The field of autonomous robotics is witnessing significant advancements in navigation and planning, with a focus on developing innovative methods to tackle complex and dynamic environments. Researchers are exploring new approaches to integrate path planning and control, enabling robots to efficiently navigate through cluttered spaces and adapt to changing situations. The introduction of acceleration obstacles and nonlinear acceleration obstacles has improved motion planning in dynamic environments, allowing for more precise and reactive navigation. Furthermore, experience-based refinement of task planning knowledge is enhancing the cognitive abilities of autonomous robots, enabling them to learn from their interactions with the environment and improve their planning success rates. Noteworthy papers include:

• The paper on Acceleration Obstacles, which demonstrates the effectiveness of analytically derived boundaries for safe avoidance maneuvers in challenging road traffic scenarios.
• The Phoenix framework, which leverages motion instruction to connect high-level semantic reflection with low-level robotic action correction, facilitating precise and fine-grained robotic action correction.
• The DYNUS uncertainty-aware trajectory planner, which achieves a 100% success rate and approximately 25% faster travel times than state-of-the-art methods in dynamic unknown environments.