of this study indicate that FRL may be a viable technique for enabling autonomous navigation and obstacle avoidance in IoT-based robotics.

Method for collision avoidance based on deep reinforcement learning with path-speed control for an autonomous ship

In this paper, we propose a collision avoidance method based on deep reinforcement learning (DRL) that simultaneously controls the path and speed of a ship. The DRL is actively applied in machine control and artificial intelligence. To verify the proposed method, we applied it to the Imazu problem. It provides benchmark scenarios for collision avoidance. In particular, we compared and analyzed the collision avoidance performance according to the level of learning and various parameters to ensure that the proposed method displays optimal avoidance performance. The results indicated that the proposed method can determine a safe avoidance path for a given situation. Finally, to compare the performance of the proposed method, we compared the collision avoidance method based on the path–speed control of the OS proposed in this study with the collision avoidance method that controls only the path of the OS (Chun et al., 2021). We observed that the proposed method failed in 6 out of 20 scenarios of the Imazu problem when only the path of the OS was controlled. However, it succeeded in collision avoidance in all the 20 scenarios when both path and speed were controlled simultaneously.

Obstacle avoidance approaches of autonomous mobile robots in harsh environment

Obstacle avoidance has been a hotspot in the field of robotics. Good performance of obstacle avoidance is key to the success of robots, especially in harsh environment where the external environmental factors pose challenges to the robots. Currently, numerous obstacle avoidance methods are being developed in an attempt to optimize the decision of the autonomous mobile robots in harsh environment. To enhance the reliability of obstacle avoidance methods, it is essential to have a clear picture of the advances and breakthroughs in this field and see which research area needs to be improved. This paper focuses on the current obstacle avoidance methods or path-planning algorithms in the underwater environment and the space environment, with description of their works and discussion of their advantages and weakness. Current problems of these methods include lack of reliable field tests, inadequate assumption of obstacles when designing the algorithm and limited capability in handling special cases that no possible path is feasible. This paper intends to provide researchers with a generalized review of obstacle avoidance methods in harsh environment and a proposal of the potential research directions in this field in the future. It is recommended that researchers to design more complex scenarios, improve the performance of robots to avoid being trapped in special situations, and consider the adaptability of robots in different environment.

A dynamic obstacle avoidance method for collaborative robots based on trajectory optimization

Background Collision detection is crucial in the design of robot planning algorithms. Efficient distance sensors can provide high-resolution environmental collision information to the robot's planning algorithm. However, this also leads to the robot obstacle avoidance performance being limited by the performance of the sensors. Therefore, it becomes a challenge to achieve efficient obstacle avoidance with low-resolution environmental information. Methods First, we use a self-developed capacitive array non-contact distance sensing flexible surface for sensing the proximity of colliding objects. Second, we designed an optimization-based dynamic obstacle avoidance planning algorithm, using only the minimum separation distance and penetration direction as obstacle avoidance information, and referring to the idea of stochastic gradient descent, using real-time collision avoidance information to do single-step optimization adjustment. Results We conducted the dynamic obstacle avoidance test experiment by connecting the electronic skin to the semi-physical prototype and the full physical prototype. The experiments show that efficient dynamic obstacle avoidance can be realized under the maximum effective range of only 5~7cm, and it has strong flexibility to avoid different shapes of dynamic obstacles in a non-contact manner, and finally arrive at the target position. Conclusions In this paper, an online obstacle avoidance planning algorithm designed based on an optimization method that is not limited to the shape of obstacles is proposed, and the effectiveness of the algorithm is verified by physical experiments in combination with a self-developed flexible distance sensing surface. It is of great significance for the safe operation of human-robot interaction in collaborative robots.

Deep reinforcement learning based collision avoidance system for autonomous ships

Autonomous ships is a key to avoid accidents caused by human errors and improve maritime safety. However, unlike the autonomous vehicles counterpart, collision avoidance for autonomous ships faces many challenges due to the hash driving environments, difficult ship control and large stopping distance. In this paper, we investigate a collision avoidance system for autonomous ships under complex encounter scenarios, such as busy ports. In the system various sensors are used to detect objects and perceive the maritime environments. To help the autonomous ships handle the complex and dynamic scenarios that may be encountered, a collision map used to describe the ships encounter scenarios is generated and utilized as the input of a deep reinforcement learning (DRL) model. The DRL model is applied to make collision avoidance and safe driving decisions. New reward functions are proposed to train the DRL model to generate safe ship maneuver actions to reduce collisions and ensure compliance with the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). Furthermore, a self-adaptive parameters sharing approach is designed for fast convergence and collision avoidance performance of the DRL model, where the parameters of the fully connected layers are shared and the correlation layers are self adapted for the DRL critic and actor networks. Simulation results show that the proposed system has high DRL convergence speed and excellent collision avoidance.

