Presear engineers autonomous systems — perception stacks, path planning algorithms, decision engines, and safety monitors — for vehicles, drones, and industrial autonomous agents.
Technical Depth
From raw sensor data to reliable real-world actions — here are the core technical capabilities powering our autonomous systems.
Combining LiDAR point clouds, RGB-D camera feeds, radar returns, and IMU data into a unified, consistent world model. We implement multi-modal fusion architectures that maintain accurate 3D object detection and semantic segmentation in real time, even when individual sensors degrade or fail.
Building consistent maps of unknown environments while tracking agent position within them — without GPS. We implement visual SLAM, LiDAR SLAM, and graph-based optimisation approaches with loop closure detection and global bundle adjustment for robust long-duration operation in complex scenes.
Computing collision-free, dynamically feasible paths through cluttered environments using sampling-based planners (RRT*, Hybrid A*), lattice planners, and trajectory optimisation. We balance smoothness, energy efficiency, and safety margins — adapting plans in real time as the environment changes.
Translating low-level perception into high-level decisions — lane changes, stop/go arbitration, obstacle avoidance maneuvers, and interaction with other agents. We combine rule-based finite state machines, hierarchical behaviour trees, and learned driving policies trained with imitation and reinforcement learning.
Building layered safety architectures that detect system faults, sensor failures, and unexpected environment states — triggering safe-stop, takeover, or degraded-mode operations. We implement runtime monitors, formal verification of safety properties, and redundant fallback control loops for mission-critical deployments.
Stress-testing autonomous systems across millions of synthetic scenarios — rare events, sensor noise, adversarial actors, and edge cases that cannot be collected in the real world. We build parameterised scenario generators in CARLA, Isaac Sim, and custom environments to achieve coverage-driven validation before hardware trials.
Our Process
A rigorous five-stage process. Click any step to explore what happens — and why it matters.
We select and configure the optimal sensor payload for the operational domain — balancing perception range, resolution, redundancy, cost, and SWaP (size, weight, and power) constraints. Every hardware decision is driven by the worst-case operational scenarios the system must handle safely.
Building the full perception pipeline from raw sensor data to a rich semantic world model — including object detection, tracking, segmentation, lane marking, and free-space estimation. We optimise inference latency to meet real-time requirements on target embedded hardware.
Integrating global path planners, local trajectory optimisers, and behaviour decision modules into a coherent stack that runs reliably under real-time constraints. We tune planning parameters against the specific kinematic limits and use-case requirements of the target platform.
Running millions of scenario variations in high-fidelity simulators — covering sensor noise, adversarial agents, weather conditions, and rare failure modes impossible to collect in the field. Safety properties are formally verified and test coverage metrics are tracked against certification requirements.
Structured field trials in progressively complex operational environments — transitioning from controlled test tracks to operational domains. We produce evidence packages for regulatory certification, document edge-case handling, and establish OTA update pipelines for continuous improvement post-deployment.
Real-World Impact
Production autonomous deployments across industries — each delivering measurable operational gains from day one.
Core Challenge
Last-mile delivery is the most expensive segment of the logistics chain. Human couriers cannot scale to meet e-commerce demand peaks, while traditional automation cannot handle the unstructured environments of pavements, building lobbies, and mixed pedestrian-vehicle zones.
Who Benefits
E-commerce fulfillment operators, hyperlocal delivery companies, and campus logistics managers seeking autonomous ground vehicles that navigate mixed environments, handle handoffs, and operate extended hours without human intervention.
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Manual material transport inside factories is a bottleneck for lean manufacturing and Industry 4.0 automation. Fixed-track AGVs lack the flexibility to adapt to production layout changes, while human forklift operators introduce variability, safety incidents, and high operational cost.
Who Benefits
Automotive plants, electronics manufacturers, and warehouse operators that need flexible, reconfigurable autonomous fleets capable of navigating dynamic factory floors alongside human workers — with full WMS integration.
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Manual inspection of power lines, wind turbines, oil & gas pipelines, and tall structures is expensive, slow, and exposes workers to significant safety risks. Routine inspections are often deferred, creating asset degradation and regulatory compliance gaps.
Who Benefits
Energy utilities, oil & gas operators, and infrastructure asset managers that need frequent, consistent, low-cost inspection data across large geographies — with AI-assisted anomaly detection integrated into asset management workflows.
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Agricultural labour shortages and rising input costs are compressing farm margins. Tractors and harvesters require skilled operators for extended hours in harsh environments — operations that are prime candidates for automation, but demand high reliability in unstructured outdoor settings.
Who Benefits
Large-scale farms, agri-tech companies, and precision agriculture solution providers seeking autonomous navigation, variable-rate application, and real-time crop monitoring that can operate from pre-dawn to sunset without operator fatigue.
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Industry-standard frameworks, simulators, and hardware SDKs — chosen for performance, safety, and long-term maintainability.
Frequently Asked
Answers to the questions engineering leaders, product teams, and safety officers ask before starting an autonomous systems engagement with Presear Softwares.
Ask Our Autonomy TeamPartner with Presear Softwares to build autonomous systems that go beyond proof-of-concept — rigorously validated, safety-monitored, and designed to deliver operational value from day one.