Search Results (334)

Multi-robot systems (MRS) have attracted a lot of attention from researchers due to their widespread application in various environments. However, in multi-robot collaborative search tasks, two problems often arise: sparse rewards for capturing targets and control oscillations. To address these issues, this paper proposes the hierarchical active perception multi-agent deep deterministic policy gradient (HAP-MADDPG) framework. This framework guides robots to efficiently explore maps and discover targets through global utility planning based on global exploration rate and local information aggregation based on local exploration rate. A stability control mechanism, which includes hysteresis logic and reward decay, is introduced to suppress control oscillations. Experimental results show that the HAP-MADDPG framework achieves a success rate of 96.25% and an average search time of 216.3 steps. The path trajectories are smooth, demonstrating the effectiveness of the proposed approach. Full article

Free-space multi-agent path planning remains challenging at large scales. Most existing methods either offer optimality guarantees but do not scale beyond a few dozen agents or rely on grid-world assumptions that do not generalize well to continuous space. In this paper, we propose a hybrid, rule-based planning framework that combines Priority Inheritance with Backtracking (PIBT) with a novel safety-aware path smoothing method. Our approach extends PiBT to eight-connected grids and selectively applies string-pulling-based smoothing while preserving collision safety through local interaction awareness and a fallback collision resolution step based on Safe Interval Path Planning (SIPP). This design allows us to reduce overall path lengths while maintaining real-time performance. We demonstrate that our method can scale to over 500 agents in large free-space environments, outperforming existing any-angle and optimal methods in terms of runtime, while producing near-optimal trajectories in sparse domains. Our results suggest this framework is a promising building block for scalable, real-time multi-agent navigation in robotics systems operating beyond grid constraints. Full article

Aiming at the inherent defects of the Horned Lizard Optimization Algorithm (HLOA), such as insufficient global exploration capability, premature convergence to local optima, and inadequate balance between exploration and exploitation, this paper proposes an enhanced Improved Horned Lizard Optimization Algorithm (IHLOA) integrated with multi-strategy improvements. Firstly, the Fuch chaotic mapping is introduced for population initialization, which enhances the ergodicity and diversity of the initial population by leveraging the pseudo-random and aperiodic characteristics of chaotic sequences, laying a high-quality foundation for subsequent optimization searches. Secondly, the golden sine strategy is embedded into the iterative update process to dynamically adjust the search step size and direction. This strategy utilizes the periodic amplitude variation in the sine function and the golden section coefficient to balance the global exploration for potential optimal regions and local exploitation for refined optimization, thereby accelerating convergence speed while avoiding local stagnation. Finally, the orthogonal crossover strategy is incorporated in the late iteration stage to promote effective information interaction between parent and offspring populations. By means of chromosome segment exchange and elitist retention mechanisms, this strategy reduces dimensional search blind spots and further enhances the algorithm’s ability to capture high-quality solutions. Comprehensive experimental evaluations are conducted based on classical benchmark test functions and eight state-of-the-art meta-heuristic algorithms. The results demonstrate that the IHLOA outperforms comparative algorithms in terms of optimization accuracy, convergence speed, and stability across 30-D, 50-D, and 80-D scenarios. For practical path planning applications, the IHLOA achieves remarkable performance improvements: in single-goal path planning, it reduces the path length by 2.54–87.64% compared with benchmark algorithms; in multi-goal path planning, it realizes a 1.24–7.99% reduction in path length and an 11.91% average reduction in the number of turning points relative to the original HLOA. Additionally, the IHLOA exhibits excellent robustness and adaptability in dynamic obstacle environments, effectively shortening the path length and reducing robot stuck times. This research not only enriches the improvement framework of meta-heuristic algorithms but also provides a high-efficiency optimization solution for mobile robot path planning in complex environments. Full article

