This PhD thesis advances robotic autonomy by developing novel path-planning and collision-avoidance solutions that enable resilient missions in complex, unstructured real-world environments. The primary contribution is D*+, a risk-aware path planner extending the D*-lite framework for ground and aerial robots. D*+ introduces a risk layer around occupied and unknown spaces, ensuring traversable paths with safety margins while operating on imperfect maps from real data. Its dynamic mapping supports adaptive replanning, enabling exploration missions in unknown environments, and is excellent for waypoint navigation. Real-world trials with a UAV and quadrupedal robot confirm its versatility across diverse scenarios.

The second contribution is the Detect Track and Avoid Architecture (DTAA), which tackles dynamic obstacles using YOLO-based detection, Kalman filter state estimation, and a nonlinear model predictive controller (NMPC) for anticipatory avoidance maneuvers. DTAA effectively handles fast-moving objects while following D*+ paths; however, it is limited by a short predictive horizon and susceptible to local minima. To overcome these weaknesses, this thesis introduces A*+T, a distributed, time-dependent multi-agent path planner. Built on an A*framework, A*+T integrates D*+ 's risk layers and DTAA's dynamic obstacle handling, adding a temporal dimension to the planning process, enabling collision checks in time and space. The temporal dimension enables distributed autonomous robots to plan collision-free paths in shared spaces based on other robots' planned paths.

Leveraging shared paths and predicted paths from DTAA, A*+T plans collision-free paths around the dynamic obstacles. Validated through simulations and real-world experiments, A*+T enhances mission readiness for multi-agent scenarios.

Beyond these, the thesis integrates these modules into complete robotic systems, enhancing mission control for large-scale applications. Demonstrations include mining inspections (visual and gas detection) and search-and-rescue missions (locating humans/objects). These original advancements offer robust, practical solutions for robotic navigation, validated through extensive real-world testing, and contribute significantly to autonomous systems in high-stakes environments.