Robots In Dynamic Environments Research Articles

Performance Test, Index System Establishment, and Comprehensive Evaluation of Earthquake Rescue Robots

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for a comprehensive evaluation method that encompasses establishing an evaluation index system, testing performance indexes, and conducting performance evaluation. Firstly, four main criterion and twenty-three sub-criterion indexes are established by conducting a comprehensive review of existing assessment measures for rescue robots across diverse domains. These indexes are validated through test modules developed by the National Earthquake Response Support Service to obtain corresponding values for each criterion. Moreover, a method for establishing the index system is proposed based on the fuzzy clustering analysis and grey correlation analysis methods. This method effectively addresses issues related to excessive subjectivity, redundancy, and ambiguous stratification of indexes. Subsequently, the DEMATEL is employed to scrutinize the interrelationships and causal connections among each index within the established index system, leading to the identification of input and output indexes based on the analysis outcomes. Finally, as an empirical example, three earthquake rescue robots are comprehensively evaluated and ranked using the super efficiency DEA model. Alongside analyzing results regarding input redundancy and output deficiency, targeted improvement suggestions are provided for each earthquake rescue robot. Additionally, comparison analysis with the entropy weight method and VIKOR method verifies the effectiveness of our proposed method.

Comprehensive Performance Evaluation of Earthquake Search and Rescue Robots Based on Improved FAHP and Radar Chart

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for an evaluation method that quantifies their performance and facilitates the selection of rescue robots with optimal overall capabilities. In this paper, twenty-two evaluation criteria are proposed based on a comprehensive review of existing evaluation criteria for rescue robots across various domains. The evaluation criteria are tested using the test modules developed by the National Earthquake Response support service, obtaining the corresponding values for each criterion. Then, the weights of the criterion layer and comprehensive evaluation index are determined based on the analytical hierarch process and trapezoidal fuzzy number complementary judgment matrix, and a new consistency test method is proposed. The qualitative evaluation and quantitative analysis are effectively combined to overcome the subjective influence of expert decision-making. Additionally, the performance of three earthquake search and rescue robots is comprehensively evaluated and ranked using the improved radar chart method as an empirical example. Finally, the robustness of the ranking results is examined using a weight sensitivity analysis. The results of the sensitivity analysis demonstrate the effectiveness and feasibility of the proposed method, thereby providing valuable insights for developing multi-objective optimization control strategies and structural designs for earthquake search and rescue robots.

Autonomous Driving of Mobile Robots in Dynamic Environments Based on Deep Deterministic Policy Gradient: Reward Shaping and Hindsight Experience Replay.

In this paper, we propose a reinforcement learning-based end-to-end learning method for the autonomous driving of a mobile robot in a dynamic environment with obstacles. Applying two additional techniques for reinforcement learning simultaneously helps the mobile robot in finding an optimal policy to reach the destination without collisions. First, the multifunctional reward-shaping technique guides the agent toward the goal by utilizing information about the destination and obstacles. Next, employing the hindsight experience replay technique to address the experience imbalance caused by the sparse reward problem assists the agent in finding the optimal policy. We validated the proposed technique in both simulation and real-world environments. To assess the effectiveness of the proposed method, we compared experiments for five different cases.

A new path planning strategy integrating improved ACO and DWA algorithms for mobile robots in dynamic environments.

This article is concerned with the path planning of mobile robots in dynamic environments. A new path planning strategy is proposed by integrating the improved ant colony optimization (ACO) and dynamic window approach (DWA) algorithms. An improved ACO is developed to produce a globally optimal path for mobile robots in static environments. Through improvements in the initialization of pheromones, heuristic function, and updating of pheromones, the improved ACO can lead to a shorter path with fewer turning points in fewer iterations. Based on the globally optimal path, a modified DWA is presented for the path planning of mobile robots in dynamic environments. By deleting the redundant nodes, optimizing the initial orientation, and improving the evaluation function, the modified DWA can result in a more efficient path for mobile robots to avoid moving obstacles. Some simulations are conducted in different environments, which confirm the effectiveness and superiority of the proposed path planning algorithms.

Real-time Trajectory Planning and Tracking Control of Bionic Underwater Robot in Dynamic Environment.

In this article, we study the trajectory planning and tracking control of a bionic underwater robot under multiple dynamic obstacles. We first introduce the design of the bionic leopard cabinet underwater robot developed in our lab. Then, we model the trajectory planning problem of the bionic underwater robot by combining its dynamics and physical constraints. Furthermore, we conduct global trajectory planning for bionic underwater robots based on the temporal-spatial Bezier curves. In addition, based on the improved proximal policy optimization, local dynamic obstacle avoidance trajectory replanning is carried out. In addition, we design the fuzzy proportional-integral-derivative controller for tracking control of the planned trajectory. Finally, the effectiveness of the real-time trajectory planning and tracking control method is verified by comparative simulation in dynamic environment and semiphysical simulation of UWSim. Among them, the real-time trajectory planning method has advantages in trajectory length, trajectory smoothness, and planning time. The error of trajectory tracking control method is controlled around 0.2 m.

