CLOUD TECHNOLOGIES IN ROBOT MOTION PLANNING: CURRENT STATE

ABSTRACT

Modern robotics struggles with real-time motion planning due to limited onboard computing. That is why cloud technologies have become essential. Cloud technologies offload complex tasks, enabling advanced algorithms, AI, and large datasets. Cloud-based planners find optimal route options, detect obstacles, and coordinate the work of multiple robots, while simulations and real-time data improve route safety and efficiency. Overall, cloud robotics improves accuracy, collaboration, and scalability, supporting advanced autonomous systems in dynamic environments.

АННОТАЦИЯ

Современная робототехника сталкивается с проблемами планирования движений в реальном времени из-за ограниченных вычислительных мощностей бортовых компьютеров. Именно поэтому облачные технологии стали необходимыми. Облачные технологии позволяют переложить сложные задачи на облачные серверы, что открывает возможности для использования сложных алгоритмов, искусственного интеллекта и больших наборов данных. Облачные планировщики находят оптимальные варианты маршрутов, обнаруживают препятствия и координируют работу нескольких роботов, а симуляции и данные в реальном времени повышают безопасность и эффективность маршрута. В целом облачная робототехника повышает точность, улучшает взаимодействие и масштабируемость, поддерживая работу сложных автономных систем в динамичных средах.

Keywords: Algorithms, AI, Safety, Efficiency, Cloud simulation.

Ключевые слова: Алгоритмы, искусственный интеллект, безопасность, эффективность, Облачная симуляция.

Introduction

Modern robotics is developing at a rapid pace, which requires autonomous control systems to achieve high speed and precision in motion planning. Trajectory planning and the execution of complex manipulations have traditionally been carried out using the robot’s own local computing resources. However, such approaches face significant limitations: the computational power of embedded processors is restricted, devices often have limited memory and energy capacity, and advanced algorithms—such as deep learning methods or high-dimensional trajectory optimization—are frequently too resource-intensive to be executed efficiently in real time. This becomes especially critical for industrial, service, and autonomous mobile robots, where accuracy and reaction speed directly affect safety and productivity.

Cloud technologies represent a promising solution to these constraints. Offloading computational tasks to remote servers makes it possible to harness high-performance resources, advanced artificial intelligence algorithms, and large datasets. This not only accelerates the planning processes but also enables the integration of data from multiple robots into a unified system for collaboration and coordination. Cloud platforms are capable of handling complex trajectory planning scenarios, analyzing dynamic environments, predicting object movements, and optimizing robot routes in real time.

The use of cloud technologies opens up new opportunities for robot learning and adaptation based on large-scale datasets, which is difficult to achieve through local processing. Such an approach enhances the overall efficiency, accuracy, and reliability of autonomous systems, creating prospects for the development of collective robotics and intelligent real-time solutions.

2. Materials and methods

1. Cloud-based route planners. Cloud-based path planners allow robots to transmit information about their location, the state of the environment, and detected obstacles to a remote server. On the server side, all computations related to constructing the optimal path, considering the dynamics of the environment and the robot’s goals, are performed. This approach reduces the computational load on the robot’s local processors and makes it possible to use advanced planning algorithms that are often too complex to be efficiently implemented on embedded hardware. By utilizing cloud resources, robots can implement high-dimensional path optimization, predictive obstacle avoidance, and adaptive routing strategies, ensuring smoother and safer navigation even in highly dynamic environments. Additionally, the cloud can store and analyze historical movement data, allowing robots to learn from past experiences and continuously improve their path-planning performance.

Cloud-based planners also enable large-scale coordination among multiple robots operating simultaneously in the same environment. By centralizing data collection and computation, the system can detect potential collisions, congestion points, and conflicting routes before they occur, dynamically adjusting each robot’s trajectory [5, c. 1345-1360].This centralized control allows for intelligent scheduling, priority task allocation, and energy-efficient routing, which are essential in industrial warehouses, service environments, and logistics hubs. Moreover, integration with real-time sensor inputs, predictive analytics, and warehouse management systems enhances the system’s ability to respond to sudden changes, such as unexpected obstacles or variable workload demands, ensuring both safety and operational efficiency at scale.

At the core of cloud-based planning lies the task of finding a path that minimizes the overall “cost” of navigation. The cost typically accounts for the total path length and the proximity to obstacles, but it may also include additional parameters such as movement speed, energy consumption, or safety. If a path is represented as a sequence of points (x_i​,y_i​), then the cost function can be expressed as:

(1)

where λ is a coefficient that defines the weight of the penalty.

Consider a warehouse robot that must move from point A to point B while avoiding obstacles. Suppose its route consists of three waypoints: A (0,0), C (2,1) - detour around the obstacle, and B (4,0). The total path length is calculated as the sum of distances between points, 4.24.

Figure 1. Cloud-Based Motion Planning in a Static Environment

If the path passes close to an obstacle, a penalty R_C =1 is applied. With λ=2, the total cost of the path is C(P)=6.24.

The server evaluates several possible paths and selects the one with the lowest cost, thereby ensuring safe, efficient, and optimal robot navigation.

