Antonios Chatzisavvas

The A* algorithm is widely adopted across agriculture, robotics, and GPS navigation for efficient route planning, yet it faces challenges in balancing search efficiency with path quality. To address these limitations, we introduce Octile–Annealed, a novel heuristic that augments the classic Octile distance with a distance-dependent annealing weight. Specifically, Octile–Annealed scales the Octile metric by a smooth function of the current node’s Euclidean distance to the final location, yielding a heuristic that is gentle near the target and more directive when far away. This design retains the geometric fidelity of Octile, accelerates search convergence in open regions, and preserves guidance in constrained corridors. Beyond discrete planning, we incorporate adaptive Bézier smoothing to post-process the grid path into a collision-free, curvature-friendly trajectory. This is particularly relevant in agricultural environments (e.g., orchard rows and cross-aisles), where machines must follow efficient routes without abrupt turns that could slow operations or risk crop damage. We benchmark Octile–Annealed against three established baselines—Euclidean and Octile—on orchard-like grids of varying sizes and obstacle patterns. The results show that Octile–Annealed consistently reduces computation time while maintaining competitive raw path lengths and producing short, smooth Bézier trajectories. Overall, the proposed heuristic enhances A*’s operational efficiency and route quality, making it well-suited for complex, structured agricultural layouts and for general navigation tasks that benefit from smooth post-processing. However, it must be acknowledged that these comparative performance metrics are strictly limited to simulated grid cases; consequently, comprehensive validation using actual field data remains necessary to fully confirm their practical applicability under real-world agricultural conditions.

The A* algorithm is broadly identified for its application in diverse fields, such as agriculture, robotics and GPS technology, due to its effectiveness in route planning. Despite its broad utility, the algorithm faces inherent limitations regarding operational efficiency and the length of the paths it generates. Addressing these constraints, this paper proposes an enhancement to the traditional A* algorithm that significantly improves its performance. Our innovative approach integrates Euclidean and Chebyshev distances into a single heuristic function, thereby enhancing pathfinding accuracy and flexibility. This combined heuristic leverages the strengths of both distance measures: the Euclidean distance provides an accurate straight-line measure between points, while the Chebyshev distance effectively handles scenarios allowing diagonal movement. Furthermore, we incorporate Bezier curves into the algorithm to smooth the generated paths. This addition is particularly advantageous in agricultural environments, where machinery must navigate complex terrains without causing damage to crops. The smooth paths produced by Bezier curves ensure more efficient and safer navigation in such settings. Comprehensive experiments conducted in various agricultural scenarios demonstrate the superior performance of the enhanced algorithm. These results reveal that the improved algorithm not only reduces the computation time needed for route planning but also generates shorter and smoother paths compared to the standard A* algorithm. The proposed approach significantly enhances the operational efficiency and route optimization capabilities of the A* algorithm, making it more suitable for complex and dynamic applications in agriculture. This advancement also holds promise for improving navigation systems in various other domains.

The A-star algorithm (A*) is a traditional and widely used approach for route planning in various domains, including robotics and automobiles in smart agriculture. However, a notable limitation of the A-star algorithm is its tendency to generate paths that lack the desired smoothness. In response to this challenge, particularly in agricultural operations, this research endeavours to enhance the evaluation of individual nodes within the search procedure and improve the overall smoothness of the resultant path. So, to mitigate the inherent choppiness of A-star-generated paths in agriculture, this work adopts a novel approach. It introduces utilizing Bezier curves as a postprocessing step, thus refining the generated paths and imparting their smoothness. This smoothness is instrumental for real-world applications where continuous and safe motion is imperative. The outcomes of simulations conducted as part of this study affirm the efficiency of the proposed methodology. These results underscore the capability of the enhanced technique to construct smooth pathways. Furthermore, they demonstrate that the generated paths enhance the overall planning performance. However, they are also well suited for deployment in rural conditions, where navigating complex terrains with precision is a critical necessity.

In the ever-evolving landscape of warehousing, the integration of unmanned ground vehicles (UGVs) has profoundly revolutionized operational efficiency. Despite this advancement, a key determinant of UGV productivity remains its energy management and battery placement strategies. While many studies explored optimizing the pathways within warehouses and determining ideal power station locales, there remains a gap in addressing the dynamic needs of energy-efficient UGVs operating in tandem. The current literature largely focuses on static designs, often overlooking the challenges of multi-UGV scenarios. This paper introduces a novel algorithm based on affinity propagation (AP) for smart battery and charging station placement in modern warehouses. The idea of the proposed algorithm is to divide the initial area into multiple sub-areas based on their traffic, and then identify the optimal battery location within each sub-area. A salient feature of this algorithm is its adeptness at determining the most strategic battery station placements, emphasizing uninterrupted operations and minimized downtimes. Through extensive evaluations in a synthesized realistic setting, our results underscore the algorithm’s proficiency in devising enhanced solutions within feasible time constraints, paving the way for more energy-efficient and cohesive UGV-driven warehouse systems.

The earth’s population is expected to increase by a phenomenal 240,000 people every single day, reaching a total of 8.20 billion by the year 2026 and 9.85 billi

In recent years, the need for robotic fleets in large warehouse environments has constantly increased. The customers require faster services concerning the delivery of their products, making the use of systems such as robots and order-management software more than essential. Numerous researchers have studied the problem of robot routing in a warehouse environment, aiming to suggest an efficient model concerning the robotic fleet’s management. In this research work, a methodology is proposed, providing feasible solutions for optimal pathfinding. A novel algorithm is proposed, which combines Dijkstra’s and Kuhn–Munkers algorithms efficiently. The proposed system considers the factor of energy consumption and chooses the optimal route. Moreover, the algorithm decides when a robot must head to a charging station. Finally, a software tool to visualize the movements of the robotic fleet and the real-time updates of the warehouse environment was developed.

It is evident that over the last years, the usage of robotics in warehouses has been rapidly increasing. The usage of robot vehicles in storage facilities has resulted in increased efficiency and improved productivity levels. The robots, however, are only as efficient as the algorithms that govern them. Many researchers have attempted to improve the efficiency of industrial robots by improving on the internal routing of a warehouse, or by finding the best locations for charging power stations. Because of the popularity of the problem, many research works can be found in the literature regarding warehouse routing. The majority of these algorithms found in the literature, however, are statically designed and cannot handle multi-robot situations, especially when robots have different characteristics. The proposed algorithm of this paper attempts to give the following solution to this issue: utilizing more than one robot simultaneously to allocate tasks and tailor the navigation path of each robot based on its characteristics, such as its speed, type and current location within the warehouse so as to minimize the task delivery timing. Moreover, the algorithm finds the optimal location for the placement of power stations. We evaluated the proposed methodology in a synthetic realistic environment and demonstrated that the algorithm is capable of finding an improved solution within a realistic time frame.