Model-Free Safety Critical Model Predictive Control for Mobile Robot in Dynamic Environments

Model-Free Safety Critical Model Predictive Control for Mobile Robot in Dynamic Environments

Path Planning of a Mobile Robot for a Dynamic Indoor Environment Based on an SAC-LSTM Algorithm.

This paper proposes an improved Soft Actor-Critic Long Short-Term Memory (SAC-LSTM) algorithm for fast path planning of mobile robots in dynamic environments. To achieve continuous motion and better decision making by incorporating historical and current states, a long short-term memory network (LSTM) with memory was integrated into the SAC algorithm. To mitigate the memory depreciation issue caused by resetting the LSTM's hidden states to zero during training, a burn-in training method was adopted to boost the performance. Moreover, a prioritized experience replay mechanism was implemented to enhance sampling efficiency and speed up convergence. Based on the SAC-LSTM framework, a motion model for the Turtlebot3 mobile robot was established by designing the state space, action space, reward function, and overall planning process. Three simulation experiments were conducted in obstacle-free, static obstacle, and dynamic obstacle environments using the ROS platform and Gazebo9 software. The results were compared with the SAC algorithm. In all scenarios, the SAC-LSTM algorithm demonstrated a faster convergence rate and a higher path planning success rate, registering a significant 10.5 percentage point improvement in the success rate of reaching the target point in the dynamic obstacle environment. Additionally, the time taken for path planning was shorter, and the planned paths were more concise.

Developing a dynamic obstacle avoidance system for autonomous mobile robots using Bayesian optimization and object tracking: Implementation and testing

This study addresses the challenges faced by autonomous mobile robots in dynamic environments where moving obstacles such as people, cars, and other mobile robots are prevalent. Current motion planning methods for mobile robots focus on stationary obstacles or equivalent static ones at each control moment, resulting in collisions with moving objects or the need for longer distances to avoid them. To address this issue, we propose a system that incorporates dynamic information about moving obstacles into the motion planning decision-making process. We developed an object tracking technique using 2D LiDARs to gather this information and optimized the relevant parameters using Bayesian optimization. The dynamic information of moving obstacles is saved in a costmap representation at each instant, allowing the robot to yield or ignore confronting obstacles based on the prediction of who shall pass first. Simulation and real-world testing demonstrate that our approach saves paths for mobile robots in dynamic environments and prevents them from colliding with dynamic obstacles, improving the practicality of mobile robots working alongside moving humans.

A safe reinforcement learning approach for autonomous navigation of mobile robots in dynamic environments

AbstractWhen deploying mobile robots in real‐world scenarios, such as airports, train stations, hospitals, and schools, collisions with pedestrians are intolerable and catastrophic. Motion safety becomes one of the most fundamental requirements for mobile robots. However, until now, efficient and safe robot navigation in such dynamic environments is still an open problem. The critical reason is that the inconsistency between navigation efficiency and motion safety is greatly intensified by the high dynamics and uncertainties of pedestrians. To face the challenge, this paper proposes a safe deep reinforcement learning algorithm named Conflict‐Averse Safe Reinforcement Learning (CASRL) for autonomous robot navigation in dynamic environments. Specifically, it first separates the collision avoidance sub‐task from the overall navigation task and maintains a safety critic to evaluate the safety/risk of actions. Later, it constructs two task‐specific but model‐agnostic policy gradients for goal‐reaching and collision avoidance sub‐tasks to eliminate their mutual interference. Then, it further performs a conflict‐averse gradient manipulation to address the inconsistency between two sub‐tasks. Finally, extensive experiments are performed to evaluate the superiority of CASRL. Simulation results show an average 8.2% performance improvement over the vanilla baseline in eight groups of dynamic environments, which is further extended to 13.4% in the most challenging group. Besides, forty real‐world experiments fully illustrated that the CASRL could be successfully deployed on a real robot.

Smooth Autonomous Patrolling for a Differential-Drive Mobile Robot in Dynamic Environments.

