Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace discrete space-time A* planning and post-processing with SIPPwRT's direct continuous, kinematic path planning in TP | TP-SIPPwRT · Small warehouse, v_task=0.50–1.00 | service time: 1,026.23 -> 944.03; 675.65 -> 601.69; 505.81 -> 435.26 | Helped |
| Replace discrete space-time A* planning and post-processing with SIPPwRT's direct continuous, kinematic path planning in TP | TP-SIPPwRT · Small warehouse, v_task=0.50–1.00 | throughput: 0.373 -> 0.397; 0.508 -> 0.552; 0.609 -> 0.689 | Helped |
| Plan continuous movements directly instead of post-processing discrete paths | TP-SIPPwRT versus TP-A* and CENTRAL · Small warehouse, 30 agents and 1,000 tasks | planning and post-processing time: 0.90; 0.92; 0.83; 264.66; 254.36; 269.91 | Helped |
| Increase task velocity | TP-SIPPwRT · Small warehouse, 30 agents | service time: 944.03 -> 601.69 -> 435.26 | Helped |
| Increase number of agents | TP-SIPPwRT · Small warehouse, task frequencies 1–10 | throughput | Helped |
| Increase task frequency | TP-SIPPwRT · Small warehouse, 10–50 agents | service time | Hurt |
| Increase task velocity | TP-SIPPwRT · Large warehouse, 250 agents | service time: 267.07; 147.90; 132.45 | Mixed |
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