Topological drone navigation in uncertain environment

Aiming at the problem of multi-objective path planning (MOPP) and dynamic obstacle avoidance of manipulator in dynamic environment, this paper proposes a solution based on hierarchical reinforcement learning (HRL) framework. Traditional path planning methods have some problems in dynamic scenes, such as poor real-time performance, difficult to balance multi-objective conflicts and insufficient adaptability to environmental changes. Therefore, this paper designs a two-tier architecture including global path planning layer and local obstacle avoidance layer, which is trained by Proximal policy optimization (PPO) and Soft Actor-Critic (SAC) algorithms respectively, and achieves collaborative optimization through an efficient information exchange mechanism between layers. At the same time, a multi-objective reward function based on dynamic weight adjustment strategy is introduced. Fuzzy logic is used to adaptively balance the relationship among path length, obstacle avoidance safety and energy consumption according to environmental complexity. Combined with Long Short-Term Memory (LSTM), the trajectory of obstacles is predicted, and the potential field method is further introduced to modify the obstacle avoidance reward, which improves the real-time response ability and robustness of the algorithm in dynamic environment. The experimental results show that the HRL-SAC-PPO method proposed in this paper shows superior performance in both static and dynamic scenarios. In the static scene, the success rate of this method reaches 100%, the average path length is shortened to 2.13m, no collision occurs, and the energy consumption is reduced to 1.12 kJ, which shows a good multi-objective optimization effect. In the dynamic scene, the trajectory error of obstacles predicted by LSTM is only 4.2%, and the safe distance between the robot arm and obstacles is improved by 35%, which significantly enhances the reliability of obstacle avoidance. In addition, the average decision delay of this method is only 11.3ms, and the peak delay is 23ms, which is much lower than that of the contrast algorithm, showing stronger real-time response ability. The ablation experiment further verified the key role of LSTM trajectory prediction, dynamic weight adjustment and layered structure on the overall performance. In the welding task verification of the real UR5 manipulator, the success rate of the system in dynamic environment is 95.3%, the average path length is 3.41m, and the maximum joint acceleration is only 0.87 radian/s², which is far below the safety threshold, indicating that the algorithm has good stability and obstacle avoidance ability in practical application. The comprehensive performance comparison shows that this method performs well in different industrial scenarios, and has stronger environmental adaptability and comprehensive path planning ability.

This paper analyzes the inapplicability of the traditional APF in the dynamic environment and the existence of local minimum. In this paper, a new potential field function is proposed. Combined with the fuzzy algorithm, the ratio of the absolute value of the difference between the gravitational force and the repulsive force of the UAV to the resultant force and their angle difference are taken as two fuzzy inputs. With the help of the fuzzy rules, the fuzzy output force is calculated to break the local minimum value.in the dynamic environment, the relative velocity repulsion potential field between UAV and dynamic obstacles is introduced to solve the problem of dynamic obstacle avoidance. The experimental simulation results show that the obstacle avoidance track drawn by the improved APF regulation in this paper is smoother and the obstacle avoidance track correlation data is better.

CoNi-MPC [1] provides an efficient framework for UAV control in air-ground cooperative tasks by relying exclusively on relative states, eliminating the need for global state estimation. However, its lack of environmental information poses significant challenges for obstacle avoidance. To address this issue, we propose a novel obstacle avoidance algorithm, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co</b>operative <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b>on-<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</b>nertial frame-based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b>bstacle <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b>voidance (CoNi-OA), designed explicitly for UAV-UGV cooperative scenarios without reliance on global state estimation or obstacle prediction. CoNi-OA uniquely utilizes a single frame of raw LiDAR data from the UAV to generate a modulation matrix, which directly adjusts the quadrotor's velocity to achieve obstacle avoidance. This modulation-based method enables real-time generation of collision-free trajectories within the UGV's non-inertial frame, significantly reducing computational demands (less than 5 ms per iteration) while maintaining safety in dynamic and unpredictable environments. The key contributions of this work include: (1) a modulation-based obstacle avoidance algorithm specifically tailored for UAV-UGV cooperation in non-inertial frames without global states; (2) rapid, real-time trajectory generation based solely on single-frame LiDAR data, removing the need for obstacle modeling or prediction; and (3) adaptability to both static and dynamic environments, thus extending applicability to featureless or unknown scenarios.