Efficient navigation in crowded and dynamic environments is crucial for robot integration into human spaces. AVOCADO (AdaptiVe Optimal Collision Avoidance Driven by Opinion) generates collision-free velocities using Velocity Obstacles and adaptation to the cooperation estimation among agents. However, it assumes holonomic motion and cannot handle non-holonomic constraints, such as those of differential-drive robots. We propose DD-AVOCADO, an extension of AVOCADO that incorporates differential-drive kinematics to compute feasible and safe velocities. The method combines AVOCADO-based planning with a non-holonomic controller and accounts for tracking errors to avoid collisions. Simulation results across diverse scenarios show a significant reduction in collisions and efficient navigation in scenarios with cooperative and non-cooperative agents, and hardware experiments demonstrate its applicability in robot platforms. The method has the potential to be applied to other dynamic models. Full article

To enhance the navigation efficiency and obstacle avoidance capability of omnidirectional mobile robots in unstructured and complex environments, this paper conducts research on intelligent path planning and management for X-type Mecanum-wheeled mobile robots with the improved Proximal Policy Optimization–Gated Recurrent Unit (PPO-GRU) model on the basis of robot kinematics modeling and deep reinforcement learning. First, by performing kinematic modeling of the X-type Mecanum-wheeled chassis and designing a high-dimensional state space along with a multi-factor composite reward function, the agent training environment for the robot–environment interaction control is established, laying the environmental foundation for in-depth research on path planning. Second, based on the construction of a Proximal Policy Optimization (PPO) path planning model, the PPO model is integrated with Gated Recurrent Units (GRUs) to form an improved PPO-GRU path planning model, thereby achieving an end-to-end path planning strategy. Finally, using a self-developed kinematic simulation platform for the X-type Mecanum-wheeled robot, the rationality and robustness of the proposed path planning model are investigated through ablation experiments, comparative experiments, dynamic environment tests, and tests considering key real-world phenomena. The research results indicate that the improved PPO-GRU path planning model increases the path planning success rate to 96%, reduces the average number of collisions by 82.7%, and achieves an average linear velocity reaching 84.5% of the maximum speed set in the environment. While attaining high-precision and robust planning management for autonomous navigation paths, it significantly improves the response speed of the agent’s autonomous navigation path planning. Full article

In complex ground environments, conventional RRT* often suffers from poor path quality and slow expansion during robot path planning. To address these issues, this paper proposes GEAR-RRT* (Goal-guided, adaptive informed-Ellipse sampling, layered obstacle-Avoidance expansion, and cost-driven Rewiring), which constructs a collaborative optimization mechanism across the three stages of sampling, expansion, and rewiring. First, the proposed method employs an adaptive informed ellipse to concentrate sampling within feasible regions while dynamically adjusting the informed-ellipse sampling domain, and further integrates Halton-directional hybrid sampling to generate high-quality candidate samples within that domain. Meanwhile, a layered expansion strategy is adopted: the planner first performs direct goal connection for rapid progress toward the goal; when this expansion is blocked by obstacles, it switches to local multi-directional offset to search for feasible expansion directions; if this still fails, an adaptive Artificial Potential Field is introduced to guide subsequent expansions until a feasible path is found. Next, a multi-factor rewiring parent selection strategy is used to optimize path length, safety clearance, and turning angle, while cubic B-spline smoothing is applied to improve path continuity. Finally, GEAR-RRT* is evaluated in five simulation environments as well as in joint ROS and physical-robot validation and is compared with five improved RRT* variants. The results demonstrate that the proposed method achieves superior overall performance in planning time, path length, and safety clearance. Full article