Today, mobile robots have a wide range of real-world applications where they can replace or assist humans in many tasks, such as search and rescue, surveillance, patrolling, inspection, environmental monitoring, etc. These tasks usually require a robot to navigate through a dynamic environment with smooth, efficient, and safe motion. In this paper, we propose an online smooth-motion-planning method that generates a smooth, collision-free patrolling trajectory based on clothoid curves. Moreover, the proposed method combines global and local planning methods, which are suitable for changing large environments and enabling efficient path replanning with an arbitrary robot orientation. We propose a method for planning a smoothed path based on the golden ratio wherein a robot's orientation is aligned with a new path that avoids unknown obstacles. The simulation results show that the proposed algorithm reduces the patrolling execution time, path length, and deviation of the tracked trajectory from the patrolling route compared to the original patrolling method without smoothing. Furthermore, the proposed algorithm is suitable for real-time operation due to its computational simplicity, and its performance was validated through the results of an experiment employing a differential-drive mobile robot.

Optimal predictive neuro-navigator design for mobile robot navigation with moving obstacles.

Introduction: The challenge of navigating a Mobile robot in dynamic environments has grasped significant attention in recent years. Despite the available techniques, there is still a need for efficient and reliable approaches that can address the challenges of real-time near optimal navigation and collision avoidance. Methods: This paper proposes a novel Log-concave Model Predictive Controller (MPC) algorithm that addresses these challenges by utilizing a unique formulation of cost functions and dynamic constraints, as well as a convergence criterion based on Lyapunov stability theory. The proposed approach is mapped onto a novel recurrent neural network (RNN) structure and compared with the CVXOPT optimization tool. The key contribution of this study is the combination of neural networks with model predictive controller to solve optimal control problems locally near the robot, which offers several advantages, including computational efficiency and the ability to handle nonlinear and complex systems. Results: The major findings of this study include the successful implementation and evaluation of the proposed algorithm, which outperforms other methods such as RRT, A-Star, and LQ-MPC in terms of reliability and speed. This approach has the potential to facilitate real-time navigation of mobile robots in dynamic environments and ensure a feasible solution for the proposed constrained-optimization problem.

Path Planning in Dynamic Environments Based on Q-Learning

With the rapid progress of science and technology, the scope of applications for mobile robots is growing. Path planning in dynamic environment is always a challenging task for mobile robot, which shows significant impacts in the medical field and the military field. Q-learning, a model-free reinforcement learning algorithm, can recognize its surroundings and demonstrate a system making decisions for itself about how this algorithm learns to make accurate decisions about achieving optimal target. Therefore, a path planning algorithm was proposed for robots in dynamic environments based on Q-learning. This algorithm can successfully generate several viable paths in environments with both static obstacles, dynamic obstacles and target point. First, three environments with different levels of complexity were created. Afterwards, to generate several optimal paths, the path planning algorithm was conducted in multiple times in different environments. Finally, the experimental results were collected and visualized to illustrate information. The effectiveness of the proposed algorithm is validated by experiment results in solving problems of path planning in dynamic environments with required point.

Path planning of mobile robots in dynamic environment based on analytic geometry and cubic Bézier curve with three shape parameters

With the improvement of technology, autonomous mobile robots have become an indispensable part of human life. Since path planning is the crucial part of mobile robots, these paths should be feasible, smooth and collision-free in a certain environment. However, forming desirable paths in a dynamic environment with static and moving obstacles is still a problem in the literature. In this paper, we present a new approach based on the analytic geometry for collision avoidance and the cubic Bézier curve with three shape parameters to obtain global and local paths in a dynamic environment. In this study, the environment for the mobile robot is considered to be a square plane without a grid consisting of several static obstacles in different geometric shapes, a mobile robot, and several dynamic obstacles in the shape of a circle with the same size. Moreover, we expressed the influence of the shape parameters on the optimal path using the objective function defined by the path length. Also, the paths generated by the cubic Bézier curve with three shape parameters and the classical cubic Bézier curve are compared in terms of their path length.

Semantic SLAM for mobile robots in dynamic environments based on visual camera sensors

Visual simultaneous localization and mapping (vSLAM) is inherently constrained by the static world assumption, which renders success in the presence of dynamic objects rather challenging. In this paper, we propose a real-time semantic vSLAM system designed for both indoor and outdoor dynamic environments. By employing object detection, we identify 80 categories and utilize motion consistency checks to pinpoint outliers in each image. Distinct methods are presented for examining the motion states of humans and other objects. For detected humans, an algorithm is introduced to assess whether an individual is seated, subsequently dividing the bounding boxes of seated individuals into two parts based on human body proportions. We then use the same threshold values for standing individuals to determine the states of the two boxes belonging to seated individuals. For non-human objects, we propose an algorithm capable of automatically adjusting the threshold values for different bounding boxes, thereby ensuring consistent detection performance across various objects. Ultimately, we retain points within static boxes contained in dynamic boxes while eliminating other points in dynamic boxes to benefit from a larger number of detected categories. Our SLAM is evaluated on indoor TUM and Bonn RGB-D datasets, with further testing conducted on the outdoor stereo KITTI dataset. The results reveal that our SLAM outperforms most SLAM systems in dynamic environments. Moreover, we test our system in real-world environments with a monocular camera, demonstrating its robustness and universality across diverse settings.

