Task Reconfiguration and Congestion Self-Healing Decisions for Warehouse Robot Fleets under Communication-Failure Disturbances

LIU Huwei; ZHAO Junhui; LIANG Kaibo; YANG Jianglong; ZHOU Li

doi:10.11999/JEIT261058

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LIU Huwei, ZHAO Junhui, LIANG Kaibo, YANG Jianglong, ZHOU Li. Task Reconfiguration and Congestion Self-Healing Decisions for Warehouse Robot Fleets under Communication-Failure Disturbances[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261058

Citation:

LIU Huwei, ZHAO Junhui, LIANG Kaibo, YANG Jianglong, ZHOU Li. Task Reconfiguration and Congestion Self-Healing Decisions for Warehouse Robot Fleets under Communication-Failure Disturbances[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261058

Citation:

LIU Huwei, ZHAO Junhui, LIANG Kaibo, YANG Jianglong, ZHOU Li. Task Reconfiguration and Congestion Self-Healing Decisions for Warehouse Robot Fleets under Communication-Failure Disturbances[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261058

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Task Reconfiguration and Congestion Self-Healing Decisions for Warehouse Robot Fleets under Communication-Failure Disturbances

doi: 10.11999/JEIT261058 cstr: 32379.14.JEIT261058

1.
School of Airport Economy Management, Beijing Institute of Economics and Management, Beijing 100102, China
2.
School of Management, Beijing Institute of Technology, Beijing 100081, China
3.
School of Economics and Management, Tianjin Agricultural University, Tianjin, 300392, China
4.
School of Computer Science and Artificial Intelligence, Beijing Wuzi University, Beijing 101149, China

Funds: The National Social Science Fund of China (24FGLB047)