Biogeography-based optimization (BBO) is a population-based metaheuristic algorithm inspired by species migration among habitats. However, the original BBO often suffers from premature convergence and insufficient population diversity when solving complex optimization problems. To address these limitations, this paper proposes a novel dynamic multi-niche biogeography-based optimization (DMBBO) algorithm. DMBBO incorporates three effective strategies: a dynamic multi-niche population structure to maintain diversity and enhance parallel search capability, a dual-source migration mechanism to improve information exchange efficiency, and a niche-level hybrid elite preservation strategy to stabilize convergence behavior and improve solution quality. Extensive experiments were conducted on the CEC2022, CEC2020, and CEC2019 benchmark test suites under different dimensional settings. The experimental results demonstrated that DMBBO consistently outperformed 23 state-of-the-art algorithms in terms of optimization accuracy, convergence speed, and robustness, with statistically significant improvements validated by Friedman ranking and Wilcoxon rank-sum tests. An ablation study and convergence behavior analysis further confirmed the effectiveness of the proposed strategies. Additionally, DMBBO was applied to robotic path planning problems in grid-based environments involving six different scenarios with varying map sizes and obstacle densities. The results showed that DMBBO is capable of generating shorter and more stable paths in both simple and complex environments, highlighting its strong applicability to constrained optimization problems. Full article

In structured orchard environments, harvesting robots operate where rigid bodies (e.g., trunks, poles, and wires) coexist with flexible foliage. Strict avoidance of all obstacles significantly compromises operational efficiency. To address this, this study proposes an end-to-end autonomous harvesting framework characterized by an “avoid-rigid, push-through-soft” strategy. This framework explicitly propagates uncertainties from sensor data and reconstruction processes into the planning and policy phases. First, a multi-task perception network acquires 2D semantic masks of fruits and branches. Class probabilities and instance IDs are back-projected onto a 3D Gaussian Splatting (3DGS) representation to construct a decision-oriented, semantically enhanced 3D scene model. The policy network accepts multi-channel 3DGS rendered observations and proprioceptive states as inputs, outputting a continuous preference vector over eight predefined motion primitives. This approach unifies path planning and action decision-making within a single closed loop. Additionally, a dynamic action shielding module was designed to perform look-ahead collision risk assessments on candidate discrete actions. By employing an action mask to block actions potentially colliding with rigid obstacles, high-risk behaviors are effectively suppressed during both training and execution, thereby enhancing the robustness and reliability of robotic manipulation. The proposed method was validated in both simulation and real-world scenarios. In complex orchard scenarios, the proposed AE-TD3 algorithm achieves a harvesting success rate of 77.1%, outperforming existing RRT (53.3%), DQN (60.9%), and TD3 (63.8%) methods. Furthermore, the method demonstrates superior safety and real-time performance, with a collision rate reduced to 16.2% and an average operation time of only 12.4 s. Results indicate that the framework effectively supports efficient harvesting operations while ensuring safety. Full article

A comprehensive analysis of three graph-theoretic path planning algorithms designed for multi-robotic systems (MRS) was undertaken. The algorithms were the multi-robot path planning algorithm (MRPP), central algorithm (CA), and the optimisation central algorithm (OCA). The primary objective of these algorithms is to enhance path optimality, mitigate computational complexity, and ensure robust inter-robot collision avoidance. The MRPP is a composite approach integrating the visibility graph (VG) for path generation. The CA, derived from VG principles, utilises a central baseline (CB) approach to reduce vertex count, thereby decreasing computational cost while maintaining path efficiency. The OCA extends CA by integrating obstacle expansion and safety margins to enhance collision avoidance and path optimisation. Comparative analysis through simulations in 2D polygonal environments compared the performance of these algorithms, considering their computational efficiency, path optimisation, and collision avoidance. CA and OCA demonstrated significant improvement over the VG-based approach, especially concerning optimality and optimisation. CA reduced the average path length by 4.3% compared with MRPP, while OCA achieved a 6.8% reduction over MRPP, and 2.5% over CA, demonstrating its superior balance between optimality and efficiency. MRPP offers robust connectivity, making it preferable in scenarios where communication is critical. The study’s findings assist in devising MPRPP solutions. Full article

