@Qace_Dynamics

QACE Dynamics | Deterministic Task Replay We are adding a new execution layer that allows robotic tasks to be replayed and inspected step by step. Each run records planner decisions, map state, sensor input, and control output. This makes it possible to review robot behavior, compare simulation with hardware, and validate task logic without rerunning the robot. The replay system supports pausing, rewinding, and branching from any step, helping developers debug and test autonomy more efficiently. More updates coming soon.

QACE Situation and Team Update We want to address what happened recently and clear up any confusion: After the recent market dump, many KOLs began selling their positions. This created a panic sell cascade, which amplified the price drop. The team is still fully present and actively working to stabilize the situation in the best way we can. We appreciate the community’s patience. We will keep working as usual.

QACE Dynamics | Weekly Progress Update This week marked the transition of navigation from internal testing into hands on beta usage, with a focus on reliability, reuse, and real world behaviour. Autonomous navigation is now available inside the beta dApp. Robots can move using prebuilt maps and onboard sensors, with support for defined locations such as task points or return positions. Once a destination is set, the system handles planning and execution without manual intervention. Navigation now operates through a layered planning approach. A global planner determines the overall route using the map, while a local planner continuously adapts movement based on live lidar and camera input. This ensures safe progress even when unexpected obstacles appear. Navigation targets are now reusable across workflows. Named locations and zones can be referenced by higher level logic, allowing tasks to be built around spatial intent rather than raw coordinates. The same navigation block runs unchanged in simulation and on physical robots, making it possible to validate behaviour in Gazebo and deploy directly to hardware using the same setup. More blocks and workflows coming next.

QACE Dynamics | Navigation and Mapping Enhancement Navigation in QACE has been extended beyond simple goal based movement. Robots now operate with a layered planning pipeline that separates long range route planning from short range obstacle handling. A global planner computes the full path using the map, while a local planner continuously adjusts motion using live lidar and camera data. This allows the robot to keep progressing toward the goal even in dynamic environments. Navigation goals are now treated as first class objects. Named locations, task points, and zones on the map can be reused across different workflows, allowing higher level tasks to reference space directly instead of coordinates. We also unified navigation state across simulation and hardware. The same navigation module runs unchanged in Gazebo and on physical robots, using identical map data, planners, and constraints. This makes testing in simulation directly transferable to real deployments. These changes make navigation more reliable under real world conditions and prepare the stack for multi step tasks that combine movement, perception, and future manipulation blocks.

QACE Dynamics | Autonomous Navigation Live on Beta Autonomous navigation is now live inside QACE. Robots navigate using an existing map and onboard sensors, with support for pinned locations such as rooms, objects, or task points. A target can be selected, and the robot plans and follows the best path from its current position. If new obstacles appear, the robot adjusts locally and continues safely without breaking the task. The same navigation block works in simulation and on real robots. It can be downloaded as a ready to use module and plugged into ROS setups or Gazebo simulations, then deployed on physical hardware using the same workflow.