Integrated Perception, Motion, and Communication

Updated 25 October 2025

Integrated Perception, Motion, and Communication (IPMC) is a framework that couples sensing, actuation, and networking to enhance robustness and efficiency in autonomous systems.
The methodology integrates hardware sensor fusion, adaptive motion planning, and tailored communication strategies to optimize performance under uncertainty and dynamic conditions.
IPMC applications span swarm robotics, autonomous vehicles, and UAV networks, offering improved safety, resource management, and collaborative intelligence.

Integrated Perception, Motion, and Communication (IPMC) encompasses the co-design and seamless coupling of sensing, actuation, and communication processes in autonomous and networked robotic systems. Rather than treating perception, motion, and communication as siloed functionalities, IPMC architectures seek to optimize their mutual dependencies in order to enhance robustness, efficiency, and collaborative intelligence—particularly in dynamic, resource-constrained, or distributed environments. The progression of the field is marked by advances ranging from tightly integrated hardware–software solutions in microrobotics to learning-based, cross-layer optimization frameworks in edge and vehicular robotics.

1. Constituent Modules and System Architectures

IPMC architectures integrate sensing modalities (e.g., IR, LiDAR, camera, hyperspectral imaging), motion planning or control algorithms (e.g., Model Predictive Control, Rapidly-Exploring Random Trees), and communication mechanisms (e.g., V2X, multi-agent sharing of semantic features) through feedback and information exchange at multiple system layers.

Physical Layer Integration: Early works demonstrated tight coupling at the hardware level, such as the “Jasmine” microrobots, where integrated IR sensors are time-multiplexed between proximity perception and channelized communication over six directional sectors. These shared hardware resources demand precise optical and temporal isolation and require protocol-level strategies to address dead zones, synchronization failure, and ambient light interference (Kornienko, 2011).
Software-Level Coordination: Layered autonomy stacks, as instantiated in risk-bounded planning frameworks, employ state estimation engines (e.g., Unscented Kalman Filters) to propagate uncertainty from perception into motion planning and control loops, ensuring that actuator commands are generated and risk-assessed in light of real sensing noise and mapping error (Renganathan et al., 2022, Renganathan et al., 2020).
Cloud and Edge Robotics: Modern IPMC systems utilize cloud-hosted planning and infrastructure-based sensing (e.g., via infrastructure sensor nodes, or ISNs), where global situational awareness is achieved through centralized or distributed fusion of sensor data, feeding MPC-based local planners for real-time obstacle avoidance and trajectory tracking (Yang et al., 29 Oct 2024).
Multi-Agent and Cooperative Systems: In CAVs (Cooperative Automated Vehicles), IPMC extends to cross-vehicle sharing of intermediate representations and motion predictions, with robust compression and alignment techniques ensuring efficiency under V2X transmission delays [(Wang et al., 26 Mar 2024), 2020.08.17, (Li et al., 2023)].

2. Sensing and Perception Integration

IPMC research advances the extraction and utilization of rich, task-relevant information from multi-modal sensor suites within resource-constrained or shared-bandwidth environments.

Perception–Compression–Communication Pipeline: In systems such as V2VNet and PE-MMSC, on-board neural encoders extract semantic, compressed representations from high-volume sensor data (e.g., LiDAR BEV feature maps, HSI+LiDAR fusion), optimizing for both downstream reconstruction/classification and efficient wireless transmission (Wang et al., 2020, Guo et al., 25 Mar 2025). Attention-based mechanisms and perception-guided fusion modules on the transmitter side prioritize regions or modalities that maximize task performance given current conditions.
Perception-Centric Adaptation: Several frameworks employ scenario change indicators or dynamic scene analysis to adapt the data-rate, down-sampling, and encoding strategies according to observed motion or environmental transitions (Guo et al., 18 Oct 2025). This enables selective communication of high-variability regions without sacrificing global task performance.
Sensor Fusion under Uncertainty: Probabilistic estimators (e.g., IMM-UKF) fuse both local sensor streams and communicated external observations, explicitly modeling non-Gaussian or history-dependent estimation uncertainty in integrated perception–planning loops (Li et al., 2023, Renganathan et al., 2022).

3. Motion Planning and Control with Embedded Perception and Communication

Emergent IPMC architectures handle the propagation of sensor and communication uncertainty through the planning and execution stack, employing risk-bounded, multiobjective, and learning-based formulations.

Perception-Aware Motion Planning: Algorithms such as Multiobjective Perception-Aware Planning (MPAP) employ Pareto-front searches that explicitly balance path cost against perception-driven heuristics (e.g., localization drift estimators, learned error predictors), often employing GPU-parallelized search for real-time responsiveness (Ichter et al., 2017). These plans adapt trajectory selection to ensure well-localized execution, switching homotopy class as perception constraints tighten.
Distributionally Robust Risk Constraints: Sampling-based planners (e.g., OFDR-RRT*, NRB-RRT) encode mapping, motion, and process noise as moment-based ambiguity sets, enforcing worst-case collision avoidance under uncertain distributions rather than assuming Gaussian noise. This results in trajectory plans that maintain user-prescribed risk thresholds across dynamic, cluttered environments (Renganathan et al., 2020, Renganathan et al., 2022).
Feedback-Controlled Integration: Controllers such as nonlinear MPC are tailored to operate atop risk-aware plans, refining control in the presence of propagated state and environment uncertainty. Local wiring of potential field costs (APF) into the MPC problem further allows geometry-aware obstacle shaping, vital for real-time adaptive navigation (Yang et al., 29 Oct 2024).