Formation Control for UAV-USVs Heterogeneous System with Collision Avoidance Performance

This paper investigates the cooperative formation trajectory tracking problem for heterogeneous unmanned aerial vehicle (UAV) and multiple unmanned surface vessel (USV) systems with collision avoidance performance. Firstly, a formation control protocol based on extended state observer (ESO) is proposed to ensure that the UAV and the USVs track the target trajectory simultaneously in the XY plane. Then, the collision avoidance control strategy of USV formation based on artificial potential field (APF) theory is designed. Specifically, the APF method is improved by reconstructing the repulsive potential field to make the collision avoidance action of USVs more in line with the requirements of International Regulations for Preventing Collisions at Sea (COLREGs). Following that, an altitude controller for the UAV is proposed to maintain the cooperative formation of the heterogeneous systems. Based on the input-to-state stability, the stability of the proposed control structure is proven, and all the signals in the closed-loop system are ultimately bounded. Finally, a simulation study is provided to show the efficacy of the proposed strategy.

Analysis of the Effect of Tax Compliance, Tax Avoidance, and Financial Reporting Quality on Company Financial Performance: A Case Study on a Manufacturing Company in Indonesia

This research investigates the intricate relationships among tax compliance, tax avoidance, financial reporting quality, and corporate financial performance in Indonesian manufacturing companies. Employing a quantitative approach, data were collected from 150 companies and analyzed using Structural Equation Modeling with Partial Least Squares (SEM-PLS). The study found robust positive associations between tax compliance and financial reporting quality with corporate financial performance. Furthermore, a nuanced relationship emerged between tax avoidance and financial performance, highlighting the importance of moderation. The empirical findings offer valuable insights for practitioners, policymakers, and academics seeking to understand the dynamics of tax-related activities and financial outcomes in the Indonesian manufacturing sector.

Event‐ driven‐observer‐based adaptive distributed sliding mode fault compensation control for interconnected nonlinear systems

AbstractIn this paper, an event‐driven‐observer‐based distributed adaptive sliding mode fault compensation control method is presented for a class of nonlinear interconnected systems in the occurrence of actuator faults and disturbances. First, a novel event‐based estimator is designed to acquire the state, fault, and lumped disturbance information. Then, by utilizing the observer outputs, an adaptive tracking‐error‐based event condition is established, based on which a distributed sliding mode fault compensation controller is designed. In the proposed control framework, on the one hand, to enhance the robustness and tracking accuracy, two adaptive parameters are constructed; on the other hand, an adaptive event‐based control mechanism is integrated to save the transmission resources. The Zeno phenomenon avoidance performance is analyzed and, finally, the applications to an inverted pendulum system and a machine power system are given to verify the fault tolerance ability of the proposed method.

Role of Tax Avoidance and Financial Performance to Increase Firm Value

The objective of this research is to determine the role of Tax Avoidance and financial performance in improving the firm value of the manufacture companies listed in Indonesia Stock Exchange (BEI). Financial report data in this research is 35 data from seven manufacturing industry companies, five years period 2017 until 2021. This study used a quantitative approach to testing hypotheses used the technique of path analysis performed through linear regression analysis. The result of this research is Tax Avoidance did not give influence to financial performance, and also did not give influence to increase firm value. The result of this research is Tax Avoidance which gives influence to increase firm value. Investors can see the three variables as a consideration for investment decision making.

Enhancing IoT Connectivity and Services for Worldwide Ships through Multi-Region Fog Cloud Architecture Platforms

As technologies such as eco-friendly ships, electric propulsion vessels, and multi-fuel propulsion systems advance, the scope of IoT applications in maritime fields is expanding, resulting in increased complexity in control factors. The gradual progression towards Maritime Autonomous Surface Ships (MASS) is further driving the evolution of ship-based IoT applications. These advancements underscore the necessity for a platform capable of ensuring reliable connectivity between ships and onshore. The limitations of the existing single cloud architecture become evident in this context. In response to these emerging challenges, this paper presents a cloud-based data platform structure anchored in the architecture of a multi-region fog cloud. Concurrently, we propose a strategic approach aimed at enhancing collision avoidance performance. This is achieved through the seamless sharing of navigation plan data among ships facilitated by the proposed platform structure. In regions densely populated with ships, there looms a potential for packet loss as data traffic sharing intensifies through the platform. To address this concern, we devised a traffic model based on the AIS data generation cycle and proposed an algorithm for subscription decision. Subsequently, we conducted comparative analyses of packet loss probabilities between the single cloud structure and the multi-region fog cloud structure. This was achieved through experimental packet loss data collected via the AWS cloud. Simulation results underscored a notable difference: with 100 subscribed ships, the packet loss probability in regions assuming a single cloud was about 28 times higher compared to the same region within the multi-region fog cloud structure. These simulations affirm the stable and effective implementation of the proposed collision avoidance performance enhancement method within the multi-region fog cloud structure. Furthermore, feasibility was corroborated through the successful implementation of the proposed platform via the AWS Cloud.

