Replanning Trigger

Triggers are classified by their source relative to the agent.

• Exogenous Triggers originate from the external environment. Examples include:
  • A new obstacle appearing on a planned path.
  • A human operator issuing a high-priority task.
  • A zone becoming temporarily inaccessible.
• Endogenous Triggers originate from the agent's internal state. Examples include:
  • Low battery level falling below a threshold.
  • A component fault detected by self-diagnostics.
  • The agent falling behind its planned schedule. This distinction is fundamental for designing the system's sensor fusion and state estimation pipelines.

Triggers differ in their temporal nature, affecting the urgency and type of replanning required.

• Reactive Triggers respond to an event that has already occurred or a condition that is currently true (e.g., "collision imminent," "task failed now"). These demand immediate, often local, replanning to resolve a critical violation.
• Predictive Triggers are based on forecasts or models of future states (e.g., "estimated time of arrival will exceed deadline," "battery will be depleted before reaching the charger"). These enable proactive replanning, allowing for smoother, more optimal adjustments before a crisis occurs. Predictive triggers rely heavily on accurate fleet state estimation and simulation models.

Not all triggers warrant the same computational response. Systems implement priority tiers to manage resource allocation.

• Critical/Safety Triggers: Demand immediate, high-frequency replanning, often bypassing normal scheduling queues. Examples include imminent collision detection or a "STOP" command. Response is typically handled by low-level collision avoidance systems like DWA or ORCA.
• Operational Triggers: Require replanning to maintain efficiency and task progress. Examples include a new task assignment or a minor route blockage. These are processed by the core real-time replanning engine.
• Optimization Triggers: Initiate replanning to improve a non-critical metric, such as overall fleet energy use. These may be processed by background anytime algorithms when compute resources are available.

A replanning trigger is not an isolated event; it is the output of a continuous execution monitoring loop. This loop constantly compares the agent's predicted state (from its plan) with its perceived state (from sensors and fleet telemetry).

• The monitor uses feasibility checkers to validate if the current plan is still executable.
• Discrepancies between the expected and actual world state generate triggers.
• This creates a closed-loop system: Plan → Execute → Monitor → Trigger → Replan. The fidelity of the monitoring directly determines the system's responsiveness and resilience.

Sophisticated orchestration platforms manage trigger sensitivity to avoid replanning thrashing—excessive, computationally expensive replans for minor, transient changes.

• Hysteresis: Applying a threshold buffer (e.g., replan only if the new obstacle remains for >2 seconds).
• Trigger Coalescing: Bundling multiple closely-spaced triggers into a single replanning event.
• Cost-Benefit Gating: Estimating the computational cost of replanning versus the expected benefit; a trigger may be ignored if the cost outweighs a marginal plan improvement. This awareness is crucial for maintaining system stability and ensuring the replanning engine does not become a bottleneck.

The type of trigger directly informs the choice of replanning algorithm.

• A reactive, critical trigger (e.g., sudden obstacle) invokes a fast, local algorithm like Dynamic Window Approach (DWA) or triggers a warm-started local optimization.
• An operational trigger (e.g., a new task) may invoke a multi-agent path finding (MAPF) resolver or a spatial-temporal scheduling algorithm.
• A predictive, system-wide trigger (e.g., forecasted congestion) may initiate a full conflict-based search (CBS) or a model predictive control (MPC) optimization over a longer replanning horizon. The trigger provides the initial constraints and objectives for the solver.

These are external, unpredictable changes to the physical workspace that invalidate the assumed world model. The planning engine must react to new spatial constraints.

Key triggers include:

• Dynamic Obstacle Insertion: A human worker, forklift, or fallen pallet enters a planned path.
• Static Map Alteration: A gate is closed, a shelf is relocated, or a new work zone is defined.
• Congestion Detection: Sensor data indicates a traffic jam forming at a key intersection or charging station.

Technical Response: The system typically updates its costmap or occupancy grid and invokes an incremental planner like D Lite* or Lifelong Planning A (LPA)** to repair the path.

Triggers originating from the internal state of a fleet member, indicating it can no longer execute its plan as intended.

Common examples:

• Mechanical Failure: A drive wheel fault or sensor malfunction reported by the agent's health monitoring system.
• Energy Depletion: Battery level falls below a threshold, necessitating an unscheduled detour to a charging dock.
• Localization Drift: The agent's estimated pose (from SLAM or other sensors) deviates significantly from its planned trajectory, risking collision or task failure.
• Payload Issues: A dropped item or an incorrectly secured load detected by onboard sensors.

Technical Response: The orchestration middleware must reassign the agent's current task (Dynamic Task Allocation) and recalculate a safe path to a recovery state, such as a maintenance bay.

Changes to high-level mission commands or business logic that supersede the current activity schedule.