4. Communication–Motion Interdependence and Multi-Agent Collaboration

IPMC systems exploit bidirectional dependencies between communication strategy and local robot dynamics, particularly in bandwidth-constrained and distributed settings.

Motion-Aware Transmission Adaptation: Edge robots dynamically adjust compression ratios, transmission frequency, and transmit power according to both perceived scene changes and actual motion maneuvers (using, e.g., trajectory curvature), under joint optimization schemes that minimize end-to-end distortion subject to channel constraints and power budgets (Guo et al., 18 Oct 2025). The scenario change indicator (SCI) is central to these schemes, dictating when more or less data fidelity is required.
Cooperative Perception and Prediction: Multi-robot cooperation is realized by sharing not only detection outputs, but also compact, intermediate feature representations and probabilistic trajectory hypotheses (e.g., GMM parameters for future intent). Spatial transformation and attention-based aggregation (e.g., via graph neural networks or multi-head attention) enable vehicles to resolve occlusions or ambiguous predictions through viewpoint fusion, even under stringent transmission delays (Wang et al., 26 Mar 2024, Wang et al., 2020, Li et al., 2023).
Human–Robot Information Gain Planning: In the context of human–robot teams, IPMC strategies such as Information Gain MCTS optimize robot actions not only for navigation, but for communicative value to a human operator’s situational understanding, leveraging learned neural models of human perception update and quantified via eye-tracking and cognitive load metrics (Chen et al., 3 Feb 2025).

5. Robustness, Resilience, and Resource Management

As IPMC-enabled systems are tasked with critical operations (e.g., automated driving, smart factories, UAV networks), robustness to routine uncertainties and resilience to major disruptions are institutionalized at the design level.

Robust Resource Management: Proactive resource allocation strategies anticipate and mitigate the impact of uncertain channel conditions, fluctuating sensor performance, or compute bottlenecks. Robust optimization balances average throughput against outage probabilities to avoid both over- and under-provisioning (Lee et al., 24 Jun 2025).
Resilience by Redundancy and Adaptation: Features such as distributed, multi-tier architectures (ground, UAV, satellite nodes), multi-modal redundancy in sensing, and proactive switching (prompted by digital twin predictions or local anomaly detection) ensure service continuity post-disruption (Lee et al., 24 Jun 2025). Coded computing and adaptable network coding across sensing, communication, and computation layers further reinforce resilience.
Open Challenges: High-dimensional decision spaces in IPMC necessitate scalable, criticality-aware resource allocation. Efficient joint source–channel–compute coding, real-time fusion in multi-modal, multi-tier networks, and self-organizing architectures that can dynamically rewire for resilience remain outstanding research frontiers (Lee et al., 24 Jun 2025).

6. Representative Applications

A non-exhaustive list of application domains for IPMC approaches includes:

Domain	Technologies/Architectures	Key IPMC Features
Swarm microrobotics	IR-based multipurpose sensing & comms hardware (Kornienko, 2011)	Dual-use sensor design, time-division protocols
Autonomous vehicles	End-to-end RGBD/BEV perception & control (Natan et al., 2022), V2VNet (Wang et al., 2020)	Deep mobile fusion, GNN aggregation, waypoint-driven control
UAV-assisted ISAC	PE-MMSC with HSI+LiDAR semantic fusion (Guo et al., 25 Mar 2025)	Attention-guided feature selection, robust fusion
Networked robotics	Scenario-aware edge communication (Guo et al., 18 Oct 2025)	Motion-dependent transmission, LTO for fast optimization
Distributed radar/smart infra	Multi-tier sensing, coded computation (Lee et al., 24 Jun 2025)	Robust & resilient architectures, resource adaptation

7. Mathematical Modeling and Optimization

Advanced IPMC implementations rely on explicit modeling of uncertainty, resource constraints, and cross-domain cost objectives.

Multiobjective Optimization: Pareto-front formulations balance task objectives (e.g., motion cost, perception drift, communication reliability), with real-time feasibility achieved via parallelized search or surrogate learning (Ichter et al., 2017, Guo et al., 18 Oct 2025).
Risk Constraints and Ambiguity Sets: Collision avoidance and safety-critical planning are cast as constraint satisfaction under ambiguity sets with specified moment vectors (mean, covariance), leading to robustified deterministic or chance-constraint formulations (Renganathan et al., 2020, Renganathan et al., 2022).
Feature Compression and Fusion Mechanics: Data pipelines leverage autoencoders, attention-based weighting, and multimodal fusion tailored by coarse semantic analysis (e.g., preliminary classification) to maximize task reliability while minimizing communication load (Guo et al., 25 Mar 2025, Wang et al., 26 Mar 2024).
Human-Perception-Aware Reward Shaping: Information-theoretic reward functions drive action selection in mixed-initiative settings, trained on observed human map editing and assessed through cognitive and task outcomes (Chen et al., 3 Feb 2025).

Conclusion

IPMC research and systems demonstrate that the joint design of perception, motion, and communication yields significant advantages in terms of task reliability, operational efficiency, and system resilience. Advances stem from both cross-domain hardware integration in miniaturized agents and algorithmic innovation in optimization, uncertainty modeling, fusion, and learning-based control. The field continues to address open challenges in scalable, robust resource management and adaptive, intelligence-driven collaboration, with broad relevance across robotics, autonomous driving, UAV networks, and digital twin-enabled infrastructures.