In complex ground environments, conventional RRT* often suffers from low planning efficiency and poor path quality for robot path planning. This paper proposes BGSE-RRT* (Bi-tree Cooperative, Goal-guided, low-discrepancy Sampling, multi-sector Expansion). First, BGSE-RRT* constructs a nonlinear switching probability via bi-tree cooperative adaptive switching, together with KD-Tree nearest-neighbor acceleration and multi-condition triggering, to adaptively balance global exploration and local convergence. Meanwhile, a goal-guided expansion with dynamic target binding and adaptive step size, under a multi-constraint feasibility check, accelerates the convergence of the two trees. When the goal-guided expansion becomes blocked, BGSE-RRT* generates candidate points in local multi-sector regions using a 2D Halton low-discrepancy sequence and selects the best candidate for expansion; if the multi-sector expansion still fails, a sampling-point-guided expansion is activated to continue advancing and search for a feasible path. Second, B-spline smoothing is applied to improve trajectory continuity. Finally, in five simulation environments and ROS/real-robot joint validation, compared with GB-RRT*, BI-RRT*, BI-APF-RRT*, and BAI-RRT*, BGSE-RRT* reduces planning time by up to 84.71%, shortens path length by 2.94–6.88%, and improves safety distance by 20.68–48.33%. In ROS/real-robot validation, the trajectory-tracking success rate reaches 100%. Full article

With the increasing use of multi-robot systems in emergency scenarios, collaborative path planning for robots has attracted greater attention. The multi-robot path-planning problem was modeled as a bilevel cooperative path planning model and solved using a memetic algorithm with a dynamic window approach and a parking scheduling strategy (MA-DWAPSS). The bilevel path planning model has divided the problem into two parts: global (static) path planning to find a near-optimal route and dynamic path planning to avoid path conflicts. Corresponding to the proposed MA-DWAPSS method, an improved memetic algorithm was developed based on genetic algorithm to find an optimal global path and a cubic Bézier curve to smooth the path and avoid sharp turns. The dynamic window approach (DWA) and parking scheduling strategy (PSS) obtain real-time sensor data and coordinate the docking and movement of robots in dynamic environments, handling obstacles in real time and preventing conflicts or unnecessary stops to improve efficiency. DWA further accounts for the dynamic characteristics of robot motion, making the path planning flexible and adaptive to rapid environmental changes. Simulation results show that the proposed method outperforms three other algorithms in path distance, time, obstacle avoidance, and smoothness. Full article

To address the tendency of the traditional Children’s Drawing Development Optimization (CDDO) algorithm to fall into local optima and converge slowly in global optimization and fire-field robot path planning, this study proposes a Multi-Strategy Enhanced Children’s Drawing Development Optimization (MECDDO) algorithm. The algorithm achieves performance improvements through three core strategies: (1) an adaptive cooperative search strategy that integrates information from the global best, worst, and random individuals and guides updates via dynamic weighting, expanding the exploration of the solution space; (2) a multi-strategy adaptive selection mechanism that constructs a pool of four differentiated strategies and dynamically adjusts selection probabilities based on strategy success rates, balancing exploration and exploitation; and (3) a global-optimum guided boundary repair strategy that reduces the loss of high-quality information from out-of-bounds solutions, enhancing local exploitation efficiency. Experiments on the CEC2017 benchmark suite demonstrate that MECDDO achieves outstanding performance across 30-, 50-, and 100-dimensional spaces. Statistical significance was evaluated using the Friedman test and Wilcoxon signed-rank test at a 0.05 significance level. The Friedman test mean rankings (M.R.) are 1.63, 2.20, and 2.70, respectively, consistently outperforming traditional CDDO (M.R. = 9.83, 9.93, 9.73, ranked 10th). Applied to mobile robot path planning, MECDDO achieves an average path length of 27.95483 in 20 × 20 grid environments (rank 1), shortening paths by 8.83% compared with CDDO (30.66212, rank 10), and 61.15516 in 40 × 40 grids (rank 1), reducing paths by 37.19% versus CDDO (97.20336, rank 9), providing trajectories free of redundant turns and convergence speeds 2–3 times faster than competing algorithms. These results validate MECDDO’s significant advantages in numerical optimization accuracy and practical robot path planning. Full article