The physical characteristics of the wireless channel can be exploited to secure communication within Internet-of-Things (IoT) networks. Several techniques have been proposed in the literature; however, the feasibility of such techniques in IoT environments remains unclear. In some cases IoT devices may be deployed in static environments wherein the channel coherence time is too high to generate keys at a faster rate and with the desired randomness. On the other hand, in some cases IoT devices are deployed in dynamic environments wherein the channel properties changes very fast over the time. Key generation schemes proposed for one environment are not directly applicable to the other. While low key generation rate (KGR) is an issue for static environments, higher values of bit error rate (BER) becomes a bottleneck in dynamic environments. This paper proposes a mapping-table based key distribution scheme for IoT networks deployed in both static and dynamic environments. In the proposed scheme, the channel magnitude is exploited in the key generation process. The proposed scheme is immune to channel estimation errors, while providing sufficient randomness in the static environment.

This paper introduces a novel control strategy for robotic obstacle avoidance by integrating Gradient Tracking Integral Sliding Mode Control (GT-ISMC) with Artificial Potential Fields (APFs). This approach aims to enhance autonomous navigation's robustness and accuracy in dynamic and uncertain environments. Traditional obstacle avoidance methods struggle with local minima and lack sufficient robustness against external disturbances and model uncertainties. The proposed method employs APFs to create smooth repulsive forces to avoid obstacles and attract the robot to the target. The novelty lies in designing an integral sliding mode controller to track the APF gradients, improving robustness against disturbances and uncertainties in the robot's dynamics, reducing chattering and improving convergence performance. Stability analysis guarantees the convergence of the robot to the desired reference while evading the obstacle. Simulations and experimental results demonstrate that the GT-ISMC with APFs effectively navigates complex environments, offering superior robustness, faster convergence, and smoother trajectories compared to conventional PID, sliding mode, and Potential Field methods. This work contributes to the advancement of autonomous robotic control by providing a reliable and efficient method for real-time obstacle avoidance, with potential applications in mobile robotics and industrial automation. Highlights A novel control strategy for robotic obstacle avoidance by integrating Gradient Tracking Integral Sliding Mode Control (GT-ISMC) with Artificial Potential Fields (APFs) is proposed. The controller enhances the robustness and accuracy of autonomous navigation in dynamic and uncertain environments, where some motion constraints are considered. The proposed method employs APFs to generate smooth, continuous repulsive forces that guide the robot away from obstacles while attracting it toward the target. By incorporating gradient tracking into the sliding mode framework, the system benefits from reduced chattering and improved convergence performance.

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.

To solve the real-time path planning of multi-robots in complex environments, a new immune planning algorithm incorporating a specific immune mechanism is presented. In the immune planning algorithm incorporating a specific immune mechanism, a new coding format for an antibody is first defined according to the impact of the obstacle distribution on the obstacle avoidance behaviors of multi-robots. Then, a new robot immune dynamic model for antibody selection is designed in terms of different impacts of obstacles and targets on robot behaviors. Finally, aiming at the local minimum problem in complex environments and inspired by the specific immune mechanism, a series of appropriate avoidance behaviors are selected through the calculation of a specific immune mechanism to help robots walk out of local minima. In addition, to solve deadlock situations, a learning strategy for the antibody concentration is presented. Compared with four related immune planning algorithms—an improved artificial potential field, a rapidly exploring random tree algorithm, a D* algorithm and a A* algorithm—the simulation results in four static environments show that the paths planned by immune planning algorithm incorporating a specific immune mechanism are the shortest and the path smoothness is generally the highest, which shows its strong planning capability in multi-obstacle environments. The simulation result in a dynamic environment with local minima shows that the immune planning algorithm incorporating a specific immune mechanism has strong planning ability in dynamic obstacle avoidance and in escaping from local minima. Additionally, an experiment in a multi-robot environment shows that two robots can not only avoid static obstacles but also avoid dynamic obstacles, which further supports the validity of the proposed immune planning algorithm incorporating a specific immune mechanism for multi-robots in real environments.

ABSTRACTAddressing the challenge of dynamic obstacle avoidance for drones in uncertain environments, this paper introduces an innovative algorithm known as Velocity and Position Chance Constrained Model Predictive Control with Control Barrier Functions (CCVP‐MPC‐CBF). This algorithm comprehensively takes into account the probabilistic constraints of relative positions and velocities between drones and obstacles, introducing the concept of collision probability and establishing corresponding thresholds. Furthermore, by incorporating Control Barrier Functions (CBF) as constraint conditions, the algorithm ensures the safety and stability of the system. This strategy effectively overcomes the limitations that traditional MPCs may encounter when dealing with uncertainties in real‐world systems, maintaining the invariance of the system's set while achieving optimal performance under the premise of ensuring system safety. Additionally, the method has been extended to enable collaborative obstacle avoidance among multiple drones. Finally, through comprehensive simulation experiments, it has been verified that even in complex environments, this algorithm enables drones to effectively avoid collisions with multiple dynamic obstacles, demonstrating the effectiveness and reliability of this approach.