Directly-trained Spiking Neural Networks for Deep Reinforcement Learning: Energy efficient implementation of event-based obstacle avoidance on a neuromorphic accelerator

Spiking Neural Networks (SNN) promise extremely low-power and low-latency inference on neuromorphic hardware. Recent studies demonstrate the competitive performance of SNNs compared with Artificial Neural Networks (ANN) in conventional classification tasks.In this work, we present an energy-efficient implementation of a Reinforcement Learning (RL) algorithm using SNNs to solve an obstacle avoidance task performed by an Unmanned Aerial Vehicle (UAV), taking a Dynamic Vision Sensor (DVS) as event-based input. We train the SNN directly, improving upon state-of-art implementations based on hybrid (not directly trained) SNNs. For this purpose, we devise an adaptation of the Spatio-Temporal Backpropagation algorithm (STBP) for RL. We then compare the SNN with a state-of-art Convolutional Neural Network (CNN) designed to solve the same task. To this aim, we train both networks by exploiting a photorealistic training pipeline based on AirSim. To achieve a realistic latency and throughput assessment for embedded deployment, we designed and trained three different embedded SNN versions to be executed on state-of-art neuromorphic hardware, targeting state-of-the-art.We compared SNN and CNN in terms of obstacle avoidance performance showing that the SNN algorithm achieves better results than the CNN with a factor of 6× less energy. We also characterize the different SNN hardware implementations in terms of energy and spiking activity.

A Motion Planning Method for Robots Based on DMPs and Modified Obstacle-Avoiding Algorithm

This paper addresses the motion planning of the manipulator in task space. To improve the overall trajectory performance, a special motion planning method based on DMPs (Dynamic Movement Primitives) and the modified obstacle-avoiding algorithm is proposed. The proposed method solves the problems of trajectory jitter and inability to avoid obstacles in some scenarios, which are faced by the scheme of steering angle. Besides, it improves the retention of teaching intentions, reduces the loss of free space, and helps the system adapt to the dynamic environment. At the theoretical level, the convergence of the target state has been proved using Lyapunov stability theory-based analysis. The availability of the proposed method is validated and analyzed by performing a series of numerical simulations and Baxter robot experiments. The results indicate that the proposed method can provide reliable solutions for motion planning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —From the perspective of demonstration learning, this paper aims to elevate the motion planning of the manipulator in task space, especially in improving the obstacle avoidance performance. The existing DMPs-based motion planning algorithm uses the scheme of steering angle to achieve obstacle avoidance, and the obstacle is regarded as a mesh of points on the boundary. The obstacle-avoiding performance is limited. How to combine the obstacle-avoiding algorithm with the DMPs-based motion planning algorithm more effectively, so as to simultaneously achieve retaining teaching intentions as much as possible, still faces challenges. This paper designs a modified obstacle-avoiding algorithm, which solves the problems of trajectory jitter and inability to avoid obstacles in some circumstances, and improves the retention of teaching intentions. The modified obstacle-avoiding algorithm is also applied to the dynamic environment. The proposed method satisfies Lyapunov stability theory-based analysis, and the experiments on Baxter robot verify the feasibility.

Robot Path Planning Research Incorporating Improved A* Algorithm and DWA Algorithm

For the traditional A* algorithm has problems such as long paths, large number of nodes, and the demand for dynamic obstacle cannot be avoided in complex environment. A mobile robot dynamic path avoidance method will be improved to improve the A * algorithm and improve DWA algorithm Two map environments are used for simulation verification. First, the evaluation function and key node selection strategy are optimized for the A* algorithm, and redundant nodes are deleted; then the dynamic obstacle distance evaluation function is added to the DWA algorithm which for the purpose of the obstacle avoidance performance can be enhanced. The results about the improved A* algorithm reduces 12.20% and 58.33% in path length and number of turning points respectively compared with the traditional A* algorithm can be obviously grasped by the simulation experiment; by using the fusion algorithm whose purpose of using arcs instead of the straight lines is to turn more smoothly, and can be closest to the global optimum while avoiding dynamic obstacles to complete the search.