With the increasing demands for autonomy and coverage efficiency in tasks such as security patrol and post-disaster exploration using mobile robots, achieving random, efficient, and complete coverage path planning has become a critical challenge. Traditional chaotic path planning methods, while capable of generating unpredictable trajectories, still have limitations in terms of randomness strength, traversal uniformity, and convergence coverage. To address this, this study proposes a complete-coverage random path planning method based on a novel four-dimensional fractal-fractional multi-scroll chaotic system. The main contributions of this research are as follows: First, by introducing additional state variables and fractal-fractional operators into the classical Chen system, a fractal-fractional chaotic system with a multi-scroll attractor structure is constructed. The output of this system is then mapped into robot angular velocity commands to achieve area coverage in unknown environments. Key findings include: the novel chaotic system possesses two positive Lyapunov exponents; Spectral Entropy (SE) and Complexity (CO) analyses indicate that when parameter B is fixed and the fractional order α increases, the dynamic complexity of the system significantly rises; in a 50 × 50 grid environment, the robot driven by this system achieved a coverage rate of 98.88% within 10,000 iterations, outperforming methods based on Lorenz, Chua systems, and random walks; ablation experiments further demonstrate that the combined effects of the fractal order β, fractional order α, and multi-scroll nonlinear terms are key to enhancing system complexity and coverage performance. The significance of this study lies in that it not only provides new ideas for constructing complex chaotic systems but also offers a reliable theoretical foundation and practical solution for mobile robots to perform efficient, random, and high-coverage autonomous inspection tasks in unknown regions. Full article

Mobile robots play an important role in promoting industrial intelligence and modernization. However, the existing obstacle avoidance path planning algorithms for mobile robots have poor stability and applicability. To this end, this paper proposes a path planning scheme for mobile robots based on ISH5N1 algorithm. Firstly, aiming at the problem of low initial population quality of SH5N1 algorithm, Tent chaos initialization strategy was proposed, which increased the diversity of the population, improved the quality of initial solution, and laid a foundation for subsequent deeper search. Secondly, by fusing the multi-source direction vectors and applying them to the position update, the solution accuracy of the algorithm was improved and the convergence speed of the algorithm was accelerated. Then, the mutation step size control strategy enhanced by Logistic chaos was used to enhance the ability of the algorithm to jump out of local optimum. Finally, the attenuation coefficient of inertia weight is optimized by combining cosine annealing strategy, which strengthens the ability of the algorithm to balance global search and local development. The ISH5N1 algorithm was compared with several commonly used intelligent optimization algorithms on benchmark functions and grid maps with different complexities. The results show that ISH5N1 algorithm shows good stability, higher solution accuracy and faster convergence speed in solving most benchmark functions. In the path planning experiment, the ISH5N1 algorithm can plan a shorter and smoother path, which further proves that the algorithm has good optimization ability and robustness. Finally, ablation experiments were carried out on a 20 × 20 grid map to verify the effectiveness of each optimization strategy. Full article

Internal hospital logistics are inherently complex, characterized by the critical need to move essential materials with high efficiency, precision, and safety. The adoption of automated guided vehicles (AGVs) is essential for automating these flows, but designing and optimizing their routes represents a significant challenge. This study presents a methodology for analyzing and optimizing AGV paths within healthcare facilities, effectively managing three-dimensional spatial complexity. The methodology leverages BIM and the open IFC standard to obtain an accurate geometric and semantic representation of the building. These data are then converted into a graph model using graph theory. Pathfinding algorithms, such as A*, are applied to this graph to calculate and optimize AGV trajectories, considering operational and collision constraints. The approach provides distance-optimized AGV paths. The integration of BIM, IFC, and graph theory proves to be an effective tool for logistical planning, simulation, and proactive management of AGVs in multi-level environments. This research contributes to the digital transformation of the construction sector by demonstrating how the integration of open standards and advanced algorithms can optimize the operational performance of complex buildings. By bridging the gap between architectural modeling and robotic logistics, the proposed approach supports the development of “smart buildings” and promotes more sustainable and technologically advanced management of healthcare facilities. Full article