Cooperative path planning for unmanned aerial vehicle (UAV) swarms has attracted considerable research attention, yet it remains challenging in complex, uncertain environments. To tackle this problem, we model the cooperative path planning task as a heterogeneous decentralized Markov Decision Process (MDP), emphasizing the symmetry-inspired role assignment between leader and wingmen UAVs, which ensures balanced and coordinated behaviors in dynamic settings. We address the problem using a Multi-Agent Soft Actor-Critic (MASAC) framework enhanced with a symmetry-aware reward mechanism designed to optimize multiple cooperative objectives: simultaneous arrival, formation topology preservation, dynamic obstacle avoidance, trajectory smoothness, and inter-agent collision avoidance. This design promotes behavioral symmetry among agents, enhancing both coordination efficiency and system robustness. Simulation results demonstrate that our method achieves efficient swarm coordination and reliable obstacle avoidance. Quantitative evaluations show that our MASAC-CA algorithm achieves a Mission Success Rate (MSR) of 99.0% with 2–5 wingmen, representing approximately 13% improvement over baseline MASAC, while maintaining Formation Keeping Rates (FKR) of 59.68–26.29% across different swarm sizes. The method also reduces collisions to near zero in cluttered environments while keeping flight duration, path length, and energy consumption at levels comparable to baseline algorithms. Finally, the proposed model’s robustness and effectiveness are validated in complex uncertain environments, underscoring the value of symmetry principles in multi-agent system design.

In complex dynamic environments, robot path planning faces challenges in multi‐objective optimization, such as path length, smoothness and obstacle avoidance capability. To address this, this paper proposes an improved sparrow search algorithm based on chaotic initialization and the golden positive cosine strategy for multi‐objective path planning. Diverse initial populations are generated through chaotic mapping to enhance global search capability and avoid falling into local optima. The golden positive cosine strategy optimizes individual position updates to accelerate convergence and ensure path smoothness. Results demonstrate that the proposed method outperforms sparrow search algorithm (SSA), with improvements of 21.1% in path length, 16.3% in smoothness and 14.2% in obstacle avoidance capability. After achieving global path optimization, the improved dynamic window approach (IDWA) is employed for real‐time obstacle avoidance in dynamic environments, dynamically adjusting the window size based on robot speed, obstacle density and target distance to adaptively expand or shrink the search space, thereby improving obstacle avoidance flexibility and efficiency. Simulation results show that the proposed method surpasses SSA in terms of path length, smoothness, obstacle avoidance capability and computational efficiency in both static and dynamic environments.

Unmanned surface vehicle (USV) path planning is a crucial technology for achieving USV autonomous navigation. Under global path planning, dynamic local obstacle avoidance has become the primary focus for safe USV navigation. In this study, a USV autonomous dynamic obstacle avoidance method based on the enhanced velocity obstacle method is proposed in order to achieve path replanning. Through further analysis of obstacles, the obstacle geometric model set in the conventional velocity obstacle method was redefined. A special triangular obstacle geometric model was proposed to reconstruct the velocity obstacle region. The collision time was predicted by fitting the previously gathered data to the detected obstacle’s distance, azimuth, and other relevant data. Then, it is combined with the collision risk to determine when obstacle avoidance should begin and end. In order to ensure safe driving between path points, the international maritime collision avoidance rules (COLREGs) are incorporated to ensure the accuracy of obstacle avoidance. Finally, through numerical simulations of various collision scenarios, it was determined that, under the assumption of ensuring a safe encounter distance, the maximum change rates of USV heading angle are optimized by 17.54%, 58.16%, and 28.63% when crossing, head-on, and overtaking, respectively. The results indicate that, by optimizing the heading angle, the enhanced velocity obstacle method can avoid the risk of ship rollover caused by an excessive heading angle during high-speed movement and achieve more accurate obstacle avoidance action in the event of a safety encounter.

To address the issues of slow convergence speed, poor dynamic adaptability, and path redundancy in the Double Deep Q Network (DDQN) within complex obstacle environments, this paper proposes an enhanced algorithm within the deep reinforcement learning framework. This algorithm, termed LPDDQN, integrates Prioritized Experience Replay (PER) and the Long Short Term Memory (LSTM) network to improve upon the DDQN algorithm. First, Prioritized Experience Replay (PER) is utilized to prioritize experience data and optimize storage and sampling operations through the SumTree structure, rather than the conventional experience queue. Second, the LSTM network is introduced to enhance the dynamic adaptability of the DDQN algorithm. Owing to the introduction of the LSTM model, the experience samples must be sliced and populated. The performance of the proposed LPDDQN algorithm is compared with five other path planning algorithms in both static and dynamic environments. Simulation analysis shows that in a static environment, LPDDQN demonstrates significant improvements over traditional DDQN in terms of convergence, number of moving steps, success rate, and number of turns, with respective improvements of 24.07%, 17.49%, 37.73%, and 61.54%. In dynamic and complex environments, the success rates of all algorithms, except TLD3 and the LPDDQN, decreased significantly. Further analysis reveals that the LPDDQN outperforms the TLD3 by 18.87%, 2.41%, and 39.02% in terms of moving steps, success rate, and number of turns, respectively.

