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Unlike the conventional static methods, the proposed method achieves the goal of balancing conflict objectives of task completion time and energy consumption with collision-free operation. A major contribution is the adoption of Proximal Policy Optimization (PPO) for trajectory planning, whereby robots are driven autonomously to navigate through unknown dynamic environments that may contain unobserved obstacles. The findings show a very high improvement in the efficiency of task execution and result in 0.028% FNR and 0.024% FPR, respectively. This shows the capability of the system to be adaptive and robust under real-world scenarios for robotic applications. These results highlight the efficiency and robustness of our method for real-world applications. <\/jats:p>","DOI":"10.1142\/s0218126625503189","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:05:13Z","timestamp":1743134713000},"source":"Crossref","is-referenced-by-count":1,"title":["Optimizing Robotic Task Sequencing and Trajectory Planning (TSTP): A Multi-Objective Hybrid Optimization Approach Coupled with Reinforcement Learning (DRL)"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8447-8822","authenticated-orcid":false,"given":"Aniruddha","family":"Joshi","sequence":"first","affiliation":[{"name":"Department of Electronics and Instrumentation Engineering, SRM Institute of Science and Technology, Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G. 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