Cloud-based planning is not limited to individual robots. By integrating data from multiple robots operating in the same environment, the cloud can optimize their trajectories collectively. This enables coordination, reduces the risk of collisions, and improves overall system efficiency. For example, in large warehouses or autonomous delivery networks, robots can share real-time information about obstacles, traffic congestion, or task priorities [8, c. 11861809]. The cloud server then redistributes tasks and recalculates optimal routes almost instantly, allowing robots to work together seamlessly.

- Another advantage of this approach is scalability. As the number of robots grows, centralized cloud servers can leverage powerful computational resources and large-scale data analytics to adapt planning strategies dynamically. This paves the way for swarm robotics, where large groups of robots collaborate intelligently in logistics, manufacturing, or service applications.

2. Machine Learning in the Cloud. Cloud servers open up new opportunities for applying artificial intelligence (AI) and machine learning (ML) algorithms, enabling robots to predict the movement of dynamic objects and optimize their routes in real time. Unlike traditional methods, where a robot only reacts to its immediate surroundings, cloud-based algorithms can analyze data from past observations and forecast the behavior of other objects. This capability is particularly important in dynamic environments such as warehouses, offices, or urban areas, where robots may encounter people, other robots, or moving obstacles [6, c. 1268447].

Cloud-based ML also enables context-aware navigation, where robots can consider external factors such as human activity, time-dependent traffic patterns, or temporary obstacles. For instance, in a warehouse setting, robots can learn peak traffic periods and adjust their routes to avoid congestion, while in urban environments, delivery robots can anticipate pedestrian movement or vehicle interactions [1, c. 118-123]. This proactive adaptation significantly enhances safety and reduces delays.

Furthermore, the integration of machine learning with cloud platforms supports collaborative multi-robot operations. Robots can share real-time updates about detected obstacles, completed tasks, or changing environmental conditions, allowing the system to coordinate movements, redistribute tasks, and optimize overall fleet performance. This cooperative approach ensures that even in large-scale operations, multiple robots can work together efficiently, minimizing downtime, energy consumption, and the risk of collisions [2, c. 103877]. At the core of this approach lies a cost function for the path, which takes into account not only the length of the route but also the probability of collisions with dynamic objects. Suppose the robot’s trajectory consists of several segments; then the general form of the cost function can be written as:

                                               (2)

where:

Cloud-based AI algorithms can update the values of  in real time whenever new dynamic objects are detected along the path.

A robot moves along a path consisting of four waypoints: A (0,0), B (1,1), C (2,1) and D (3,0). The segment lengths are calculated as 1.41,  = 1.0, 1.41.

Let us assume that on the segment B→C there is a probability of collision with another robot,  = 0.5, and the safety weight is α=2. The cost of the path is then:

C(P)=  α ) + =4.41.                                     (3)

If the cloud server detects the movement of a new object on the segment C→D with probability  = =0.3, the cost function is updated C(P)=5.4.

Thus, the robot receives updated information about increased risk on certain segments and can adjust its path to minimize the likelihood of collision. Cloud-based machine learning enables robots not only to anticipate hazards but also to adapt their routes in real time, significantly improving safety and operational efficiency.

The integration of ML into cloud robotics also allows for continuous improvement of decision-making models. Robots can share their operational data with the cloud, where machine learning algorithms refine predictive models using collective experience. Over time, this leads to better estimations of collision probabilities, more accurate trajectory predictions, and smarter strategies for avoiding risks.

Furthermore, cloud-based ML makes it possible to incorporate contextual factors into path planning. For instance, the system can consider traffic patterns in a warehouse, human activity in offices, or even weather conditions in outdoor environments. By factoring in such diverse variables, robots achieve a higher degree of situational awareness, which improves both safety and task efficiency.

In large-scale applications, such as fleets of delivery drones or autonomous warehouse robots, this approach enables cooperative behavior: robots can coordinate their routes, reduce congestion, and dynamically redistribute tasks [7, c. 012001]. As a result, cloud-driven machine learning not only optimizes individual paths but also enhances the overall performance of multi-robot systems.

3. Simulation in the cloud. Before a robot begins moving in a real environment, cloud platforms allow routes to be tested in a virtual simulation environment. This is especially important in dynamic conditions, where a path can become unsafe due to the appearance of new obstacles or the movement of other robots. If a route is deemed risky, the cloud server recalculates the optimal path, taking into account safety, travel time, and other factors.

Cloud-based simulations also support multi-robot coordination. By modeling the movements of several robots simultaneously, the system can predict potential collisions, optimize task distribution, and ensure smooth navigation even in congested areas. This collective testing enables proactive conflict resolution and allows robots to adapt dynamically to each other’s movements, improving overall operational efficiency [3, c. 1187].

Additionally, simulations in the cloud facilitate continuous learning and optimization. Data collected from each simulation can be fed back into cloud-based planning and machine learning algorithms, enabling robots to improve performance over time. This iterative approach allows robots to adapt to changes in the environment, anticipate risks more effectively, and achieve higher levels of safety, speed, and precision in their tasks.The route cost function can include several components: path length, probability of collision with obstacles, and estimated segment traversal time. A simple version of the cost function for route recalculation can be expressed as:

                                            (4)

where:

length of the i-th segment of the path;

probability of collision on the i-th segment;

estimated travel time for the segment;

α, β – coefficients representing the relative importance of safety and travel time.