Machine Learning-Based Agoraphilic Navigation Algorithm for Use in Dynamic Environments with a Moving Goal

This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal.

Flexible Behavioral Decision Making of Mobile Robot in Dynamic Environment

How to achieve flexible behavioral decision making in a dynamic environment is an important prerequisite for a mobile robot to execute various tasks. Traditional methods suffer from discontinuous and unstable learning ability. This article brings the mobile robot continuous and stable learning ability in the dynamic environment, through improving the binding manner between the cerebellum and basal ganglia in the neuromodulatory system proposed in our previous work, thus to enhance its flexible behavioral decision-making performance. Moreover, a more biological significance index, i.e., the curiosity index, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Cur$ </tex-math></inline-formula> , is designed to mimic the activity switch of the locus coeruleus between the tonic and phasic mode, to modulate the exploration-exploitation tradeoff in the reinforcement learning (RL) of the basal ganglia. Influence of varying learning rate and varying discount rate on the performance of the RL is also investigated. The experiment results in static and dynamic environments, as well as the comparative experiment in the complex maze environment and the complex dynamic environment, demonstrate the potential of the proposed neuromodulatory system.

Policy Optimization to Learn Adaptive Motion Primitives in Path Planning With Dynamic Obstacles

This letter addresses the kinodynamic motion planning for non-holonomic robots in dynamic environments with both static and dynamic obstacles – a challenging problem that lacks a universal solution yet. One of the promising approaches to solve it is decomposing the problem into the smaller sub-problems and combining the local solutions into the global one. The crux of any planning method for non-holonomic robots is the generation of motion primitives that generates solutions to local planning sub-problems. In this work we introduce a novel learnable steering function (policy), which takes into account kinodynamic constraints of the robot and both static and dynamic obstacles. This policy is efficiently trained via the policy optimization. Empirically, we show that our steering function generalizes well to unseen problems. We then plug in the trained policy into the sampling-based and lattice-based planners, and evaluate the resultant POLAMP algorithm (Policy Optimization that Learns Adaptive Motion Primitives) in a range of challenging setups that involve a car-like robot operating in the obstacle-rich parking-lot environments. We show that POLAMP is able to plan collision-free kinodynamic trajectories with success rates higher than 92%, when 50 simultaneously moving obstacles populate the environment showing better performance than the state-of-the-art competitors.

Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data

Scene understanding is crucial for autonomous robots in dynamic environments for making future state predictions, avoiding collisions, and path planning. Camera and LiDAR perception made tremendous progress in recent years, but face limitations under adverse weather conditions. To leverage the full potential of multi-modal sensor suites, radar sensors are essential for safety critical tasks and are already installed in most new vehicles today. In this paper, we address the problem of semantic segmentation of moving objects in radar point clouds to enhance the perception of the environment with another sensor modality. Instead of aggregating multiple scans to densify the point clouds, we propose a novel approach based on the self-attention mechanism to accurately perform sparse, single-scan segmentation. Our approach, called Gaussian Radar Transformer, includes the newly introduced Gaussian transformer layer, which replaces the softmax normalization by a Gaussian function to decouple the contribution of individual points. To tackle the challenge of the transformer to capture long-range dependencies, we propose our attentive up- and downsampling modules to enlarge the receptive field and capture strong spatial relations. We compare our approach to other state-of-the-art methods on the RadarScenes data set and show superior segmentation quality in diverse environments, even without exploiting temporal information.

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

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

Motion Saliency-Based Collision Avoidance for Mobile Robots in Dynamic Environments

Obstacle avoidance is a sizable challenge for robots working in a multiple dynamic obstacle environment. Conventional obstacle avoidance methods often require complex calculations to process all dynamic obstacles detected in the scene. Avoiding dangerous moving objects in time is often difficult when obstacles are many. In this article, we propose a robot obstacle avoidance method based on motion saliency for dynamic environments. First, we use segmented dynamic objects to calculate the saliency of dynamic objects and segment dangerous dynamic objects. Then, we use the B-spline curve to predict the movement of dangerous dynamic objects and combine it with the nonlinear model predictive control method to avoid dangerous obstacles in the dynamic environment of the robot. Considering the motion behavior of different dynamic objects, our obstacle avoidance strategy is to generate obstacle-free paths by adjusting the speed of the robot or connecting the centers of the rolling variable-size circles. Finally, we conduct a series of experiments to verify the effectiveness of our method.