Intelligent Path Planning for Robotic Systems: Modeling, Optimization and Real-Time Decision-Making
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 1571
Special Issue Editors
Interests: modeling and simulation, operation optimization, and decision analysis for complex systems including unmanned systems, and power and energy systems
Special Issues, Collections and Topics in MDPI journals
Interests: robotic dexterous manipulation; audio-visual SLAM; embodied AI in robotic system
Interests: modeling and optimization control for complex systems including unmanned aerial vehicle power systems, ro-bot power systems, and other hybrid power systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Optimization algorithms are pivotal in enabling robots to achieve autonomy in complex, dynamic environments. While traditional methods often fail to address scalability and real-time demands, emerging techniques—from metaheuristics (PSO, GA, ACO) to combinatorial optimization and reinforcement learning—are revolutionizing robotic path planning. However, their practical deployment requires tight integration with environmental perception, robust decision-making under uncertainty, and computationally efficient execution.
This Special Issue focuses on algorithmic innovation and system-level implementation for robotic path planning, emphasizing three interconnected pillars:
(1) Modeling and Optimization
- Advanced formulations for kinodynamic/dynamic constraints in continuous/discrete spaces;
- Multi-objective optimization (e.g., energy-time-risk tradeoffs) with interpretability guarantees;
- Hybrid architectures combining classical optimization with learning-based components.
(2) Real-Time Planning and Decision-Making
- Online replanning with provable latency bounds (e.g., anytime algorithms, model predictive control);
- Adaptive strategies for dynamic obstacles and uncertain environments (e.g., stochastic RL, meta-learning);
- Hardware–algorithm co-design (e.g., edge computing, FPGA acceleration).
(3) Environmental Intelligence for Planning
- Active perception–modeling–planning loops (e.g., uncertainty-aware mapping);
- Human-aware navigation (e.g., social cost maps, intent prediction);
- Physics-informed terrain interaction (e.g., deformable surfaces, fluid dynamics).
We seek contributions that
- Propose novel algorithms with theoretical rigor and practical validation (simulation + hardware);
- Address real-world challenges, such as sensor noise, computation/communication bottlenecks, safety-critical constraints;
- Demonstrate applications in autonomous vehicles, agile robots, search-and-rescue, or other latency-sensitive domains.
Dr. Jingrui Zhang
Dr. Yu Xie
Dr. Po Li
Guest Editors
Manuscript Submission Information
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Keywords
- kinodynamic planning
- anytime algorithms
- model predictive control
- chance-constrained optimization
- mul-ti-objective optimization
- subgoal replanning
- latency-aware scheduling
- uncertainty-aware mapping
- human–robot spatial cognition
- neuromorphic computing for planning
- physics-informed neural planners
- federated learn-ing for multi-robot systems
- bio-inspired navigation
- heuristic/meta-heuristic path planning
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