Autonomous Ground Robotic Systems (AGRS) refer to unmanned ground vehicles (UGVs) equipped with artificial intelligence-based control systems that enable independent operation with minimal or no human intervention. These platforms represent a significant evolution in robotics technology, transitioning from traditional remote-piloted vehicles to fully autonomous agents capable of complex decision-making, environmental perception, and coordinated multi-agent operations.

Autonomous ground robotic systems combine advances in autonomous vehicle technology, artificial intelligence, sensor fusion, and control theory to create platforms that can navigate complex terrain, perceive their environment, and execute missions with varying degrees of human oversight ¹⁾. These systems differ fundamentally from remotely-piloted vehicles by incorporating onboard decision-making capabilities that allow them to respond to environmental conditions and mission objectives without continuous human commands.

The technological foundation rests on several key components: SLAM (Simultaneous Localization and Mapping) algorithms for navigation, computer vision systems for environmental perception, path planning algorithms, and reinforcement learning or decision-tree systems for tactical decision-making ²⁾. Modern systems increasingly employ large language models and transformer-based architectures to enhance reasoning capabilities and enable more sophisticated mission planning.

Contemporary autonomous ground robotic systems include specialized platforms designed for various operational contexts. Notable examples include the Ratel, TerMIT, Ardal, Rys, Zmiy, Protector, and Volia systems, which have been deployed in active operations and demonstrate the practical viability of autonomous ground platforms. These systems showcase different design philosophies and capabilities, ranging from light reconnaissance variants to heavier platforms capable of sustained autonomous operations in denied environments.

Early implementations of these platforms have demonstrated success in coordinated operations with aerial systems (drones), representing a significant advancement in multi-agent autonomous systems ³⁾. This coordination requires sophisticated communication protocols, shared situational awareness systems, and decentralized decision-making frameworks that allow ground and aerial platforms to operate cohesively toward common objectives.

The transition from remote piloting to autonomous control involves several technical innovations. Perception systems use multi-modal sensor inputs including LIDAR, cameras, and thermal imaging to build environmental models. Planning algorithms employ both classical approaches (graph-based path planning, collision avoidance) and learning-based methods that improve through experience ⁴⁾.

Decision-making frameworks increasingly incorporate hierarchical architectures where higher-level strategic reasoning (handled by language models or symbolic AI systems) directs lower-level reactive control. This separation enables systems to reason about abstract mission objectives while maintaining robust real-time control ⁵⁾. Communication protocols allow multiple autonomous systems to share sensor data and coordinate actions through decentralized consensus mechanisms.

Autonomous ground systems face significant technical and operational challenges. Environmental complexity in real-world terrain, including occlusion, dynamic obstacles, and adverse weather, remains difficult for perception systems. Latency requirements in tactical environments demand millisecond-level response times, creating tension with more computationally intensive AI approaches. Robustness and reliability are critical concerns—system failures in operational contexts can have severe consequences, requiring extensive validation and redundancy.

Adversarial robustness presents additional challenges, as these systems may operate in environments where opponents actively attempt to deceive or disable them ⁶⁾. Questions of appropriate human oversight, legal frameworks for autonomous weapons systems, and ethical considerations regarding autonomous decision-making in contested environments remain active areas of discussion within both technical and policy communities.

Active research in autonomous ground robotics focuses on improving multi-agent coordination, enhancing adversarial robustness, and developing more sophisticated reasoning systems that can handle ambiguous or incomplete information. Integration of large language models with classical robotics approaches shows promise for creating systems with improved contextual understanding and long-horizon planning capabilities. Efforts continue to address the gap between simulation performance and real-world deployment, particularly regarding sim-to-real transfer and domain adaptation.

• Ratel Unmanned Ground Robot
• Ardal Unmanned Ground Robot
• TerMIT Unmanned Ground Robot
• Rys Unmanned Ground Robot
• Protector Unmanned Ground Robot