implement dynamic obstacle avoidance

To address the lack of systematic benchmarks for evaluating the safety of obstacle-avoidance controllers in dynamic environments, this paper proposes the first three-dimensional evaluation framework tailored to object-perception-based controllers. The framework systematically assesses three core dimensions: kinematic completeness, control-point continuity, and trajectory stability. Grounded in representative robot–obstacle interaction scenarios, it introduces an experiment-driven, quantitative evaluation methodology to comparatively analyze three mainstream controller classes. Results expose common deficiencies across controllers—particularly in motion smoothness and trajectory stability. Crucially, this work establishes the first reproducible, extensible, and standardized assessment infrastructure for obstacle avoidance. By unifying evaluation criteria and metrics, it provides both theoretical foundations and practical tools for performance benchmarking, defect diagnosis, and safety-oriented optimization of navigation algorithms.

Functional safety verification of autonomous driving motion planners faces challenges posed by complex and learning-based planners. This paper proposes a real-time runtime protection framework for trajectory safety validation, introducing— for the first time—a temporal protection module that jointly enforces geometric feasibility, dynamic feasibility, and cost rationality checks. The framework adopts a modular architecture and implements online validation of trajectory candidates on a real-time operating system, with successful deployment on embedded hardware. Experiments demonstrate that the system reliably detects unsafe trajectories under millisecond-level latency constraints. The source code is publicly available, and comprehensive fallback strategies are under integration. This work significantly enhances runtime safety assurance for black-box or learning-based planners, bridging a critical gap between planning flexibility and functional safety compliance.

To address the challenge of proactive obstacle avoidance for multi-velocity moving targets in dynamic human-robot coexistence environments, this paper proposes DTAA—a novel end-to-end autonomous navigation framework. Methodologically, DTAA integrates YOLOv8-based detection, Ultralytics-enabled embedded tracking, and Kalman filtering for robust state estimation; introduces a heuristic clustering algorithm to construct a dynamic “dangerous space set”; and synergistically couples nonlinear model predictive control (NMPC) with D*+ path planning to enable spatiotemporal joint prediction and priority-aware collision avoidance. Evaluated on Boston Dynamics Spot robots across underground, indoor, and outdoor real-world scenarios, DTAA consistently maintains safe distances from moving agents, demonstrating high real-time performance and strong robustness under complex dynamics. Key contributions include the DTAA architectural design, the dangerous space set generation mechanism, and a novel co-optimization paradigm integrating NMPC with heuristic clustering.

This work addresses the challenges of real-time obstacle avoidance, high control latency, and the trade-off between safety and agility for autonomous micro aerial vehicles operating in unknown, dynamic environments. We propose a map-free, parameter-free, end-to-end closed-loop navigation framework. Methodologically, it introduces the first deep integration of nonlinear model predictive control (NMPC) with adaptive control barrier functions (CBFs), augmented by a lightweight RGB-D temporal neural network for depth estimation and a minimum-time-to-collision–driven threat-prioritization mechanism; dynamic heuristic optimization further enables online balancing of safety and agility. Experiments across diverse indoor and outdoor dynamic scenarios demonstrate zero mapping overhead and zero manual tuning, achieving high-speed flight with zero collisions, significantly reduced response latency, and minimized over-conservative constraints—establishing a new paradigm for real-time reactive navigation in complex dynamic environments.

To address safety and adaptability challenges in autonomous mobile robot navigation within unknown dynamic environments, this paper proposes a planning–control co-design framework. Methodologically, it integrates directional distance metrics with conical motion prediction to construct a risk assessment model, designs a customized cost map, and combines a reference corrector with control barrier functions (CBFs) to enable adaptive velocity modulation and trajectory tracking under safety-boundary constraints. Key contributions include: (i) the first coupling of directional distance and conical prediction for quantitative dynamic risk assessment; and (ii) a novel joint regulation mechanism integrating safety boundaries and reference correction. Extensive evaluations in simulation and complex real-world scenarios demonstrate that the proposed method significantly improves narrow-passage traversal success rates and reactive obstacle avoidance capability, achieving a favorable balance between high safety assurance and navigation efficiency.

This work addresses the conservatism of conventional motion planning approaches that model robots as points or circles, which often hinder efficient navigation in narrow environments. To overcome this limitation, the authors propose a safe local motion planning method based on discrete-time control barrier functions, unifying the representation of polyhedral robots and dynamically updated convex free space as polyhedra. This formulation ensures that the number of safety constraints scales only with local geometric complexity, substantially improving scalability. Notably, the approach eliminates the need for explicit obstacle detection by directly integrating occupancy grids and LiDAR measurements into a model predictive controller for real-time collision avoidance. Extensive simulations and hardware experiments demonstrate up to a 91-fold reduction in computation time, enabling real-time 10 Hz control on embedded platforms.

This work addresses the challenge of real-time safe autonomous navigation in spatially constrained and dynamically changing environments by proposing a real-time control architecture integrated with 3D LiDAR perception. The approach introduces an ellipsoidal safety region aligned with the robot’s body geometry, which rotates with the robot’s pose in the world frame to generate time-varying obstacle avoidance constraints. A dedicated time-varying Control Barrier Function (CBF) is designed for each LiDAR point, enabling efficient handling of numerous constraints at control frequency while minimally interfering with the primary navigation task. Extensive field experiments on a quadrupedal robot demonstrate robust performance in complex scenarios such as narrow underground corridors, where the system reliably copes with dynamic obstacles, unreliable high-level commands, and abrupt localization shifts, thereby validating its high reliability and practicality.

This work addresses the challenge of safe control for mobile robots in dynamic environments, where precise trajectory tracking and collision avoidance must be simultaneously guaranteed. A unified robust safety control framework is proposed that leverages a generalized kinematic transformation to cast the dynamics of heterogeneous platforms—including Ackermann-steered ground vehicles, differential-drive robots, and quadrotors—into a strict-feedback form. A sliding-mode controller is designed to achieve high-precision trajectory tracking, while a Collision Cone Control Barrier Function (C3BF)-based safety filter rigorously enforces obstacle avoidance constraints. Notably, this study presents the first application of sliding-mode control to Ackermann-steered ground robots. The approach’s generality, robustness, and safety are validated through both simulations and physical experiments across all three robotic platforms.

This work proposes a map-free, four-dimensional spatiotemporal trajectory planning framework to address the challenge of real-time obstacle avoidance and motion planning for quadrotor UAVs in unknown dynamic environments. Departing from conventional mapping-based approaches, the method directly leverages visual perception to construct safe flight corridors and integrates object segmentation, tracking, and trajectory optimization to enable dynamic collision avoidance. A reactive backup planning module is further introduced to resolve deadlock scenarios, substantially enhancing system robustness. Experimental results demonstrate that the proposed approach outperforms existing methods in both simulation and real-world settings, achieving efficient and safe navigation in complex, dynamic, and previously unseen environments.