Autonomous mobile robot encompasses modules such as perception, path planning, decision-making, and control. Among these modules, path planning serves as a prerequisite for mobile robots to accomplish tasks. Enhancing path planning capability of mobile robots can effectively save costs, reduce energy consumption, and improve work efficiency. The primary slime mold algorithm (SMA) exhibits characteristics such as a reduced number of parameters, strong robustness, and a relatively high level of exploratory ability. SMA performs well in path planning for mobile robots. However, it is prone to local optimization and lacks dynamic obstacle avoidance, making it less effective in real-world settings. This paper presents an enhanced SMA (ESMA) path-planning algorithm for mobile robots. The ESMA algorithm incorporates adaptive techniques to enhance global search capabilities and integrates an artificial potential field to improve dynamic obstacle avoidance. Compared to the SMA algorithm, the SMA-AGDE algorithm, which combines the Adaptive Guided Differential Evolution algorithm, and the Lévy Flight-Rotation SMA (LRSMA) algorithm, resulted in an average reduction in the minimum path length of (3.92%, 8.93%, 2.73%), along with corresponding reductions in path minimum values and processing times. Experiments show ESMA can find shortest collision-free paths for mobile robots in both static and dynamic environments.

One of the ultimate goals of mobile robotics research is to build robots that can safely carry out missions in hazardous and populated environments. Most of today's commercial mobile devices scale poorly along this dimension. Their motion planning relies on accurate, static models of the environments, and therefore they often fail their mission if humans or other unpredictable obstacles block their path. To build autonomous mobile robots one has to build systems that can perceive their environments, react to unforeseen circumstances, and plan dynamically in order to achieve their mission. Thus, the objective of the motion planning and control problem is to find collision-free trajectories, in static or dynamic environments containing some obstacles, between a start and a goal configuration. It has attracted much research in recent years. In this context the term control has a broad meaning that includes many different controls, such as low-level motor control, and behaviour control, where behaviour represents many complicated tasks, like obstacle avoidance and goal seeking. This article describes an intelligent motion planning and navigation system for omnidirectional mobile robots based on fuzzy logic. Owing to its simplicity and hence its short response time, the fuzzy navigator is especially suitable for on-line applications with strong real-time requirements. On-line planning is an on-going activity. The planner receives a continuous flow of information about occurring events and generates new commands in response to the incoming events, while previously planned motions are being executed. The fuzzy-rule-base of the proposed system combines the repelling influence, which is related to the distance and the angle between the robot and nearby obstacles, with the attracting influence produced by the distance and the angular difference between the actual direction and position of the robot and the final configuration, to generate actuating commands for the mobile platform. It can be considered as an on-line local navigation method for omnidirectional mobile robots for the generation of instantaneous collision-free motions. This reactive system is especially suitable for real-time applications. The use of fuzzy logic leads to a transparent system which can be tuned by hand or by a set of learning rules. Furthermore, this approach allows obstacle avoidance and navigation in dynamic environments. The functioning of the fuzzy motion planner with respect to omnidirectional mobile robots and results of simulated experiments are presented.

The traditional Rapidly-exploring Random Tree Star (RRT*) suffers from the low path generation efficiency, numerous invalid exploration points, and unsuitability for navigation in unknown dynamic environments. In this article, we propose a dynamic path planning scheme by combining the improved RRT* and the improved dynamic window approach (DWA). For pregenerating an initial path, we use the artificial potential field (APF) method to expand new nodes. The adaptive dynamic step-size is introduced for accelerating the optimal path searching. Moreover, the improved ant colony algorithm is used to perform multiobjective optimization on the generated initial path. When unknown obstacles appear in the path, the improved DWA is developed for obstacle avoidance. Finally, the proposed method is validated by simulation and experiment in both of the static and dynamic environments. In particular, the simulation results show that, compared with some existing methods, our algorithm can generate a higher-quality initial path in the static environment and avoid unknown dynamic obstacles effectively in the dynamic environment. Furthermore, we implement our algorithm in a mobile robot to verify the correctness and effectiveness in the practical scenario.