Suppose a robot moves along a route with three points  =1.41,  = 1.0,

Assume collision probabilities: =0.2, =0.5, estimated segment travel times: =1.0, =1.2, and coefficients: α=2, β=1.5. Then the route cost is C(P)=7.12

The cloud server can test multiple alternative routes and select the one with the lowest cost, ensuring a balance between safety and delivery time.

3. Results and discussion

Modern robotic systems face a number of complex challenges related to motion planning. One of the key issues is the high computational load on the robots’ embedded processors, which limits their ability to execute advanced planning algorithms in real time. In addition, it is essential to ensure route planning accuracy so that robots can move safely and efficiently in dynamic environments, avoiding collisions with obstacles and other robots.

Another relevant challenge is ensuring information exchange between multiple robots for the coordination of joint actions. For example, in a warehouse, a robot must navigate from one point to another while optimally selecting its route based on the current situation and the actions of other robots. Solving such tasks requires a comprehensive approach that combines efficient planning, risk assessment, and the ability to make collaborative decisions, which makes cloud technologies particularly promising for their implementation.

In a modern warehouse, multiple autonomous robots often operate simultaneously, each following its own planned route, such as P₁, P₂, and P₃. The main objective in such multi-robot systems is to minimize the total operational cost of all routes while maintaining safety, efficiency, and timely delivery.

Conflicts can occur when routes intersect or overlap, which increases the risk of collisions and delays. Cloud-based simulation and planning platforms are essential for managing these challenges. They continuously monitor all active routes and can adjust robot paths in real time to reduce the likelihood of collisions and optimize overall performance.

For example, one robot may be rerouted along a slightly longer but safer path to avoid a high-traffic intersection. Another robot may adjust its speed when moving through intersecting segments to maintain safe spacing [4 c. 102259]. The system can also prioritize urgent deliveries, allowing certain robots to take the fastest route while others adapt their paths accordingly.

In addition to collision avoidance, cloud platforms can incorporate real-time data from sensors, warehouse management systems, and dynamic updates, such as temporary obstacles, variable robot speeds, or changes in shipment schedules. This enables the system to plan adaptively and respond to unexpected changes in the warehouse environment.

By simulating multiple routing alternatives and analyzing their outcomes, the cloud platform can select the most efficient combination of routes for all robots, ensuring a balanced trade-off between safety, speed, and overall operational efficiency. This capability is especially important in large-scale warehouses, where managing multiple robots manually would be inefficient and prone to errors.

Cloud-based coordination also supports scalability. As more robots are added to the system, the platform continues to optimize their routes, resolve conflicts, and anticipate potential bottlenecks. This allows for safe and efficient operation of large robotic fleets in complex and dynamic warehouse environments.

In summary, cloud simulations enable not only pre-movement testing of robot routes but also coordinated multi-robot operations, enhancing safety, efficiency, and adaptability. By leveraging real-time data and intelligent planning algorithms, cloud-based route planning becomes a key tool for modern automated logistics, ensuring that multiple robots can operate together smoothly and effectively in dynamic conditions.

4.   Conclusion

Cloud technologies significantly improve the efficiency of robot movement planning by offloading computational tasks from local devices to remote servers. This allows robots to use more complex algorithms for route selection, obstacle prediction, and movement time optimization, which was previously limited by their onboard processors.

One of the key advantages is the integration of data from multiple robots. A cloud platform enables the collection of information about the positions, routes, and dynamic environment of all devices simultaneously. As a result, each robot gains access to more comprehensive information about the workspace, reducing the likelihood of collisions and redundant actions.

The use of artificial intelligence algorithms in the cloud allows robots not only to analyze the current situation but also to predict potential obstacles and optimal paths. For example, in dynamic environments—such as warehouses or shopping centers—robots can proactively adjust their routes to avoid people and other machines.

Using cloud-based simulations enhances robot safety. Before performing actual movements, robots can test routes in a virtual environment, which helps identify hazardous situations, evaluate route efficiency, and make adjustments without risking equipment damage or disrupting the production process.

The practical significance of cloud solutions in robotics can be considered in several areas:

1. Autonomous transportation systems. Drones, robotic vehicles, and transport platforms use cloud computing for safe and efficient navigation in urban and industrial settings.
2. Industrial and service robots.In factories and service robotics, the cloud enables route optimization, reduces downtime, and increases productivity.
3. Warehouse logistics.Warehouse robots can collaboratively plan routes for quick and safe movement of goods, minimizing delivery time and collision risks.

Thus, cloud technologies not only enhance the accuracy and speed of planning but also facilitate collaborative interaction among robots, allowing them to operate as a coordinated team. In the future, this opens opportunities for more complex autonomous control systems, integration of robotics with “smart” urban and industrial systems, and scaling of robotic processes without significantly increasing local computational resources.

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