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Open research questions in Robotic Path Planning Algorithms

98 unresolved questions extracted from the limitations and future-work sections of 475 Robotic Path Planning Algorithms papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • The paper suggests that future research can focus on improving the efficiency of the algorithm. The paper suggests that future research can explore the application of the approach to other domains.

    Bulk Search For Optimally Solving Two Variants Of Anonymous Multi-agent Pathfinding · 2026 · DOI

  • The paper identifies a gap in existing solutions for AMAPFD with the SOC objective. The paper notes that existing approaches for AMAPF have a critical bottleneck in one part of the solving process.

    Bulk Search For Optimally Solving Two Variants Of Anonymous Multi-agent Pathfinding · 2026 · DOI

  • The inherent uncertainty and imprecision in sensor inputs - The complexity of path planning in static, unknown environments - The need to balance linguistic complexity, sensor integration depth, and fuzzification

    Factorial Design-Based Optimization of Fuzzy Logic Controller Parameters for Autonomous Robot Navigation in Static Environments · 2026 · DOI

  • The high dimensionality of the state space in multi-agent systems. The need to enforce complex specifications beyond collision avoidance. The requirement for rigorous adherence to spatial goals and strict time bounds.

    Sampling-Based Multi-Agent Path Planning Guided by Spatio-Temporal Logic Mission Objectives · 2026 · DOI

  • Autonomous exploration of unknown environments remains a fundamental challenge in mobile robotics. Communication latency, limited scalability, and risk of interference are significant challenges. The lack of effective solutions for autonomous exploration of unknown environments is a significant challenge.

    Efficient multi-robot exploration of unknown environments using inverted ant colony optimization and reinforcement learning · 2026 · DOI

  • The large task space and the occurrence of infeasible task actions. The need to address geometric infeasibility and non-monotonicity. The presence of obstacles and cluttered environments.

    Benchmark evaluation in task and motion planning using iteratively deepened AND/OR graph networks · 2026 · DOI

  • Exploring more efficient sampling or hierarchical partitions to handle dozens of agents. Applying the approach to other multi-agent systems with similar challenges.

    Collision-aware cooperative multi-UAV path planning with hierarchical PPO-LSTM · 2026 · DOI

  • Flat optimisation or single-level reinforcement-learning agents scale poorly as map size, obstacle density, or fleet size increase. There is a need for a more efficient and scalable approach to multi-UAV path planning.

    Collision-aware cooperative multi-UAV path planning with hierarchical PPO-LSTM · 2026 · DOI

  • Prior work has relied exclusively on Conflict-Based Search, resulting in non-scalable solutions. There is a need for a more scalable approach to TAPF.

    Alternating Target–Path Planning for Scalable Multi-Agent Coordination · 2026 · DOI

  • Further evaluation of kRLaCAM*-CR on larger problem instances. Application of Cardinality Reduction (CR) to other MAPF algorithms.

    Scalable Robust Multi-Agent Path Finding (Student Abstract) · 2026 · DOI

  • Existing algorithms like kRCBS struggle to scale to larger problem instances. There is a need for more efficient and scalable MAPF algorithms.

    Scalable Robust Multi-Agent Path Finding (Student Abstract) · 2026 · DOI

  • Existing algorithms have limitations in handling delays and unrestricted walking transfers. There is a need for more efficient and accurate journey planning approaches.

    Fast and Memory Efficient Multimodal Journey Planning with Delays · 2026 · DOI

  • The proposed framework is tested in a limited set of scenarios and environments. The real-world experiments are conducted in an indoor environment with a relatively small number of obstacles.

    Real-time obstacle avoidance in mobile robots using deep reinforcement learning · 2026 · DOI

  • To extend the proposed framework to more complex and dynamic environments. To integrate the proposed framework with other robotic systems and sensors. To conduct more extensive testing and evaluation of the proposed framework.

    Real-time obstacle avoidance in mobile robots using deep reinforcement learning · 2026 · DOI

  • Traditional Coverage Path Planning approaches struggle with irregular coastlines and exclusion zones - There is a need for a more efficient and effective method for maritime coverage path planning

    Critic-free Deep Reinforcement Learning for maritime coverage path planning on irregular hexagonal grids · 2026 · DOI

  • Traditional planning methods are conservative and mainly used for low-speed flights. There is a need for efficient and safe whole-body motion planning for quadrotors.

    Search-based hierarchical whole-body motion planning and control for quadrotors · 2026 · DOI

  • Irregular coastlines and exclusion zones - Computational complexity of traditional methods - Need for real-time onboard deployment

    Critic-free Deep Reinforcement Learning for maritime coverage path planning on irregular hexagonal grids · 2026 · DOI

  • Testing the method in real-world environments. Applying the approach to more complex or dynamic environments.

    Search-based hierarchical whole-body motion planning and control for quadrotors · 2026 · DOI

  • Integration of diffusion-based warm-starting with large vision-language action models (e.g., π0.5) is mentioned as future work but lacks specification of how safety constraints from MPC would be enforced on learned policies and how object-centric representations could be compatible with language-conditioned action generation.

    Warm-Starting Collision-Free Model Predictive Control With Object-Centric Diffusion · 2026 · DOI

  • The comparison of conditioning strategies (occupancy grids, image-based ResNet-18, configuration-space, Slot Attention) is performed only in simulation with controlled viewpoints and synthetic scenes. Robustness of each representation to real sensor noise, partial observability, and occlusions in dynamic scenes requires evaluation.

    Warm-Starting Collision-Free Model Predictive Control With Object-Centric Diffusion · 2026 · DOI

  • Future research will focus on POMDP-based probabilistic state representation methods, as well as on more accurate simulation of physical environments, to enhance the applicability of this approach in diverse practi- cal scenarios such as environmental monitoring and precision agriculture.

    CLMPO-EC: A Lightweight Multi-UAV Multiarea Coverage Path Planning Method Using Deep Reinforcement Learning · 2026 · DOI

  • Insufficient population diversity and reduced exploration ability in existing metaheuristic algorithms. High dimensionality of the optimization problem. Need for smooth and continuous joint angle, velocity, and acceleration trajectories.

    Time-optimal trajectory planning for robotic manipulators based on an improved pufferfish optimization algorithm · 2026 · DOI

  • Further evaluation of the proposed algorithm using different benchmark functions and robotic platforms. Investigation of the application of the algorithm to other optimization problems. Development of more advanced initialization strategies and adaptive parameter mechanisms.

    Time-optimal trajectory planning for robotic manipulators based on an improved pufferfish optimization algorithm · 2026 · DOI

  • Dynamic obstacle interaction scenarios. Sensor noise and interference from dynamic environmental changes. Limited computational resources and sensor data.

    Adaptive Navigation for Robots Based on Object Relationship Reasoning and Reinforcement Learning · 2026 · DOI

  • Traditional navigation methods lack robustness and semantic rationality in dynamic environments. Reinforcement learning algorithms lack modeling of environmental semantic information.

    Adaptive Navigation for Robots Based on Object Relationship Reasoning and Reinforcement Learning · 2026 · DOI

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98 open questions have been extracted from the limitations and future-work passages of 475 Robotic Path Planning Algorithms papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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