Received Date: 2026-07-30
Accepted Date: 2026-10-16
Rev Recd Date: 2026-10-16

Available Online: 2026-10-29

Abstract

Abstract

Objective Warehouse robot fleets allocate streaming pickup-and-delivery tasks in narrow aisles. Packet loss can hide a healthy robot, a physical failure can invalidate its assignment, and a blockage can propagate queues. Existing studies often separate communication, task allocation, and multi-agent path finding. Scheduling may therefore use simulator-side states that were never delivered, while evaluation may omit failed transmissions, retries, or relay hops. This inconsistent information boundary obscures whether recovery is deployable. This study develops a method whose reconfiguration feasible domain is constrained by delivered-packet evidence. Methods A communication-availability-constrained event-triggered reconfiguration method, termed CAR-ETR, is proposed. Its central contribution is a cross-layer hard gate rather than a new event trigger, Hungarian matcher, congestion weight, or conflict resolver. Delivered heartbeat, retransmission, or relay packets form the availability set $ {\mathcal{A}}_{t} $; this set determines the assignable-robot set $ \mathcal{R}_{t}^{a} $, which determines which variables $ {x}_{ik,t} $ may enter task reconfiguration. Undelivered or unprobed states cannot be restored from simulator truth. The mechanism therefore changes the feasible domain instead of only modifying a matching cost or update frequency. An occupancy-history congestion field and a fixed-point executor support traffic redirection and discrete safety without being claimed as independent innovations.The warehouse is a grid graph, and each task has a release time, pickup node, delivery node, and due time. Availability is updated during heartbeat rounds. Direct delivery permits one retry; after failure, the nearest reachable robot within radius $ {R}_{c} $ may relay the packet. Source-to-relay loss increases with normalized Manhattan distance, relay-to-gateway delivery is sampled independently, and each hop permits one retry. A source enters $ {\mathcal{A}}_{t} $ only after end-to-end delivery. Every attempted heartbeat, retry, relay hop, assignment command, acknowledgement, centralized-planning packet, and local-conflict packet is counted.Reconfiguration uses fresh snapshots and is triggered by fault changes, backlog, prolonged waiting, or a maximum silent interval. The matching cost combines cached distance, projected lateness, task age, and an exponentially decayed occupancy field. The window $ H=16 $ covers four heartbeat periods and two minimum reconfiguration intervals; $ \beta =0.88 $ gives a 5.42-step half-life and retains 87.1% of the infinite-kernel mass. A fixed-point resolver keeps one priority winner for duplicate targets and removes moves into failed or stationary robots and opposite-edge swaps. Runtime assertions check vertex, swap, and failed-node invariants after every step.Experiments use a $ 31\times 23 $ warehouse with 24 robots, eight stations, and 480 steps. Compound disturbances combine correlated packet loss, four temporary blockages, and robot failures. Baselines are Greedy-A*, periodic Hungarian assignment, selective-conflict prioritized replanning, RHCR-PP with space-time reservations, and RHCR-LNS2 with large-neighborhood conflict repair. All methods share task, disturbance, communication, and safety settings. The primary experiment uses 20 paired seeds, Student-$ t $-based 95% confidence intervals, paired Wilcoxon tests, and global Holm correction over 40 comparisons. Additional experiments vary peak loss, fleet size, component removal, 0-12 simultaneous failures, a $ 5\times 5 $ $ H $-$ \beta $ grid, event thresholds, communication radius, and heartbeat period. Results and Discussions CAR-ETR completes $ 128.75\pm 6.55 $ tasks, with a mean flow time of $ 62.99\pm 1.50 $ steps, a restricted recovery duration of $ 27.85\pm 15.65 $ steps, and a recovery rate of 0.95. Relative to selective-conflict prioritized replanning, completed tasks increase by 19.88%, mean flow time decreases by 24.61%, service loss decreases by 42.83%, and all packet attempts per task decrease by 37.54%; these differences remain significant after global Holm correction. The 55.65% lower mean recovery duration is not significant after correction and is not treated as a confirmed advantage.Relative to RHCR-LNS2, the completed-task difference is -0.23%, and completed tasks, flow time, service loss, and resilience do not differ significantly. CAR-ETR reduces gateway-related packet attempts by 48.85% and decision time by 74.75%, while increasing all packet attempts by 24.47%. Relative to RHCR-PP, completed tasks are 0.73% lower, gateway-related attempts are 48.74% lower, and decision time is 66.35% lower, while all packet attempts are 23.48% higher. CAR-ETR therefore exchanges robot-to-robot local messages for lower centralized coordination and online computation rather than universally dominating rolling-horizon planning.When peak packet loss rises from 10% to 70%, completed tasks decrease by 3.00%, while all packet attempts increase by 20.96% and retry-plus-relay attempts increase sevenfold. The operational fault-tolerance criterion requires a recovery rate of at least 0.80 and completed tasks of at least 90% of the no-failure mean. All tested levels through 12 simultaneous temporary failures satisfy it, establishing a verified lower bound of 12 of 24 robots for the tested 96-step duration. Service loss nevertheless rises from 35.00 to 62.51, so this is not a theoretical threshold and cannot be extrapolated to permanent failures. An overly long minimum reconfiguration interval degrades throughput and recovery; removing congestion feedback produces the largest ablation loss. Weak scaling remains effective through 36 robots, but saturation and local-message amplification appear at 48 robots. No vertex collision, opposite-edge swap, or active-to-failed-robot overlap occurs in 1476 runs. Conclusions CAR-ETR establishes a communication-evidence gate from delivered packets to task-reconfiguration feasibility, with congestion guidance and fixed-point conflict resolution supporting execution. It improves service over local periodic replanning and reduces gateway coordination and online computation relative to strong rolling-horizon baselines, at the cost of more total packet attempts. Evidence remains limited to discrete-event simulation; continuous dynamics, wireless contention, localization error, energy constraints, permanent failures, and component interactions require hardware-in-the-loop and physical-platform validation.
- Multi-robot systems,
- Networked unmanned systems,
- Task reconfiguration,
- Congestion self-healing,
- Event-triggered decision making

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References(23)

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