Primary triggers:

• New High-Priority Order: A rush order is received, requiring immediate picking and routing, preempting existing agent assignments.
• Task Cancellation or Modification: An item is out of stock, or a delivery address is changed.
• Global Optimization Event: A load balancing algorithm determines a more efficient distribution of work and initiates a fleet-wide reschedule.
• Human Operator Directive: A supervisor manually intervenes via a Human-in-the-Loop Interface to redirect an agent.

Technical Response: This often requires a full or partial replan at the Spatial-Temporal Scheduling layer, potentially using Conflict-Based Search (CBS) for multi-agent path coordination.

The agent is falling behind or will miss a hard deadline, making the current time-annotated plan infeasible.

Detection mechanisms:

• Execution Monitoring systems compare actual progress against the planned timeline.
• Predictive Analysis forecasts a delay based on current speed, congestion ahead, or an upcoming interaction.

Specific triggers:

• Schedule Slippage: An agent is delayed at a workstation, causing a cascade of missed time windows for subsequent tasks.
• Synchronization Failure: Two agents required to meet for a handoff will not arrive concurrently.
• Deadline Imminent: A promised delivery or process completion time is at risk.

Technical Response: The planner may switch to a more aggressive (potentially less energy-efficient) motion profile, request priority right-of-way via Zone Management Protocols, or trigger a task reassignment.

A predicted or imminent violation of safe operational separation between two or more agents, requiring deconfliction.

This is a core trigger for reactive safety systems:

• Collision Prediction: A Velocity Obstacles (VO) or Optimal Reciprocal Collision Avoidance (ORCA) algorithm calculates that current velocities will lead to a conflict within the next few seconds.
• Deadlock Detection: Algorithms identify a gridlock scenario where multiple agents are mutually blocked with no forward progress (e.g., a circular wait at a four-way intersection).
• Resource Contention: Two agents are assigned to the same pickup station, rack, or narrow corridor simultaneously.

Technical Response: Low-level Collision Avoidance Systems react with local velocity adjustments. Higher-level orchestration may resolve deadlocks by imposing an order or replanning paths for the involved agents.

Triggers related to the health of the software and communication infrastructure that underpins fleet coordination.

Critical system-level triggers:

• Network Partition/Latency Spike: An agent loses reliable connection to the central Orchestration Middleware, forcing it into a degraded, safety-focused autonomous mode until connectivity is restored.
• Planner Service Failure: A core planning microservice crashes, requiring failover to a backup instance and potential replan of all in-flight tasks.
• Map or Configuration Update: A new facility map or set of speed limits is deployed, requiring all agents to replan against the new world model.
• Fleet Scaling Event: New agents are dynamically added to or removed from the operational fleet, changing the available resource pool.

Technical Response: Handled by the Exception Handling Framework, which may involve agent safe-stops, plan invalidation, and system-wide state reconciliation.

The continuous process of comparing an agent's actual execution state and sensor readings against its expected plan to detect discrepancies, failures, or the need for replanning. This is the primary upstream system that generates replanning triggers by identifying deviations such as an agent falling behind schedule, encountering an unmapped obstacle, or receiving a sensor fault. It acts as the watchdog for plan integrity.

A software component that validates whether a candidate plan or trajectory satisfies all system constraints before execution. When a new plan is generated in response to a replanning trigger, the feasibility checker verifies it against:

• Kinodynamic constraints (acceleration, torque limits)
• Collision avoidance with static and dynamic obstacles
• Operational limits (battery, payload) This ensures the new plan is executable and safe.

The process of modifying an existing plan to accommodate a failure, unexpected event, or new constraint, often by reusing parts of the original plan to minimize disruption. This is a common algorithmic response to a replanning trigger. Instead of full re-planning from scratch, plan repair techniques like constraint posting or partial order planning attempt to surgically fix the invalidated segment of the plan, which is computationally faster and results in more predictable agent behavior.

An algorithm designed to efficiently update its output when given a small change to its input, reusing previous computations rather than starting from scratch. Algorithms like D Lite* and Lifelong Planning A (LPA)** are foundational for handling replanning triggers in real-time. When a trigger indicates a small change in the environment (e.g., a new obstacle), these algorithms update the cost map and repair the path orders of magnitude faster than a complete re-plan, enabling sub-second reaction times.

An optimization technique where the solver is initialized with a solution from a previous, similar problem to significantly reduce the time required to find a new optimal solution. In response to a replanning trigger, the previous valid plan is used as the initial guess or 'seed' for the new optimization. This is critical for Model Predictive Control (MPC) and non-linear solvers, where convergence from a cold start is too slow for real-time operation (< 100ms cycles).

The implementation strategy of Model Predictive Control (MPC) where only the first control action from the optimized sequence is executed before the horizon is shifted forward and the optimization is repeated. This framework inherently generates periodic replanning triggers at each control step. External triggers (like a new obstacle) can also be injected into this cycle, causing the MPC to re-optimize the trajectory over the moving horizon with updated constraints.